changeset 0:cf69ad260611 draft default tip

planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
author ecology
date Mon, 09 Jan 2023 13:36:02 +0000
parents
children
files Lib_preprocess_S2.r alpha_beta.r biodiv_indices_global.r comparison_div.r comparison_div.xml functions.r indices_spectral.r macro.xml pca_raster.r preprocess_S2.r prosail-master/R/Lib_PROSAIL.R prosail-master/R/Lib_PROSAIL_HybridInversion.R prosail-master/R/Lib_SpectralIndices.R test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml test-data/Metadata_validation.txt test-data/Mission.csv test-data/NDVI.tabular test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip test-data/S2A_Subset test-data/S2A_Subset.hdr test-data/S2A_T33NUD_Plots.zip val_metadata.r
diffstat 22 files changed, 15610 insertions(+), 0 deletions(-) [+]
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Lib_preprocess_S2.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,1335 @@
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# preprocS2
+# Lib_preprocess_S2.R
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Copyright 2021/08 Jean-Baptiste FERET
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# This Library contains functions to preprocess Sentinel-2 images downloaded from
+# different data hubs, such as THEIA, PEPS or SCIHUB
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+
+#" This function adjusts information from ENVI header
+#"
+#" @param dsn character. path where to store the stack
+#" @param bands list. should include "bandname", and if possible "wavelength"
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom utils read.table
+#" @importFrom raster hdr raster
+#" @export
+adjust_envi_hdr <- function(dsn, bands, sensor = "Unknown", stretch = FALSE) {
+
+  # Edit hdr file to add metadata
+  hdr <- read_envi_header(get_hdr_name(dsn))
+  hdr$`band names` <- bands$bandname
+  if (length(bands$wavelength) == length(bands$bandname)) {
+    hdr$wavelength <- bands$wavelength
+  }else {
+    hdr$wavelength <- NULL
+  }
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`sensor type` <- sensor
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dsn))
+
+  # remove unnecessary files
+  file2remove <- paste(dsn, ".aux.xml", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  file2remove <- paste(dsn, ".prj", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  file2remove <- paste(dsn, ".stx", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  return(invisible())
+}
+
+#" This function saves reflectance files
+#"
+#" @param s2sat character. Sentinel-2 mission ("2A" or "2B")
+#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters)
+#" @param dateacq_s2 double. date of acquisition
+#"
+#" @return s2mission character. name of the S2 mission (2A or 2B)
+#" @importFrom sen2r safe_getMetadata check_scihub_connection s2_list
+#" @export
+check_s2mission <- function(s2sat, tile_s2, dateacq_s2) {
+
+  # is mission already defined by user?
+  if (!is.null(s2sat)) {
+    if (s2sat == "2A") {
+      s2mission <- "2A"
+    }else if (s2sat == "2B") {
+      s2mission <- "2B"
+    }else {
+      message("Could not identify if image from Sentinel-2A or -2B")
+      message("Defining central wavelength of spectral bands based on S2A")
+      s2mission <- "2A"
+    }
+  }else {
+    message("Could not identify if image from Sentinel-2A or -2B")
+    message("Defining central wavelength of spectral bands based on S2A")
+    s2mission <- "2A"
+  }
+  return(s2mission)
+}
+
+#" this function aims at computing directory size
+#" @param path character. path for directory
+#" @param recursive boolean . set T if recursive
+#"
+#" @return size_files numeric. size in bytes
+#" - image stack
+#" - path for individual band files corresponding to the stack
+#" - path for vector (reprojected if needed)
+#"
+#" @importFrom raster raster
+#" @importFrom tools file_path_sans_ext file_ext
+#" @export
+dir_size <- function(path, recursive = TRUE) {
+  stopifnot(is.character(path))
+  files <- list.files(path, full.names = TRUE, recursive = recursive)
+  vect_size <- sapply(files, function(x) file.size(x))
+  size_files <- sum(vect_size)
+  return(size_files)
+}
+
+#" This function reads S2 data from L2A directories downloaded from
+#" various data hubs including THEIA, PEPS & SCIHUB (SAFE format & LaSRC)
+#" @param path_dir_s2 character. path for S2 directory
+#" @param path_vector character. path for vector file
+#" @param s2source character. type of directory format (depends on atmospheric correction: SAFE produced from Sen2Cor)
+#" @param resolution numeric. buffer applied to vector file (in meters)
+#" @param interpolation character. method for resampling. default = "bilinear"
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listout list.
+#" - image stack
+#" - path for individual band files corresponding to the stack
+#" - path for vector (reprojected if needed)
+#"
+#" @importFrom raster raster
+#" @importFrom tools file_path_sans_ext file_ext
+#" @export
+extract_from_s2_l2a <- function(path_dir_s2, path_vector = NULL, s2source = "SAFE",
+                                resolution = 10, interpolation = "bilinear", fre_sre = "FRE") {
+  # Get list of paths corresponding to S2 bands and depending on S2 directory
+  s2_bands <- get_s2_bands(path_dir_s2 = path_dir_s2,
+                           s2source = s2source,
+                           resolution = resolution,
+                           fre_sre = fre_sre)
+
+  if (length(s2_bands$s2bands_10m) > 0) {
+    rastmp <- raster::raster(s2_bands$s2bands_10m[[1]])
+  } else if (length(s2_bands$s2bands_20m) > 0) {
+    rastmp <- raster::raster(s2_bands$s2bands_20m[[1]])
+  }
+  # check if vector and raster share the same projection. if not, re-project vector
+  if (!is.null(path_vector)) {
+    raster_proj <- raster::projection(rastmp)
+    path_vector_reproj <- paste(tools::file_path_sans_ext(path_vector), "_reprojected.shp", sep = "")
+    path_vector <- reproject_shp(path_vector_init = path_vector,
+                                 newprojection = raster_proj,
+                                 path_vector_reproj = path_vector_reproj)
+  }
+  # Extract data corresponding to the vector footprint (if provided) & resample data if needed
+  if (length(s2_bands$s2bands_10m) > 0) {
+    stack_10m <- read_s2bands(s2_bands = s2_bands$s2bands_10m, path_vector = path_vector,
+                              resampling = 1, interpolation = interpolation)
+  }
+  if (length(s2_bands$s2bands_20m) > 0) {
+    if (resolution == 10 && s2source != "LaSRC") {
+      resampling <- 2
+    }else {
+      resampling <- 1
+    }
+    stack_20m <- read_s2bands(s2_bands = s2_bands$s2bands_20m, path_vector = path_vector,
+                              resampling = resampling, interpolation = interpolation)
+  }
+  # get full stack including 10m and 20m spatial resolution
+  if (length(s2_bands$s2bands_10m) > 0 && length(s2_bands$s2bands_20m) > 0) {
+    diffxstart <- attributes(stack_10m)$dimensions[[1]]$from - attributes(stack_20m)$dimensions[[1]]$from
+    diffxstop <- attributes(stack_10m)$dimensions[[1]]$to - attributes(stack_20m)$dimensions[[1]]$to
+    diffystart <- attributes(stack_10m)$dimensions[[2]]$from - attributes(stack_20m)$dimensions[[2]]$from
+    diffystop <- attributes(stack_10m)$dimensions[[2]]$to - attributes(stack_20m)$dimensions[[2]]$to
+    if (!diffxstop == 0) {
+      # size of 20m > size of 10m --> reduce 20m
+      # size of 10m > size of 20m --> reduce 10m
+      if (diffxstop > 0) {
+        stack_10m <- stack_10m[, 1:(dim(stack_10m)[1] - diffxstop), , ]
+      }else if (diffxstop < 0) {
+        stack_20m <- stack_20m[, 1:(dim(stack_20m)[1] + diffxstop), , ]
+      }
+    }
+    if (!diffystop == 0) {
+      if (diffystop > 0) {
+        stack_10m <- stack_10m[, , 1:(dim(stack_10m)[2] - diffystop), ]
+      }else if (diffystop < 0) {
+        stack_20m <- stack_20m[, , 1:(dim(stack_20m)[2] + diffystop), ]
+      }
+    }
+    if (!diffxstart == 0) {
+      if (diffxstart > 0) {
+        stack_20m <- stack_20m[, (1 + diffxstart):dim(stack_20m)[1], , ]
+      }else if (diffxstart < 0) {
+        stack_10m <- stack_10m[, (1 - diffxstart):dim(stack_10m)[1], , ]
+      }
+    }
+    if (!diffystart == 0) {
+      if (diffystart > 0) {
+        stack_20m <- stack_20m[, , (1 + diffystart):dim(stack_20m)[2], ]
+      }else if (diffystart < 0) {
+        stack_10m <- stack_10m[, , (1 - diffystart):dim(stack_10m)[2], ]
+      }
+    }
+    # reorder bands with increasing wavelength
+    s2bands <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "Cloud")
+    namebands <- c(names(s2_bands$s2bands_10m), names(s2_bands$s2bands_20m))
+    reorder_bands <- match(s2bands, namebands)
+    namebands <- namebands[reorder_bands]
+    listfiles <- c(stack_10m$attr, stack_20m$attr)[reorder_bands]
+
+    # adjust size to initial vector footprint without buffer
+    # --> buffer is needed in order to ensure that extraction following
+    # footprint of vector matches for images of different spatial resolution
+    # get bounding box corresponding to footprint of image or image subset
+    bb_xycoords <- get_bb(path_raster = listfiles[1],
+                          path_vector = path_vector, buffer = 0)
+
+    # prepare reading data for extent defined by bounding box
+    nxoff <- bb_xycoords$UL$col
+    nyoff <- bb_xycoords$UL$row
+    nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+    nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+    nbufxsize <- nxsize
+    nbufysize <- nysize
+    s2_stack <- stars::read_stars(listfiles, along = "band",
+                                  RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                  nXSize = nxsize, nYSize = nysize,
+                                                  nBufXSize = nbufxsize, nBufYSize = nbufysize,
+                                                  resample = "nearest_neighbour"), proxy = TRUE)
+
+
+    names(s2_stack$attr) <- namebands
+  }else if (length(s2_bands$s2bands_10m) > 0) {
+    s2_stack <- stack_10m
+    namebands <- names(s2_bands$s2bands_10m)
+    names(s2_stack$attr) <- namebands
+  }else if (length(s2_bands$s2bands_20m) > 0) {
+    s2_stack <- stack_20m
+    namebands <- names(s2_bands$s2bands_20m)
+    names(s2_stack$attr) <- namebands
+  }
+
+  listout <- list("s2_stack" = s2_stack, "s2_bands" = s2_bands, "path_vector" = path_vector,
+                  "namebands" = namebands)
+  return(listout)
+}
+
+#" This function gets coordinates of a bounding box defined by a vector (optional) and a raster
+#"
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param buffer numeric. buffer applied to vector file (in meters)
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @export
+get_bb <- function(path_raster, path_vector = NULL, buffer = 0) {
+
+  if (!is.null(path_vector)) {
+    # get bounding box with a 50m buffer in order to allow for interpolation
+    bb_xycoords <- get_bb_from_vector(path_raster = path_raster,
+                                      path_vector = path_vector,
+                                      buffer = buffer)
+  }else if (is.null(path_vector)) {
+    bb_xycoords <- get_bb_from_fullimage(path_raster)
+  }
+  return(bb_xycoords)
+}
+
+#" This function gets extreme coordinates of a bounding box corresponding to a full image
+#"
+#" @param path_raster character. path for raster file
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @importFrom raster raster
+#" @export
+get_bb_from_fullimage <- function(path_raster) {
+  # get raster coordinates corresponding to Full image
+  rasterobj <- raster::raster(path_raster)
+  bb_xycoords <- list()
+  bb_xycoords[["UL"]] <- data.frame("row" = 1, "col" = 1)
+  bb_xycoords[["UR"]] <- data.frame("row" = 1, "col" = dim(rasterobj)[2])
+  bb_xycoords[["LL"]] <- data.frame("row" = dim(rasterobj)[1], "col" = 1)
+  bb_xycoords[["LR"]] <- data.frame("row" = dim(rasterobj)[1], "col" = dim(rasterobj)[2])
+  return(bb_xycoords)
+}
+
+#" This gets bounding box corresponding to a vector from a raster (UL, UR, LL, LR corners)
+#"
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param buffer numeric. buffer applied to vector file (in meters)
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @importFrom sf st_read st_bbox st_crop
+#" @importFrom rgeos gbuffer bbox2SP
+#" @importFrom sp SpatialPoints bbox
+#" @importFrom raster projection extract extent raster
+#" @importFrom methods as
+#" @export
+get_bb_from_vector <- function(path_raster, path_vector, buffer = 0) {
+
+  data_raster <- raster::raster(path_raster)
+  # extract BB coordinates from vector
+  bb_vector <- rgeos::gbuffer(spgeom = as(sf::st_read(dsn = path_vector, quiet = TRUE), "Spatial"),
+                              width = buffer, byid = TRUE)
+  # extract BB coordinates from raster
+  bb_raster <- rgeos::bbox2SP(bbox = bbox(data_raster))
+  # compute intersection
+  intersect <- rgeos::gIntersection(bb_vector, bb_raster)
+  bbext <- raster::extent(intersect)
+  xmin <- bbext[1]
+  xmax <- bbext[2]
+  ymin <- bbext[3]
+  ymax <- bbext[4]
+  # get coordinates of bounding box corresponding to vector
+  corners <- list()
+  corners[["UR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymax))
+  corners[["LR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymin))
+  corners[["UL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymax))
+  corners[["LL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymin))
+  raster::projection(corners[["UL"]]) <- raster::projection(corners[["UR"]]) <-
+    raster::projection(corners[["LL"]]) <- raster::projection(corners[["LR"]]) <-
+    raster::projection(sf::st_read(dsn = path_vector, quiet = TRUE))
+  # get coordinates for corners of bounding box
+  bb_xycoords <- list()
+  for (corner in names(corners)) {
+    ex_df <- as.data.frame(raster::extract(data_raster, corners[[corner]], cellnumbers = TRUE))
+    colrow <- ind2sub(data_raster, ex_df$cell)
+    bb_xycoords[[corner]] <- data.frame("row" = colrow$row, "col" = colrow$col)
+  }
+  return(bb_xycoords)
+}
+
+#" get hdr name from image file name, assuming it is BIL format
+#"
+#" @param impath path of the image
+#"
+#" @return corresponding hdr
+#" @import tools
+#" @export
+get_hdr_name <- function(impath) {
+  if (tools::file_ext(impath) == "") {
+    impathhdr <- paste(impath, ".hdr", sep = "")
+  }else if (tools::file_ext(impath) == "bil") {
+    impathhdr <- gsub(".bil", ".hdr", impath)
+  }else if (tools::file_ext(impath) == "zip") {
+    impathhdr <- gsub(".zip", ".hdr", impath)
+  }else {
+    impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "")
+  }
+
+  if (!file.exists(impathhdr)) {
+    message("WARNING : COULD NOT FIND hdr FILE")
+    print(impathhdr)
+    message("Process may stop")
+  }
+  return(impathhdr)
+}
+
+#" This function returns path for the spectral bands to be used
+#"
+#" @param path_dir_s2 character. Path for the directory containing S2 data. either L2A .SAFE S2 file or THEIA directory
+#" @param s2source character. defines if data comes from SciHub as SAFE directory, from THEIA or from LaSRC
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution
+#" @export
+get_s2_bands <- function(path_dir_s2, s2source = "SAFE", resolution = 10, fre_sre = "FRE") {
+
+  if (s2source == "SAFE" || s2source == "Sen2Cor") {
+    listbands <- get_s2_bands_from_sen2cor(path_dir_s2 = path_dir_s2, resolution = resolution)
+  }else if (s2source == "THEIA") {
+    listbands <- get_s2_bands_from_theia(path_dir_s2 = path_dir_s2, resolution = resolution,
+                                         fre_sre = fre_sre)
+  }else if (s2source == "LaSRC") {
+    listbands <- get_s2_bands_from_lasrc(path_dir_s2 = path_dir_s2, resolution = resolution)
+  }else {
+    message("The data source (Atmospheric correction) for Sentinel-2 image is unknown")
+    message("Please provide S2 images from one of the following data sources:")
+    message("- LaSRC (atmospheric correction: LaSRC)")
+    message("- THEIA (atmospheric correction: MAJA)")
+    message("- SAFE (atmospheric correction: Sen2Cor)")
+    s2bands_10m <- s2bands_20m <- granule <- mtdfile <- metadata_msi <- metadata_lasrc <- NULL
+    listbands <- list("s2bands_10m" = s2bands_10m, "s2bands_20m" = s2bands_20m, "GRANULE" = granule,
+                      "metadata" = mtdfile, "metadata_MSI" = metadata_msi,
+                      "metadata_lasrc" = metadata_lasrc)
+  }
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in SAFE / sen2Cor directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @export
+get_s2_bands_from_sen2cor <- function(path_dir_s2, resolution = 10) {
+  # build path for all bands
+  if (resolution == 10) {
+    b10m <- c("B02", "B03", "B04", "B08")
+    b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12")
+  }else {
+    b10m <- c()
+    b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  }
+  # get granule directory & path for corresponding metadata XML file
+  granule <- list.dirs(list.dirs(path_dir_s2, recursive = FALSE)[grep(pattern = "GRANULE",
+                                                                     x = list.dirs(path_dir_s2, recursive = FALSE))], recursive = FALSE)
+  mtdfile <- file.path(granule, "MTD_TL.xml")
+  if (file.exists(file.path(path_dir_s2, "MTD_MSIL2A.xml"))) {
+    mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL2A.xml")
+  } else {
+    mtd_msi_file <- NULL
+  }
+
+  # Define path for bands
+  s2bands_20m_dir <- file.path(granule, "IMG_DATA", "R20m")
+  s2bands_10m_dir <- file.path(granule, "IMG_DATA", "R10m")
+  s2bands_10m <- s2bands_20m <- list()
+  for (band in b20m) {
+    s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list.files(s2bands_20m_dir, pattern = band))
+  }
+  for (band in b10m) {
+    s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list.files(s2bands_10m_dir, pattern = band))
+  }
+  # get cloud mask
+  cloud <- "MSK_CLDPRB_20m"
+  cloud_20m_dir <- file.path(granule, "QI_DATA")
+  s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud))
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "GRANULE" = granule,
+                    "metadata" = mtdfile,
+                    "metadata_MSI" = mtd_msi_file,
+                    "metadata_lasrc" = NULL)
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in LaSRC directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @importFrom stringr str_subset
+#" @export
+get_s2_bands_from_lasrc <- function(path_dir_s2, resolution = 10) {
+
+  # get granule directory & path for corresponding metadata XML file
+  granule <- path_dir_s2
+  mtdfile <- file.path(granule, "MTD_TL.xml")
+  if (file.exists(file.path(path_dir_s2, "MTD_MSIL1C.xml"))) {
+    mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL1C.xml")
+  } else {
+    mtd_msi_file <- NULL
+  }
+
+  # build path for all bands
+  b10m <- c("band2", "band3", "band4", "band5", "band6", "band7", "band8", "band8a", "band11", "band12")
+  b10m_standard <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  # Define path for bands
+  s2bands_10m <- s2bands_20m <- list()
+  for (i in 1:seq_along(b10m)) {
+    s2bands_10m[[b10m_standard[i]]] <- file.path(path_dir_s2,
+                                                 list.files(path_dir_s2,
+                                                            pattern = paste(b10m[i], ".tif", sep = "")))
+  }
+
+  # get metadata file containing offset
+  mtd_lasrc <- str_subset(list.files(path_dir_s2, pattern = "S2"), ".xml$")
+  if (file.exists(file.path(path_dir_s2, mtd_lasrc))) {
+    metadata_lasrc <- file.path(path_dir_s2, mtd_lasrc)
+  } else {
+    metadata_lasrc <- NULL
+  }
+  # get cloud mask
+  cloud <- "CLM"
+  s2bands_10m[["Cloud"]] <- file.path(path_dir_s2, list.files(path_dir_s2, pattern = cloud))
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "GRANULE" = granule,
+                    "metadata" = mtdfile,
+                    "metadata_MSI" = mtd_msi_file,
+                    "metadata_lasrc" = metadata_lasrc)
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in THEIA directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @export
+get_s2_bands_from_theia <- function(path_dir_s2, resolution = 10, fre_sre = "FRE") {
+
+  # build path for all bands
+  if (resolution == 10) {
+    b10m <- c("B02", "B03", "B04", "B08")
+    b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12")
+  } else {
+    b10m <- c()
+    b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  }
+
+  # get path_tile_s2 directory & path for corresponding metadata XML file
+  path_tile_s2 <- list.dirs(path_dir_s2, recursive = FALSE)
+  files_tile_s2 <- list.files(path_tile_s2, recursive = FALSE)
+  mtdfile <- file.path(path_tile_s2, files_tile_s2[grep(pattern = "MTD_ALL.xml", x = files_tile_s2)])
+
+  # Define path for bands
+  s2bands_10m_dir <- s2bands_20m_dir <- path_tile_s2
+  s2bands_10m <- s2bands_20m <- list()
+  for (band in b20m) {
+    band_20m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE)
+    list_files_20m <- list.files(s2bands_20m_dir, pattern = band_20m_pattern)
+    s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list_files_20m)[grep(pattern = fre_sre,
+                                                                           x = file.path(s2bands_20m_dir, list_files_20m))]
+  }
+  for (band in b10m) {
+    band_10m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE)
+    list_files_10m <- list.files(s2bands_10m_dir, pattern = band_10m_pattern)
+    s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list_files_10m)[grep(pattern = fre_sre,
+                                                                           x = file.path(s2bands_10m_dir, list_files_10m))]
+  }
+
+  # get cloud mask 10m
+  cloud_10m <- "CLM_R1"
+  cloud_10m_dir <- file.path(path_tile_s2, "MASKS")
+  s2bands_10m[["Cloud"]] <- file.path(cloud_10m_dir, list.files(cloud_10m_dir, pattern = cloud_10m))
+
+  # get cloud mask 20m
+  cloud_20m <- "CLM_R2"
+  cloud_20m_dir <- file.path(path_tile_s2, "MASKS")
+  s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud_20m))
+
+  # return list bands
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "path_tile_s2" = path_tile_s2,
+                    "metadata" = mtdfile)
+  return(listbands)
+}
+
+#" This function check S2 data level:
+#" - L2A: already atmospherically corrected
+#" - L1C: requires atmospheric corrections with sen2cor
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return s2level character. S2 level: L1C or L2A
+#" @export
+get_s2_level <- function(prodname) {
+  prodname <- basename(prodname)
+  if (length(grep(pattern = "L1C_", x = prodname)) == 1) {
+    s2level <- "L1C"
+  } else if (length(grep(pattern = "L2A_", x = prodname)) == 1) {
+    s2level <- "L2A"
+  }
+  return(s2level)
+}
+
+#" This function gets tile from S2 image
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return tilename character
+#" @importFrom tools file_path_sans_ext
+#" @export
+get_tile <- function(prodname) {
+  prodname <- basename(prodname)
+  tilename <- tools::file_path_sans_ext(gsub("_.*", "", gsub(".*_T", "", prodname)))
+  return(tilename)
+}
+
+#" This function gets acquisition date from S2 image
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return dateacq character
+#" @export
+get_date <- function(prodname) {
+  prodname <- basename(prodname)
+  dateacq <- as.Date(gsub("T.*", "", gsub(".*_20", "20", prodname)), format = "%Y%m%d")
+  return(dateacq)
+}
+
+#" download S2 L1C data from Copernicus hub or Google cloud
+#"
+#" @param list_safe safe object. produced with sen2r::s2_list
+#" @param l1c_path character. path for storage of L1C image
+#" @param path_vector path for a vector file
+#" @param time_interval dates. time interval for S2 query
+#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and
+#" @param forcegoogle boolean. set to TRUE if only google requested
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return prodname character. S2 Product name
+#" @importFrom sen2r safe_is_online s2_list s2_download s2_order check_gcloud
+#" @export
+get_s2_l1c_image <- function(list_safe, l1c_path, path_vector, time_interval,
+                             googlecloud = FALSE, forcegoogle = FALSE) {
+  # Check if available from Copernicus hub first
+  copernicus_avail <- sen2r::safe_is_online(list_safe)
+  # if available: download
+  prodname <- attr(list_safe, which = "name")
+  if (file.exists(file.path(l1c_path, prodname))) {
+    message("L1C file already downloaded")
+    message(file.path(l1c_path, prodname))
+  } else {
+    if (copernicus_avail == TRUE && forcegoogle == FALSE) {
+      sen2r::s2_download(list_safe, outdir = l1c_path)
+    } else if (copernicus_avail == FALSE || forcegoogle == TRUE) {
+      # if not available and googlecloud==TRUE
+      if (googlecloud == TRUE) {
+        # check if google cloud SDK available from this computer
+        ggc <- sen2r::check_gcloud()
+        if (ggc == TRUE) {
+          message("downloading from Google cloud")
+          list_safe_ggc <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = path_vector),
+                                          time_interval = time_interval,
+                                          server = "gcloud")
+          prodname <- attr(list_safe_ggc, which = "name")
+          if (file.exists(file.path(l1c_path, prodname))) {
+            message("L1C file already downloaded")
+            message(file.path(l1c_path, prodname))
+          } else {
+            sen2r::s2_download(list_safe_ggc, outdir = l1c_path)
+            # check if QI_DATA exists in DATASTRIP, and create it if not the case
+            datastrip_path <- file.path(l1c_path, prodname, "DATASTRIP")
+            dsdir <- list.dirs(datastrip_path, recursive = FALSE)
+            if (length(match(list.dirs(dsdir, recursive = FALSE, full.names = FALSE), "QI_DATA")) == 0) {
+              dir.create(file.path(dsdir, "QI_DATA"))
+            }
+          }
+        } else if (ggc == FALSE) {
+          message("googlecloud set to TRUE but missing")
+          message("Please install Google cloud SDK")
+          message("https://cloud.google.com/sdk/docs/install")
+          message("and/or set configuration of sen2r following instructions")
+          message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/")
+        }
+      }
+    }
+    if (copernicus_avail == FALSE && googlecloud == FALSE) {
+      message("S2 image in Long Term Archive (LTA)")
+      message("Ordering image from  LTA")
+      message("This may take 1 day, please run your script later")
+      orders2 <- sen2r::s2_order(list_safe)
+      message("An alternative is possible with Google cloud SDK")
+      message("https://cloud.google.com/sdk/docs/install")
+      message("and/or set configuration of sen2r following instructions")
+      message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/")
+    }
+  }
+  return(prodname)
+}
+
+#" download S2 L2A data from Copernicus hub or convert L1C to L2A
+#"
+#" @param l2a_path character. path for storage of L2A image
+#" @param spatial_extent path for a vector file
+#" @param dateacq character. date of acquisition
+#" @param deletel1c Boolean. set TRUE to delete L1C images
+#" @param Sen2Cor Boolean. set TRUE to automatically perform atmospheric corrections using sen2Cor
+#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return pathl2a character. Path for L2A image
+#" @importFrom sen2r s2_list s2_download
+#" @importFrom R.utils getAbsolutePath
+
+#" @export
+get_s2_l2a_image <- function(l2a_path, spatial_extent, dateacq,
+                             deletel1c = FALSE, sen2cor = TRUE,
+                             googlecloud = FALSE) {
+
+  # Needs to be updated: define path for L1c data
+  l1c_path <- l2a_path
+  # define time interval
+  time_interval <- as.Date(c(dateacq, dateacq))
+  # get list S2 products corresponding to study area and date of interest using sen2r package
+  if (googlecloud == TRUE) {
+    server <- c("scihub", "gcloud")
+  } else if (googlecloud == FALSE) {
+    server <- "scihub"
+  }
+  list_safe <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = spatial_extent),
+                              time_interval = time_interval,
+                              server = server, availability = "check")
+  # download products
+  sen2r::s2_download(list_safe, outdir = l2a_path)
+  # name all products
+  prodname <- attr(list_safe, which = "name")
+  prodfullpath <- file.path(l2a_path, prodname)
+  if (sen2cor == TRUE) {
+    for (imgname in prodname) {
+      s2level <- get_s2_level(imgname)
+      if (s2level == "L1C") {
+        datepattern <- gsub(pattern = "-", replacement = "", x = dateacq)
+        pathl2a <- s2_from_l1c_to_l2a(prodname = imgname, l1c_path = l2a_path, l2a_path = l2a_path,
+                                      datepattern = datepattern, tmp_path = NULL)
+        if (deletel1c == TRUE) {
+          unlink(x = R.utils::getAbsolutePath(file.path(l1c_path, prodname)),
+                 recursive = TRUE, force = TRUE)
+          # delete from full path and add atmospherically corrected
+          whichimg <- grep(x = prodfullpath, pattern = imgname)
+          dateacq <- get_date(imgname)
+          tilename <- get_tile(imgname)
+          pathl2a <- list.files(path = l2a_path, pattern = tilename, full.names = TRUE)
+          pathl2a <- pathl2a[grep(x = pathl2a, pattern = dateacq)]
+          pathl2a <- pathl2a[grep(x = basename(pathl2a), pattern = "L2A")]
+          prodfullpath[whichimg] <- pathl2a
+        }
+      }
+    }
+  }
+
+  return(prodfullpath)
+}
+
+#" convert image coordinates from index to X-Y
+#"
+#" @param Raster image raster object
+#" @param image_index coordinates corresponding to the raster
+ind2sub <- function(data_raster, image_index) {
+  c <- ((image_index - 1) %% data_raster@ncols) + 1
+  r <- floor((image_index - 1) / data_raster@ncols) + 1
+  my_list <- list("col" = c, "row" = r)
+  return(my_list)
+}
+
+#" mosaicing a set of rasters
+#"
+#" @param list_rasters character. list of paths corresponding to rasters to mosaic
+#" @param dst_mosaic character. path and name of mosaic produced
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom gdalUtils mosaic_rasters
+#" @importFrom raster hdr raster
+#" @export
+mosaic_rasters <- function(list_rasters, dst_mosaic, stretch = FALSE) {
+
+  # produce mosaic
+  gdalUtils::mosaic_rasters(gdalfile = list_rasters, dst_dataset = dst_mosaic,
+                            separate = FALSE, of = "Ehdr", verbose = TRUE)
+
+  # convert hdr to ENVI format
+  raster::hdr(raster(dst_mosaic), format = "ENVI")
+  # add info to hdr based on initial rasters
+  hdr_init <- read_envi_header(get_hdr_name(list_rasters[1]))
+  hdr <- read_envi_header(get_hdr_name(dst_mosaic))
+  hdr$`band names` <- hdr_init$`band names`
+  hdr$wavelength <- hdr_init$wavelength
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`sensor type` <- hdr_init$`sensor type`
+  hdr$`coordinate system string` <- read.table(paste(file_path_sans_ext(dst_mosaic), ".prj", sep = ""))
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dst_mosaic))
+  return(invisible())
+}
+
+#" Reads ENVI hdr file
+#"
+#" @param hdrpath Path of the hdr file
+#"
+#" @return list of the content of the hdr file
+#" @export
+read_envi_header <- function(hdrpath) {
+  if (!grepl(".hdr$", hdrpath)) {
+    stop("File extension should be .hdr")
+  }
+  hdr <- readLines(hdrpath)
+  ## check ENVI at beginning of file
+  if (!grepl("ENVI", hdr[1])) {
+    stop("Not an ENVI header (ENVI keyword missing)")
+  } else {
+    hdr <- hdr [-1]
+  }
+  ## remove curly braces and put multi-line key-value-pairs into one line
+  hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr)
+  l <- grep("\\{", hdr)
+  r <- grep("\\}", hdr)
+
+  if (length(l) != length(r)) {
+    stop("Error matching curly braces in header (differing numbers).")
+  }
+
+  if (any(r <= l)) {
+    stop("Mismatch of curly braces in header.")
+  }
+
+  hdr[l] <- sub("\\{", "", hdr[l])
+  hdr[r] <- sub("\\}", "", hdr[r])
+
+  for (i in rev(seq_along(l))) {
+    hdr <- c(
+      hdr [seq_len(l [i] - 1)],
+      paste(hdr [l [i]:r [i]], collapse = "\n"),
+      hdr [-seq_len(r [i])]
+    )
+  }
+
+  ## split key = value constructs into list with keys as names
+  hdr <- sapply(hdr, split_line, "=", USE.NAMES = FALSE)
+  names(hdr) <- tolower(names(hdr))
+
+  ## process numeric values
+  tmp <- names(hdr) %in% c(
+    "samples", "lines", "bands", "header offset", "data type",
+    "byte order", "default bands", "data ignore value",
+    "wavelength", "fwhm", "data gain values"
+  )
+  hdr [tmp] <- lapply(hdr [tmp], function(x) {
+    as.numeric(unlist(strsplit(x, ", ")))
+  })
+
+  return(hdr)
+}
+
+#" This function reads a list of files corresponding to S2 bands
+#" S2 bands are expected to have uniform spatial resolution and footprint
+#" @param s2_bands list. list of S2 bands obtained from get_s2_bands
+#" @param path_vector path for a vector file
+#" @param resampling numeric. resampling factor (default = 1, set to resampling = 2 to convert 20m into 10m resolution)
+#" @param interpolation character. method for resampling. default = "bilinear"
+#"
+#" @return stack_s2 list. contains stack of S2 bands
+#"
+#" @importFrom stars read_stars
+#" @importFrom sf st_bbox st_read st_crop
+#" @export
+
+read_s2bands <- function(s2_bands, path_vector = NULL,
+                         resampling = 1, interpolation = "bilinear") {
+  # get bounding box corresponding to footprint of image or image subset
+  bb_xycoords <- get_bb(path_raster = s2_bands[[1]],
+                        path_vector = path_vector, buffer = 50)
+
+  # prepare reading data for extent defined by bounding box
+  nxoff <- bb_xycoords$UL$col
+  nyoff <- bb_xycoords$UL$row
+  nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+  nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+  nbufxsize <- resampling * nxsize
+  nbufysize <- resampling * nysize
+  if (resampling == 1) {
+    interpolation <- "nearest_neighbour"
+  }
+  # write interpolated individual bands in temp directory
+  tmpdir <- tempdir()
+  tmpfile <- list()
+  for (band in names(s2_bands)) {
+    stack_s2_tmp <- stars::read_stars(s2_bands[[band]], along = "band",
+                                      RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                      nXSize = nxsize, nYSize = nysize,
+                                                      nBufXSize = nbufxsize, nBufYSize = nbufysize,
+                                                      resample = interpolation), proxy = FALSE)
+    if (!is.null(path_vector)) {
+      stack_s2_tmp <- sf::st_crop(x = stack_s2_tmp, y = st_bbox(st_read(dsn = path_vector, quiet = TRUE)))
+    }
+    tmpfile[[band]] <- file.path(tmpdir, tools::file_path_sans_ext(basename(s2_bands[[band]])))
+    if (band == "Cloud") {
+      stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]],
+                         driver =  "ENVI", type = "Byte", overwrite = TRUE)
+    } else {
+      stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]],
+                         driver =  "ENVI", type = "Int16", overwrite = TRUE)
+    }
+    gc()
+  }
+
+  stack_s2 <- stars::read_stars(tmpfile, along = "band", proxy = TRUE)
+  return(stack_s2)
+}
+
+#" This function reads a raster stack, and gets footprint as pixel coordinates or vector file as input
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param bbpix list. coordinates of pixels corresponding to a bounding box
+#"
+#" @return starsobj stars object corresponding to raster or raster subset
+#"
+#" @importFrom stars read_stars
+#" @importFrom sf st_bbox st_read st_crop
+#" @export
+read_raster <- function(path_raster, path_vector = NULL, bbpix = NULL) {
+  # get bounding box corresponding to footprint of image or image subset
+  if (is.null(bbpix)) {
+    bb_xycoords <- get_bb(path_raster = path_raster,
+                          path_vector = path_vector, buffer = 0)
+  } else {
+    bb_xycoords <- bbpix
+  }
+  # prepare reading data for extent defined by bounding box
+  nxoff <- bb_xycoords$UL$col
+  nyoff <- bb_xycoords$UL$row
+  nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+  nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+  nbufxsize <- nxsize
+  nbufysize <- nysize
+  starsobj <- stars::read_stars(path_raster, along = "band",
+                                RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                  nXSize = nxsize, nYSize = nysize,
+                                                  nBufXSize = nbufxsize, nBufYSize = nbufysize),
+                                proxy = FALSE)
+  return(starsobj)
+}
+
+#" This function reprojects a shapefile and saves reprojected shapefile
+#"
+#" @param path_vector_init character. path for a shapefile to be reprojected
+#" @param newprojection character. projection to be applied to path_vector_init
+#" @param path_vector_reproj character. path for the reprojected shapefile
+#"
+#" @return path_vector character. path of the shapefile
+#" - path_vector_init if the vector did not need reprojection
+#" - path_vector_reproj if the vector needed reprojection
+#"
+#" @importFrom rgdal readOGR writeOGR
+#" @importFrom sp spTransform
+#" @importFrom raster projection
+#" @export
+reproject_shp <- function(path_vector_init, newprojection, path_vector_reproj) {
+
+  dir_vector_init <- dirname(path_vector_init)
+  # shapefile extension
+  fileext <- file_ext(basename(path_vector_init))
+  if (fileext == "shp") {
+    name_vector_init <- file_path_sans_ext(basename(path_vector_init))
+    vector_init_ogr <- rgdal::readOGR(dir_vector_init, name_vector_init, verbose = FALSE)
+  } else if (fileext == "kml") {
+    vector_init_ogr <- rgdal::readOGR(path_vector_init, verbose = FALSE)
+  }
+  vector_init_proj <- raster::projection(vector_init_ogr)
+
+  if (!vector_init_proj == newprojection) {
+    dir_vector_reproj <- dirname(path_vector_reproj)
+    name_vector_reproj <- file_path_sans_ext(basename(path_vector_reproj))
+    vector_reproj <- sp::spTransform(vector_init_ogr, newprojection)
+    rgdal::writeOGR(obj = vector_reproj, dsn = dir_vector_reproj, layer = name_vector_reproj,
+                    driver = "ESRI Shapefile", overwrite_layer = TRUE)
+    path_vector <- path_vector_reproj
+  } else {
+    path_vector <- path_vector_init
+  }
+  return(path_vector)
+}
+
+
+#" perform atmospheric corrections to convert L1C to L2A data with Sen2cor
+#"
+#" @param prodname character. produced with sen2r::s2_list
+#" @param l1c_path character. path of directory where L1C image is stored
+#" @param l2a_path character. path of directory where L2A image is stored
+#" @param datepattern character. pattern corresponding to date of acquisition to identify L2A directory
+#" @param tmp_path character. path of temporary directory where L2A image is stored
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return pathl2a character. S2 Product name
+#" @importFrom sen2r safe_is_online s2_list s2_download s2_order
+#" @importFrom R.utils getAbsolutePath
+#"
+#" @export
+s2_from_l1c_to_l2a <- function(prodname, l1c_path, l2a_path, datepattern, tmp_path = NULL) {
+
+  # define path for tmp directory
+  if (is.null(tmp_path)) {
+    tmp_path <- tempdir(check = TRUE)
+  }
+  tmp_prodlist <- prodname
+  # perform Sen2Cor atmospheric corrections
+  binpath <- sen2r::load_binpaths()
+  # 2- open a command prompt and directly run sen2cor with following command line
+  cmd <- paste(binpath$sen2cor,
+               "--output_dir", R.utils::getAbsolutePath(l2a_path),
+               R.utils::getAbsolutePath(file.path(l1c_path, prodname)), sep = " ")
+  system(cmd)
+  pathl2a <- list.files(path = l2a_path, pattern = datepattern, full.names = TRUE)
+
+  return(pathl2a)
+}
+
+#" This function saves cloud masks.
+#" "cloudMask_Binary" is default binary mask with 0 where clouds are detected and 1 for clean pixels
+#" "cloudMask_RAW" is the original cloud layer produced by atmospheric correction algorithm
+#" --> may be useful to refine cloud mask
+#"
+#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a
+#" @param cloud_path character.
+#" @param s2source character.
+#" @param footprint character. path for vector file defining footprint of interest in the image
+#" @param saveraw boolean. should the original cloud mask layer be saved?
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return list of cloudmasks (binary mask, and raw mask if required)
+#" @importFrom sf st_read
+#" @importFrom stars write_stars
+#" @importFrom raster raster
+#" @export
+save_cloud_s2 <- function(s2_stars, cloud_path, s2source = "SAFE",
+                          footprint = NULL, saveraw = FALSE, maxchunk = 256) {
+
+  whichcloud <- which(names(s2_stars$attr) == "Cloud")
+  # Save cloud mask
+  if (saveraw == TRUE) {
+    cloudraw <- file.path(cloud_path, "CloudMask_RAW")
+    obj <- stars::read_stars(s2_stars$attr[whichcloud], proxy = TRUE)
+    sizeobj <- dim(obj)[1] * dim(obj)[2] / (1024**2)
+    nbchunks <- ceiling(sizeobj / maxchunk)
+    stars::write_stars(obj,
+                       dsn = cloudraw,
+                       driver =  "ENVI",
+                       type = "Byte",
+                       chunk_size = c(dim(obj)[1], dim(obj)[2] / nbchunks),
+                       progress = TRUE)
+  } else {
+    cloudraw <- NULL
+  }
+  # Save cloud mask as in biodivMapR (0 = clouds, 1 = pixel ok)
+  cloudmask <- stars::read_stars(s2_stars$attr[whichcloud], proxy = FALSE)
+  if (s2source == "SAFE" || s2source == "THEIA") {
+    cloudy <- which(cloudmask[[1]] > 0)
+    sunny <- which(cloudmask[[1]] == 0)
+  } else if (s2source == "LaSRC") {
+    cloudy <- which(is.na(cloudmask[[1]]))
+    sunny <- which(cloudmask[[1]] == 1)
+  }
+
+  cloudmask[[1]][cloudy] <- 0
+  cloudmask[[1]][sunny] <- 1
+  cloudbin <- file.path(cloud_path, "CloudMask_Binary")
+  stars::write_stars(cloudmask, dsn = cloudbin, driver =  "ENVI", type = "Byte", overwrite = TRUE)
+  cloudmasks <- list("BinaryMask" = cloudbin, "RawMask" = cloudraw)
+  # delete temporary file
+  file.remove(s2_stars$attr[whichcloud])
+  if (file.exists(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))) file.remove(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))
+  gc()
+  return(cloudmasks)
+}
+
+#" This function saves reflectance files
+#"
+#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a
+#" @param refl_path character. path for reflectance file to be stored
+#" @param format character. file format for reflectance data
+#" @param datatype character. data type (integer, float, 16bits, 32bits...)
+#" @param s2sat character. Sentinel-2 mission ("2A" or "2B")
+#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters)
+#" @param dateacq_s2 double. date of acquisition
+#" @param MTD character. path for metadata file
+#" @param MTD_MSI character. path for metadata MSI file
+#" @param mtd_lasrc character. path for metadata LaSRC file
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return None
+#" @importFrom stars write_stars st_apply
+#" @importFrom XML xml
+#" @export
+save_reflectance_s2 <- function(s2_stars, refl_path, format = "ENVI", datatype = "Int16",
+                                s2sat = NULL, tile_s2 = NULL, dateacq_s2 = NULL,
+                                mtd = NULL, mtd_msi = NULL, mtd_lasrc = NULL,
+                                maxchunk = 256) {
+  # identify if S2A or S2B, if possible
+  s2mission <- check_s2mission(s2sat = s2sat, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2)
+
+  # define central wavelength corresponding to each spectral band
+  if (s2mission == "2A") {
+    wl_s2 <- list("B02" = 496.6, "B03" = 560.0, "B04" = 664.5,
+                  "B05" = 703.9, "B06" = 740.2, "B07" = 782.5, "B08" = 835.1,
+                  "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+  } else if (s2mission == "2B") {
+    wl_s2 <- list("B02" = 492.1, "B03" = 559.0, "B04" = 665.0,
+                  "B05" = 703.8, "B06" = 739.1, "B07" = 779.7, "B08" = 833.0,
+                  "B8A" = 864.0, "B11" = 1610.4, "B12" = 2185.7)
+  }
+  if (s2mission == "2A") {
+    sensor <- "Sentinel_2A"
+  } else if (s2mission == "2B") {
+    sensor <- "Sentinel_2B"
+  }
+
+  # apply offset when necessary
+  listbands_bis <- c("B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12")
+  if (!is.null(mtd_msi) && is.null(mtd_lasrc)) {
+    # read XML file containing info about geometry of acquisition
+    s2xml <- XML::xmlToList(mtd_msi)
+    xml_offset <- s2xml$General_Info$Product_Image_Characteristics$BOA_ADD_offset_VALUES_LIST
+    bands <- lapply(s2xml$General_Info$Product_Image_Characteristics$Spectral_Information_List, "[[", 4)
+    if (!is.null(xml_offset) && !is.null(bands)) {
+      bandid  <- lapply(bands, "[[", 1)
+      bandname  <- lapply(bands, "[[", 2)
+      offset <- data.frame("bandname" = unlist(bandname),
+                           "bandid" = unlist(bandid),
+                           "offset" = unlist(lapply(xml_offset, "[[", 1)))
+      selbands <- match(listbands_bis, offset$bandname)
+      offset <- offset[selbands, ]
+      boa_quantval <- as.numeric(s2xml$General_Info$Product_Image_Characteristics$QUANTIFICATION_VALUES_LIST$BOA_QUANTIFICATION_VALUE[1])
+    } else {
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = 0)
+      boa_quantval <- 10000
+    }
+  } else if (!is.null(mtd_lasrc)) {
+    # read XML file containing info about geometry of acquisition
+    s2xml <- XML::xmlToList(mtd_lasrc)
+    attributes_lasrc <- s2xml$bands[[14]]$.attrs
+    attributes_lasrc_df <- data.frame(attributes_lasrc)
+    if (match("add_offset", rownames(attributes_lasrc_df)) > 0 && match("scale_factor", rownames(attributes_lasrc_df)) > 0) {
+      xml_offset <- as.numeric(attributes_lasrc[["add_offset"]])
+      boa_quantval <- 1 / as.numeric(attributes_lasrc[["scale_factor"]])
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = xml_offset)
+    } else {
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = 0)
+      boa_quantval <- 10000
+    }
+  } else {
+    offset <- data.frame("bandname" = listbands_bis,
+                         "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                         "offset" = 0)
+    boa_quantval <- 10000
+  }
+
+  # identify where spectral bands are in the stars object
+  stars_spectral <- list()
+  starsnames <- names(s2_stars$attr)
+  stars_spectral$bandname <- starsnames[which(!starsnames == "Cloud")]
+  stars_spectral$wavelength <- wl_s2[stars_spectral$bandname]
+
+  sortedwl <- names(wl_s2)
+  reorder <- match(sortedwl, stars_spectral$bandname)
+  elim <- which(is.na(reorder))
+  if (length(elim) > 0) {
+    reorder <- reorder[-elim]
+  }
+  pathr <- s2_stars$attr[reorder]
+
+  names(pathr) <- NULL
+  s2_stars2 <- stars::read_stars(pathr, along = "band", proxy = TRUE)
+  stars_spectral$bandname <- stars_spectral$bandname[reorder]
+  stars_spectral$wavelength <- stars_spectral$wavelength[reorder]
+
+  uniqueoffset <- as.numeric(unique(offset$offset))
+  if (length(uniqueoffset) > 1) {
+    message("Warning: BOA offset differs between bands.")
+    message("offset will not be applied to the final S2 reflectance raster")
+    message("check metadata file to identify the offset applied on each band")
+    print(mtd_msi)
+  } else {
+    message("applying offset to reflectance data")
+    if (is.null(mtd_lasrc) || uniqueoffset == 0) {
+      offsets2 <- function(x) (round(x + uniqueoffset) * (10000 / boa_quantval))
+      s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2)
+    } else {
+      offsets2 <- function(x) (round(10000 * ((x + uniqueoffset * boa_quantval) / boa_quantval)))
+      s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2)
+    }
+  }
+  write_stack_s2(stars_s2 = s2_stars2, stars_spectral = stars_spectral, refl_path = refl_path,
+                 format = format, datatype = datatype, sensor = sensor, maxchunk = maxchunk)
+  # save metadata file as well if available
+  if (!is.null(mtd)) {
+    if (file.exists(mtd)) {
+      file.copy(from = mtd, to = file.path(dirname(refl_path), basename(mtd)), overwrite = TRUE)
+    }
+  }
+  # save metadata file as well if available
+  if (!is.null(mtd_msi)) {
+    if (file.exists(mtd_msi)) {
+      file.copy(from = mtd_msi, to = file.path(dirname(refl_path), basename(mtd_msi)), overwrite = TRUE)
+    }
+  }
+  # save LaSRC metadata file as well if available
+  if (!is.null(mtd_lasrc)) {
+    if (file.exists(mtd_lasrc)) {
+      file.copy(from = mtd_lasrc, to = file.path(dirname(refl_path), basename(mtd_lasrc)), overwrite = TRUE)
+    }
+  }
+  # delete temporary file
+  for (pathtemp in pathr) {
+    file.remove(pathtemp)
+    if (file.exists(paste(pathtemp, ".hdr", sep = ""))) file.remove(paste(pathtemp, ".hdr", sep = ""))
+  }
+  gc()
+  return(invisible())
+}
+
+#" ENVI functions
+#"
+#" based on https://github.com/cran/hyperSpec/blob/master/R/read.ENVI.R
+#" added wavelength, fwhm, ... to header reading
+#" Title
+#"
+#" @param x character.
+#" @param separator character
+#" @param trim_blank boolean.
+#"
+#" @return list.
+#" @export
+split_line <- function(x, separator, trim_blank = TRUE) {
+  tmp <- regexpr(separator, x)
+  key <- substr(x, 1, tmp - 1)
+  value <- substr(x, tmp + 1, nchar(x))
+  if (trim_blank) {
+    blank_pattern <- "^[[:blank:]]*([^[:blank:]]+.*[^[:blank:]]+)[[:blank:]]*$"
+    key <- sub(blank_pattern, "\\1", key)
+    value <- sub(blank_pattern, "\\1", value)
+  }
+  value <- as.list(value)
+  names(value) <- key
+  return(value)
+}
+
+#" save raster footprint as vector file
+#"
+#" @param path_raster character. path for a raster file
+#" @param path_vector character. path for a vector file
+#" @param driver character. driver for vector
+#"
+#" @return None
+#" @importFrom raster raster extent projection
+#" @importFrom sf st_as_sf st_write
+#" @export
+vectorize_raster_extent <- function(path_raster, path_vector, driver = "ESRI Shapefile") {
+  rast <- raster(path_raster)
+  e <- extent(rast)
+  # coerce to a SpatialPolygons object
+  p <- as(e, "SpatialPolygons")
+  projection(p) <- projection(rast)
+  p <- sf::st_as_sf(p)
+  sf::st_write(obj = p, path_vector, driver = driver)  # create to a shapefile
+  return(invisible())
+}
+
+#" writes ENVI hdr file
+#"
+#" @param hdr content to be written
+#" @param hdrpath Path of the hdr file
+#"
+#" @return None
+#" @importFrom stringr str_count
+#" @export
+write_envi_header <- function(hdr, hdrpath) {
+  h <- lapply(hdr, function(x) {
+    if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w+") > 1)) {
+      x <- paste0("{", paste(x, collapse = ", "), "}")
+    }
+    # convert last numerics
+    x <- as.character(x)
+  })
+  writeLines(c("ENVI", paste(names(hdr), h, sep = " = ")), con = hdrpath)
+  return(invisible())
+}
+
+#" This function writes a raster Stack object into a ENVI raster file
+#"
+#" @param stackobj list. raster stack
+#" @param stackpath character. path where to store the stack
+#" @param bands list. should include "bandname", and if possible "wavelength"
+#" @param datatype character. should be INT2S or FLT4S for example
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom utils read.table
+#" @export
+write_rasterstack_envi <- function(stackobj, stackpath, bands, datatype = "INT2S",
+                                   sensor = "Unknown", stretch = FALSE) {
+
+  r <- raster::writeRaster(x = stackobj, filename = stackpath, format = "Ehdr", overwrite = TRUE, datatype = datatype)
+  raster::hdr(r, format = "ENVI")
+  # Edit hdr file to add metadata
+  hdr <- read_envi_header(get_hdr_name(stackpath))
+  hdr$`band names` <- bands$bandname
+  if (length(bands$wavelength) == length(bands$bandname)) {
+    hdr$wavelength <- bands$wavelength
+  } else {
+    hdr$wavelength <- NULL
+  }
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`coordinate system string` <- read.table(paste(stackpath, ".prj", sep = ""))
+  proj <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][1], split = "=")[[1]][2]
+  zone <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][2], split = "=")[[1]][2]
+  datum <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][3], split = "=")[[1]][2]
+  oldproj <- hdr$`map info`
+  newproj <- gsub(pattern = "projection", replacement = proj, x = oldproj)
+  newproj <- paste(newproj, zone, datum, sep = ", ")
+  hdr$`map info` <- newproj
+  hdr$`sensor type` <- sensor
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(stackpath))
+
+  # remove unnecessary files
+  file2remove <- paste(stackpath, ".aux.xml", sep = "")
+  file.remove(file2remove)
+  file2remove <- paste(stackpath, ".prj", sep = "")
+  file.remove(file2remove)
+  file2remove <- paste(stackpath, ".stx", sep = "")
+  file.remove(file2remove)
+  return(invisible())
+}
+
+
+#" This function writes a stars object into a raster file
+#"
+#" @param stars_s2 list. stars object containing raster data. Can be produced with function Crop_n_resample_S2
+#" @param stars_spectral list. band name to be saved in the stack and spectral bands corresponding to the image
+#" @param refl_path character. path where to store the image
+#" @param format character. default = ENVI BSQ. otherwise use gdal drivers
+#" @param datatype character. should be Int16 or Float64 for example
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return None
+#" @export
+write_stack_s2 <- function(stars_s2, stars_spectral, refl_path, format = "ENVI",
+                           datatype = "Int16", sensor = "Unknown", maxchunk = 256) {
+
+  # write raster file from proxy using chunks
+  sizeobj <- 2 * dim(stars_s2)[1] * dim(stars_s2)[2] * dim(stars_s2)[3] / (1024**2)
+  nbchunks <- ceiling(sizeobj / maxchunk)
+  stars::write_stars(obj = stars_s2,
+                     dsn = refl_path,
+                     driver =  format,
+                     type = datatype,
+                     chunk_size = c(dim(stars_s2)[1], ceiling(dim(stars_s2)[2] / nbchunks)),
+                     progress = TRUE)
+
+  if (format == "ENVI") {
+    adjust_envi_hdr(dsn = refl_path, bands = stars_spectral,
+                    sensor = sensor, stretch = TRUE)
+  }
+  return(invisible())
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/alpha_beta.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,142 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    # type of PCA:
+    # PCA: no rescaling of the data
+    # SPCA: rescaling of the data
+    typepca <- as.character(args[4])
+    alpha <- as.logical(args[5])
+    beta <- as.logical(args[6])
+    funct <- as.logical(args[7])
+    all <- as.logical(args[8])
+    nbcpu <- as.integer(args[9])
+    source(args[10])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+pca_files <- pca_output$PCA_Files
+pix_per_partition <- pca_output$Pix_Per_Partition
+nb_partitions <- pca_output$nb_partitions
+# path for the updated mask
+input_mask_file <- pca_output$MaskPath
+
+
+selected_pcs <- seq(1, dim(raster::stack(input_image_file))[3])
+
+selected_pcs <- all(selected_pcs)
+################################################################################
+##                      MAP ALPHA AND BETA DIVERSITY                          ##
+################################################################################
+print("MAP SPECTRAL SPECIES")
+
+kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, SelectedPCs = selected_pcs, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, TypePCA = typepca, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters)
+
+image_name <- tools::file_path_sans_ext(basename(input_image_file))
+if (alpha == TRUE || beta == TRUE || all == TRUE) {
+## alpha
+  print("MAP ALPHA DIVERSITY")
+  index_alpha <- c("Shannon")
+  alpha_div <- biodivMapR::map_alpha_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, Index_Alpha = index_alpha, nbclusters = nbclusters, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE)
+
+  alpha_zip <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres.zip")
+  alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA")
+  unzip(alpha_zip, exdir = alpha_path)
+  alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres")
+  alpha_raster <- raster::raster(alpha_path)
+  get_alpha <- convert_raster(alpha_raster)
+
+ if (alpha == TRUE || all == TRUE) {
+   colnames(get_alpha) <- c("Alpha", "longitude", "latitude")
+   plot_indices(get_alpha, titre = "Alpha")
+
+   write.table(get_alpha, file = "alpha.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+}
+  if (beta == TRUE || all == TRUE) {
+## beta
+  print("MAP BETA DIVERSITY")
+  beta_div <- biodivMapR::map_beta_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters)
+
+  beta_path <- file.path(output_dir, image_name, typepca, "BETA", "BetaDiversity_BCdiss_PCO_10")
+  beta_raster <- raster::raster(beta_path)
+  get_beta <- convert_raster(beta_raster)
+
+  colnames(get_beta) <- c("Beta", "longitude", "latitude")
+  plot_indices(get_beta, titre = "Beta")
+
+  write.table(get_beta, file = "beta.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+  }
+}
+
+
+################################################################################
+##          COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS                 ##
+################################################################################
+
+if (funct == TRUE || all == TRUE) {
+  mapper <- biodivMapR::map_functional_div(Original_Image_File = input_image_file, Functional_File = pca_files,  Selected_Features = selected_pcs, Output_Dir = output_dir, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, TypePCA = typepca, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE)
+
+  funct_zip <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres.zip")
+  funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL")
+  unzip(funct_zip, exdir = funct_path)
+  funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres")
+  funct_raster <- raster::raster(funct_path)
+  get_funct <- convert_raster(funct_raster)
+
+  colnames(get_funct) <- c("Functionnal", "longitude", "latitude")
+  plot_indices(get_funct, titre = "Functionnal")
+
+  write.table(get_funct, file = "Functionnal.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/biodiv_indices_global.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,112 @@
+#Rscript
+
+###########################################
+##    Mapping  biodiversity indicators   ##
+###########################################
+
+#####Packages :  raster,
+#                rgdal,
+#                sp,
+#                rasterdiv,
+#                ggplot2,
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    data_header <- args[2]
+    data <- args[3]
+    alpha <- as.numeric(args[4])
+    source(args[5])
+
+}
+
+########################################################################
+##                   COMPUTE BIODIVERSITY INDICES                     ##
+########################################################################
+
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  data_raster <- file.path("data_dir/results/Reflectance", data_raster[1])
+}
+
+# Read raster
+copndvi <- raster::raster(data_raster)
+
+copndvilr <- raster::reclassify(copndvi, cbind(252, 255, NA), right = TRUE)
+
+#Resample using raster::aggregate and a linear factor of 10
+copndvilr <- raster::aggregate(copndvilr, fact = 20)
+#Set float numbers as integers to further speed up the calculation
+storage.mode(copndvilr[]) <- "integer"
+
+# Convert raster to SpatialPointsDataFrame
+r_pts <- convert_raster(copndvilr)
+
+#Shannon's Diversity
+sha <- rasterdiv::Shannon(copndvilr, window = 9, np = 1)
+sha_df <- data.frame(raster::rasterToPoints(sha, spatial = TRUE))
+sha_name <- "Shannon"
+r_pts[, sha_name] <- sha_df[, 1]
+
+#Renyi's Index
+ren <- rasterdiv::Renyi(copndvilr, window = 9, alpha = alpha, np = 1)
+ren_df <- data.frame(raster::rasterToPoints(ren[[1]]))
+ren_name <- "Renyi"
+r_pts[, ren_name] <- ren_df[, 3]
+
+#Berger-Parker's Index
+ber <- rasterdiv::BergerParker(copndvilr, window = 9, np = 1)
+ber_df <- data.frame(raster::rasterToPoints(ber, spatial = TRUE))
+ber_name <- "Berger-Parker"
+r_pts[, ber_name] <- ber_df[, 1]
+
+#Pielou's Evenness
+pie <- rasterdiv::Pielou(copndvilr, window = 9, np = 1)
+pie_df <- data.frame(raster::rasterToPoints(pie))
+if (length(pie_df[, 1]) == length(r_pts[, 1])) {
+  pie_name <- "Pielou"
+  r_pts[, pie_name] <- pie_df[, 1]
+}
+
+#Hill's numbers
+hil <- rasterdiv::Hill(copndvilr, window = 9, alpha = alpha, np = 1)
+hil_df <- data.frame(raster::rasterToPoints(hil[[1]]))
+hil_name <- "Hill"
+r_pts[, hil_name] <- hil_df[, 3]
+
+#Parametric Rao's quadratic entropy with alpha ranging from 1 to 5
+prao <- rasterdiv::paRao(copndvilr, window = 9, alpha = alpha, dist_m = "euclidean", np = 1)
+prao_df <- data.frame(raster::rasterToPoints(prao$window.9[[1]]))
+prao_name <- "Prao"
+r_pts[, prao_name] <- prao_df[, 3]
+
+#Cumulative Residual Entropy
+cre <- rasterdiv::CRE(copndvilr, window = 9, np = 1)
+cre_df <- data.frame(raster::rasterToPoints(cre))
+if (length(cre_df[, 1]) == length(r_pts[, 1])) {
+  cre_name <- "CRE"
+  r_pts[, cre_name] <- cre_df[, 1]
+}
+
+if (length(cre_df[, 1]) == length(r_pts[, 1]) || length(pie_df[, 1]) == length(r_pts[, 1])) {
+list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Pielou", "Hill", "Prao", "CRE")
+} else {
+list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Hill", "Prao")
+}
+## Plotting all the graph and writing a tabular
+for (indice in list_indice) {
+  plot_indices(data = r_pts, titre = indice)
+}
+
+write.table(r_pts, file = "BiodivIndex.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/comparison_div.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,190 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+##remotes::install_github("jbferet/biodivMapR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    plots_zip <- args[4]
+    choice <- as.character(args[5])
+    source(args[6])
+    # type of PCA:
+    # PCA: no rescaling of the data
+    # SPCA: rescaling of the data
+    typepca <- as.character(args[7])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+pca_files <- pca_output$PCA_Files
+pix_per_partition <- pca_output$Pix_Per_Partition
+nb_partitions <- pca_output$nb_partitions
+# path for the updated mask
+input_mask_file <- pca_output$MaskPath
+
+# 3- Select principal components from the PCA raster
+# Select components from the PCA/SPCA/MNF raster
+sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8")
+image_name <- tools::file_path_sans_ext(basename(input_image_file))
+output_dir_full <- file.path(output_dir, image_name, typepca, "PCA")
+
+write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt"))
+sel_pc <-  file.path(output_dir_full, "Selected_Components.txt")
+
+
+################################################################################
+##                      MAP ALPHA AND BETA DIVERSITY                          ##
+################################################################################
+print("MAP SPECTRAL SPECIES")
+
+kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca)
+
+################################################################################
+##          COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS                 ##
+################################################################################
+## read selected features from dimensionality reduction
+
+## path for selected components
+
+# location of the directory where shapefiles used for validation are saved
+dir.create("VectorDir")
+unzip(plots_zip, exdir = "VectorDir")
+
+# list vector data
+path_vector <- biodivMapR::list_shp("VectorDir")
+name_vector <- tools::file_path_sans_ext(basename(path_vector))
+
+# location of the spectral species raster needed for validation
+path_spectralspecies <- kmeans_info$SpectralSpecies
+# get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...)
+biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = FALSE)
+
+shannon_rs <- c(biodiv_indicators$Shannon)[[1]]
+fric <- c(biodiv_indicators$FunctionalDiversity$FRic)
+feve <- c(biodiv_indicators$FunctionalDiversity$FEve)
+fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv)
+# if no name for plots
+biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1)
+
+
+####################################################
+# write RS indicators                              #
+####################################################
+# write a table for Shannon index
+
+# write a table for all spectral diversity indices corresponding to alpha diversity
+results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher,
+                      biodiv_indicators$Shannon, biodiv_indicators$Simpson,
+                      biodiv_indicators$FunctionalDiversity$FRic,
+                      biodiv_indicators$FunctionalDiversity$FEve,
+                      biodiv_indicators$FunctionalDiversity$FDiv)
+
+names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv")
+write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+if (choice == "Y") {
+# write a table for Bray Curtis dissimilarity
+bc_mean <- biodiv_indicators$BCdiss
+bray_curtis <- data.frame(name_vector, bc_mean)
+colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1])
+write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+####################################################
+# illustrate results
+####################################################
+# apply ordination using PCoA (same as done for map_beta_div)
+
+mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE)
+betapco <- labdsv::pco(mat_bc_dist, k = 3)
+
+# assign a type of vegetation to each plot, assuming that the type of vegetation
+# is defined by the name of the shapefile
+
+nbsamples <- shpname <- c()
+for (i in 1:length(path_vector)) {
+  shp <- path_vector[i]
+  nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE))
+  shpname[i] <- tools::file_path_sans_ext(basename(shp))
+}
+
+type_vegetation <- c()
+for (i in 1: length(nbsamples)) {
+  for (j in 1:nbsamples[i]) {
+    type_vegetation <- c(type_vegetation, shpname[i])
+  }
+}
+
+#data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space
+results <- data.frame("vgtype" = type_vegetation, "pco1" = betapco$points[, 1], "pco2" = betapco$points[, 2], "pco3" = betapco$points[, 3], "shannon" = shannon_rs, "fric" = fric, "feve" = feve, "fdiv" = fdiv)
+
+#plot field data in the PCoA space, with size corresponding to shannon index
+g1 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco2, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g2 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g3 <- ggplot2::ggplot(results, ggplot2::aes(x = pco2, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+#extract legend
+get_legend <- function(a_gplot) {
+    tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot))
+    leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+    legend <- tmp$grobs[[leg]]
+    return(legend)
+}
+
+legend <- get_legend(g3)
+gall <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1 + ggplot2::theme(legend.position = "none"), g2 + ggplot2::theme(legend.position = "none"), g3 + ggplot2::theme(legend.position = "none"), nrow = 1), legend, nrow = 2, heights = c(3, 2))
+
+
+filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE)
+
+filename
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/comparison_div.xml	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,138 @@
+<tool id="srs_process_data" name="Compare diversity indicators" version="@VERSION@" profile="20.01">
+    <description>with remote sensing data</description>
+    <macros>
+        <import>macro.xml</import>
+    </macros>
+    <expand macro="SRS_requirements"/>
+    <command detect_errors="exit_code"><![CDATA[
+        #import re 
+        #if $method.type == 'envi_bil': 
+          #set input_raster = $method.input_raster
+          #set input_raster_identifier = re.sub('[^\s\w\-]', '_', str($input_raster.element_identifier)) 
+          #set input_header = $method.input_header
+          #set input_header_identifier = re.sub('[^\s\w\-]+[^.hdr]', '_', str($input_header.element_identifier)) 
+          cp '${input_raster}' '${input_raster_identifier}' &&
+          cp '${input_header}' '${input_header_identifier}' &&
+        #end if
+        Rscript
+            '$__tool_directory__/comparison_div.r'
+             #if $method.type == 'envi_bil':
+              '$input_raster_identifier' 
+              '$input_header_identifier'
+              ''
+            #else:
+              ''
+              ''
+              '$method.input'
+            #end if
+            '$input_plot'
+            '$choice'
+            '$__tool_directory__/functions.r'
+            '$typepca'
+            '$output_div'
+            '$output_beta'
+            '$plots'
+        ]]>
+    </command>
+    <inputs>
+        <conditional name="method">
+            <param name="type" type="select" label="In which format are your data ?">
+                <option value="zipper">The data you are using are in a zip folder Reflectance</option>
+                <option value="envi_bil">Your already have the files in ENVI BIL format</option>
+            </param>
+            <when value="zipper">
+                <param name="input" type="data" format="zip" multiple="true" label="Input data"/>
+            </when>
+            <when value="envi_bil">
+                <param name="input_raster" type="data" format="bil" label="Input raster" help="It can be the raw data in bil or the PCA raster layer in bil"/>
+                <param name="input_header" type="data" format="hdr" label="Input header"/>
+            </when>
+        </conditional>
+        <param name="input_plot" type="data" format="data" label="Plots folder zip"/>
+        <param name="choice" type="select" label="Do you want to compute beta diversity (needs mutliple locations) ?" display="radio">
+            <option value="Y">Yes</option>
+            <option value="N">No</option>
+        </param>
+        <param name="typepca" type="select" label="Do you want to do a PCA or a SPCA ?" display="radio" help="If you choose PCA there is no rescaling of the data as oppposed as if you choose SPCA">
+            <option value="SPCA">SPCA</option>
+            <option value="PCA">PCA</option>
+        </param>
+    </inputs>
+    <outputs>
+        <data name="output_div" from_work_dir="Diversity.tabular" format="tabular" label="Global diversity">
+        </data>
+        <data name="output_beta" from_work_dir="BrayCurtis.tabular" format="tabular" label="Bray Curtis">
+            <filter>choice == 'Y'</filter>
+        </data>
+        <collection type="list" name="plots" label="Comparison plot">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.png" visible="false" format="png"/>
+            <filter>choice =='Y'</filter>
+        </collection>
+    </outputs>
+    <tests>
+        <test>
+            <param name="type" value="envi_bil"/>
+            <param name="input_raster" value="S2A_Subset"/>
+            <param name="input_header" value="S2A_Subset.hdr"/>
+            <param name="input_plot" value="S2A_T33NUD_Plots.zip"/>
+            <param name="choice" value="Y"/>
+            <output name="output_div">
+                <assert_contents>
+                    <has_n_lines n="25"/>
+                </assert_contents>
+            </output>
+            <output name="output_beta">
+                <assert_contents>
+                    <has_n_lines n="25"/>
+                </assert_contents>
+            </output>
+            <output_collection name="plots" type="list" count="1"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+========================================================================
+Process satellite remote sensing data to produce biodiversity indicators
+========================================================================
+
+
+**What it does**
+
+Féret and Asner (2014) developed a method for **tropical forest** diversity mapping based on very high spatial resolution airborne imaging spectroscopy.
+
+The goal of this tool using the package biodivMapR is to compute diversity indices over each spatial polygon of a shapefile of plots, if available, in order to compare field inventories with diversity indices estimated from remotely-sensed images.
+
+**Input description**
+
+It expects an image file as input, with a specific data format. ENVI HDR image with BIL interleave required.
+The image is an ENVI raster including :
+
+- A binary file (which has no extension here).
+
+- A header file (with .hdr extension).
+
+The header file is a text file including all necessary metadata which can be read with a text editor. It includes image dimensions, projection, and the name and central wavelength for each spectral band.
+
+In order to get such input we advise to use the tool preprocessing sentinel 2 data. 
+
++--------------+----------+--------------+
+|      BIL     | ENVI HDR |  Shapefiles  |
++==============+==========+==============+
+| raster stack | Metadata |  plots.zip   |
++--------------+----------+--------------+
+|      ...     |    ...   |      ...     |
++--------------+----------+--------------+
+
+**Output**
+
+- Two tabulars : 
+    - One matrix for Bray-Curtis indicator
+    - One table for the following indicators; Species richness, shannon, fisher, simpson, richness, eveness, divergence
+
+- One comparison png plot in the Pcoa space that summarizes α- and β-diversity in scatterplots and illustrates that the combination of the three components computed with PCoA allows proper differentiation among vegetation types:
+    - PCoA#1 allows differentiating medium and high diversity forests from low diversity forest and low vegetation, but does not discriminate medium and high diversity forests.
+    - PCoA#2 allows differentiating low diversity forest from medium/high diversity forests and low vegetation
+    - PCoA#3 allows differentiating medium diversity forests from high diversity forests and low vegetation.
+
+    ]]>    </help>
+        <expand macro="SRS_BDMRref"/>
+</tool>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/functions.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,79 @@
+#Rscript
+
+#########################
+##      Functions      ##
+#########################
+
+#####Packages : raster
+#               sp
+#               ggplot2
+
+####Set paramaters for all tools using BiodivMapR
+
+# path for the Mask raster corresponding to image to process
+# expected to be in ENVI HDR format, 1 band, integer 8bits
+# expected values in the raster: 0 = masked, 1 = selected
+# set to FALSE if no mask available
+input_mask_file <- FALSE
+
+# relative or absolute path for the Directory where results will be stored
+# For each image processed, a subdirectory will be created after its name
+output_dir <- "RESULTS"
+
+# SPATIAL RESOLUTION
+# resolution of spatial units for alpha and beta diversity maps (in pixels), relative to original image
+# if Res.Map = 10 for images with 10 m spatial resolution, then spatial units will be 10 pixels x 10m = 100m x 100m surfaces
+# rule of thumb: spatial units between 0.25 and 4 ha usually match with ground data
+# too small window_size results in low number of pixels per spatial unit, hence limited range of variation of diversity in the image
+window_size <- 10
+
+# PCA FILTERING: Set to TRUE if you want second filtering based on PCA outliers to be processed. Slower
+filterpca <- TRUE
+
+################################################################################
+##                    DEFINE PARAMETERS FOR METHOD                            ##
+################################################################################
+nbcpu <- 4
+maxram <- 0.5
+nbclusters <- 50
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+ndvi_thresh <- 0.5
+blue_thresh <- 500
+nir_thresh  <- 1500
+continuum_removal <- TRUE
+
+
+
+#### Convert raster to dataframe
+
+# Convert raster to SpatialPointsDataFrame
+convert_raster <- function(data_raster) {
+r_pts <- raster::rasterToPoints(data_raster, spatial = TRUE)
+
+# reproject sp object
+geo_prj <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
+r_pts <- sp::spTransform(r_pts, sp::CRS(geo_prj))
+
+
+# Assign coordinates to @data slot, display first 6 rows of data.frame
+r_pts@data <- data.frame(r_pts@data, longitude = sp::coordinates(r_pts)[, 1],
+                         latitude = sp::coordinates(r_pts)[, 2])
+
+return(r_pts@data)
+}
+
+
+#### Potting indices
+
+plot_indices <- function(data, titre) {
+  graph_indices <- ggplot2::ggplot(data) +
+  ggplot2::geom_point(ggplot2::aes_string(x = data[, 2], y = data[, 3], color = data[, titre]), shape = "square", size = 2) + ggplot2::scale_colour_gradient(low = "blue", high = "orange", na.value = "grey50") +
+  ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) + ggplot2::ggtitle(titre)
+
+ggplot2::ggsave(paste0(titre, ".png"), graph_indices, width = 12, height = 10, units = "cm")
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/indices_spectral.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,105 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages :  expint,
+#                pracma,
+#                R.utils,
+#                raster,
+#                sp,
+#                matrixStats,
+#                ggplot2,
+#                expandFunctions,
+#                stringr,
+#                XML,
+#                rgdal,
+#                stars,
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    data_header <- args[2]
+    data <- args[3]
+    source(args[4])
+    source(args[5])
+    source(args[6])
+    indice_choice <- strsplit(args[7], ",")[[1]]
+    source(args[8])
+    output_raster <- as.character(args[9])
+
+}
+
+########################################################################
+##                  COMPUTE SPECTRAL INDEX : NDVI                     ##
+########################################################################
+
+if (data != "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+
+  # Read raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  data_raster <- file.path("data_dir/results/Reflectance", data_raster[1])
+  refl <- raster::brick(data_raster)
+  refl2 <- raster::raster(data_raster)
+} else {
+  # Read raster
+  refl <- raster::brick(data_raster)
+  refl2 <- raster::raster(data_raster)
+}
+# get raster band name and clean format. Expecting band name and wav
+# reflFactor = 10000 when reflectance is coded as INT16
+refl <- raster::aggregate(refl, fact = 10)
+
+# Convert raster to SpatialPointsDataFrame
+refl2 <- raster::aggregate(refl2, fact = 10)
+r_pts <- convert_raster(refl2)
+table_ind <- r_pts
+# create directory for Spectralelength to be documented in image
+hdr_refl <- read_envi_header(get_hdr_name(data_raster))
+sensorbands <- hdr_refl$wavelength
+# compute a set of spectral indices defined by indexlist from S2 data indices
+si_path <- file.path("SpectralIndices")
+dir.create(path = si_path, showWarnings = FALSE, recursive = TRUE)
+# Save spectral indices
+
+indices <- lapply(indice_choice, function(x) {
+  indices_list <- computespectralindices_raster(refl = refl, sensorbands = sensorbands,
+                                                  sel_indices = x,
+                                                  reflfactor = 10000, stackout = FALSE)
+
+  index_path <- file.path(si_path, paste(basename(data_raster), "_", x, sep = ""))
+  spec_indices <- stars::write_stars(stars::st_as_stars(indices_list$spectralindices[[1]]), dsn = index_path, driver = "ENVI", type = "Float32")
+
+  # write band name in HDR
+  hdr <- read_envi_header(get_hdr_name(index_path))
+  hdr$`band names` <- x
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(index_path))
+  # Writting the tabular and the plot
+  r_pts[, x] <- as.data.frame(sapply(spec_indices, c))
+  plot_indices(data = r_pts, titre = x)
+  return(r_pts)
+})
+
+new_table <- as.data.frame(indices)
+new_table <- new_table[, !grepl("longitude", names(new_table))]
+new_table <- new_table[, !grepl("latitude", names(new_table))]
+new_table <- new_table[, !grepl(basename(data_raster), names(new_table))]
+
+table_ind <- cbind(table_ind, new_table)
+if (length(indice_choice) == 1) {
+  colnames(table_ind) <- c(basename(data_raster), "longitude", "latitude", indice_choice)
+}
+
+write.table(table_ind, file = "Spec_Index.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+# Get the raster layer of the indice as an output
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macro.xml	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,102 @@
+<macros>
+    <token name="@VERSION@">0.0.1</token>
+    <xml name="SRS_requirements">
+        <requirements>
+            <requirement type="package" version="4.2.2">r-base</requirement>
+            <requirement type="package" version="2.12.2">r-r.utils</requirement>
+            <requirement type="package" version="1.0_7">r-sf</requirement>
+            <requirement type="package" version="1.5_32">r-rgdal</requirement>
+            <requirement type="package" version="0.6_1">r-rgeos</requirement>
+            <requirement type="package" version="0.5_5">r-stars</requirement>
+            <requirement type="package" version="1.5.0">r-stringr</requirement>
+            <requirement type="package" version="1.9.8">r-biodivmapr</requirement>
+            <requirement type="package" version="0.36">r-xfun</requirement>
+            <requirement type="package" version="0.51.4">r-rastervis</requirement>
+            <requirement type="package" version="3.4.0">r-ggplot2</requirement>
+            <requirement type="package" version="2.11.0">r-mapview</requirement>
+            <requirement type="package" version="2.3">r-gridextra</requirement>
+            <requirement type="package" version="3.0.0">r-emstreer</requirement>
+            <requirement type="package" version="3.2.3">r-filesstrings</requirement>
+            <!--requirement type="package" version="9.0.1">proj</requirement-->
+            <yield/>
+        </requirements>
+    </xml>
+    <xml name="SRS_input">
+       <param name="input" type="data" format="tabular,csv,txt" label="Input table"/>
+       <param name="colnames" type="boolean" label="First line is a header line" checked="true"/>
+    </xml>
+    <xml name="SRS_bibref">
+       <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {obisindicators: Develop marine biodiversity indicators from OBIS data},
+            author = {Ben Ben and Pieter Provoost and Tylar Murray},
+            year = {2022},
+            note = {https://marinebon.org/obisindicators,
+            https://github.com/marinebon/obisindicators},
+            }
+            </citation>
+        </citations>
+    </xml>
+    <xml name="SRS_S2ref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {preprocS2: preprocessing of Sentinel-2 data downloaded from various data hubs / produced from various atmospheric correction methods},
+            author = {Jean-Baptiste Feret},
+            year = {2022},
+            note = {R package version 1.1.3},
+            url = {https://gitlab.com/jbferet/preprocS2},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_BDMRref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {biodivMapR: biodivMapR: an R package for a- and ß-diversity mapping using remotely-sensed images},
+            author = {Jean-Baptiste Feret and Florian {de Boissieu}},
+            year = {2022},
+            note = {R package version 1.9.4},
+            url = {https://github.com/jbferet/biodivMapR},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_rasterdivref">
+        <citations>
+            <citation type="doi">doi:10.1016/j.ecolind.2016.07.039</citation> 
+            <citation type="doi">doi:10.1101/2021.01.23.427872</citation>     
+        </citations>
+    </xml>
+    <xml name="SRS_prosailref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {prosail: PROSAIL leaf and canopy radiative transfer model and inversion routines},
+            author = {Jean-Baptiste Feret and Florian {de Boissieu}},
+            year = {2022},
+            note = {R package version 1.1.1},
+            url = {https://gitlab.com/jbferet/prosail},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_metaref">
+        <citations>
+            <citation type="doi">doi.org/10.5281/zenodo.5907920</citation>
+        </citations>
+    </xml>
+    <xml name="SRS_vegetpheno">
+        <citations>
+            <citation type="doi">doi.org/10.1016/j.rse.2013.09.022</citation>
+        </citations>
+    </xml>
+    <xml name="topic">
+        <edam_topics>
+           <edam_topic>topic_0610</edam_topic>
+           <edam_topic>topic_3050</edam_topic>
+        </edam_topics>
+    </xml>
+</macros>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pca_raster.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,75 @@
+#Rscript
+
+###########################################
+##    Getting PCA raster   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+## remotes::install_github("jbferet/biodivMapR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    typepca <- as.character(args[4])
+    source(args[5])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+# expected to be in ENVI HDR
+
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+
+pca_path <- file.path(output_dir, basename(data_raster), typepca, "PCA", "OutputPCA_8_PCs")
+pca_raster <- raster::raster(pca_path)
+get_pca <- convert_raster(pca_raster)
+
+colnames(get_pca) <- c("PCA", "longitude", "latitude")
+plot_indices(get_pca, titre = "PCA")
+
+write.table(get_pca, file = "PCA.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+#### Get the raster layer files
+pca_files <- file.path("RESULTS", basename(data_raster), typepca, "PCA")
+to_dir_short <- output_dir
+file.copy(pca_files, to_dir_short) #copy files from long to short paths
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/preprocess_S2.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,121 @@
+#Rscript
+
+###########################################
+##     Preprocessing Sentinel 2 data     ##
+###########################################
+
+#####Packages : sen2r,
+#    jqr,
+#    protolite,
+#    raster,
+#    sf,
+#    rgeos,
+#    sp,
+#    raster,
+#    stars,
+#    stringr,
+#    progress,
+#    rgdal,
+#    R.utils,
+#    gdalUtils,
+#    fasterize,
+#    XML,
+#    XML2
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data <- args[1]
+    source(args[2])
+    data_source <- as.character(args[3])
+    sat_type <- as.character(args[4])
+}
+
+##____________________________________________________________________##
+##        Define where data is stored and where to write results      ##
+##--------------------------------------------------------------------##
+
+#Create a directory where to unzip your folder of data
+dir.create("data_dir")
+unzip(data, exdir = "data_dir")
+
+# Result directory
+result_path <- "results"
+dir.create(path = result_path, showWarnings = FALSE, recursive = TRUE)
+
+#Csv file for output useless but needed for linter
+write.csv(data_source, "Mission.csv")
+
+# define raster path
+if (data_source == "SAFE") {
+    path_s2 <- file.path("data_dir", list.files("data_dir", pattern = ".SAFE"))
+    #To define the level and know if a correction is needed (convert not ready yet)
+    level_info <- get_s2_level(path_s2)
+    if (level_info == "L1C") {
+        stop("! This tool works for data of L2A level and NOT for the L1C level which is currently a work in progress !")
+    }
+}else {
+    path_s2 <- file.path("data_dir")
+}
+
+##____________________________________________________________________##
+##                  Extract, resample & stack data                    ##
+##--------------------------------------------------------------------##
+# define resolution
+resolution <- 10
+# define source of data
+s2source <- data_source
+
+s2obj <- extract_from_s2_l2a(path_dir_s2 = path_s2,
+                                        path_vector = NULL,
+                                        s2source = s2source,
+                                        resolution = resolution)
+
+##____________________________________________________________________##
+##                        Write CLOUD MASK                            ##
+##--------------------------------------------------------------------##
+
+# directory for cloud mask
+cloud_path <- file.path(result_path, "CloudMask")
+dir.create(path = cloud_path, showWarnings = FALSE, recursive = TRUE)
+# Filename for cloud mask
+cloudmasks <- save_cloud_s2(s2_stars = s2obj$s2_stack,
+                                       cloud_path = cloud_path,
+                                       s2source = s2source, saveraw = TRUE)
+
+zip_cloud <- file.path("Cloud.zip")
+zip::zip(zip_cloud, cloud_path)
+##____________________________________________________________________##
+##                        Write REFLECTANCE                           ##
+##--------------------------------------------------------------------##
+
+# directory for Reflectance
+refl_dir <- file.path(result_path, "Reflectance")
+dir.create(path = refl_dir, showWarnings = FALSE, recursive = TRUE)
+
+if (data_source == "SAFE") {
+    # filename for Reflectance
+    refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$GRANULE), "_Refl", sep = ""))
+
+    # Save Reflectance file as ENVI image with BIL interleaves
+    tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][2], 2)
+    dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][4], 1, 8), format = "%Y%m%d")
+}else {
+    # filename for Reflectance
+    refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$path_tile_s2), "_Refl", sep = ""))
+
+    # Save Reflectance file as ENVI image with BIL interleaves
+    tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][2], 2)
+    dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][4], 1, 8), format = "%Y%m%d")
+}
+
+save_data <- save_reflectance_s2(s2_stars = s2obj$s2_stack, refl_path = refl_path,
+                               s2sat = sat_type, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2,
+                               format = "ENVI", datatype = "Int16", mtd = s2obj$s2_bands$metadata, mtd_msi = s2obj$s2_bands$metadata_MSI)
+
+zip_files <- file.path("Refl.zip")
+zip::zip(zip_files, refl_dir)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_PROSAIL.R	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,1233 @@
+# ============================================================================= =
+# prosail
+# Lib_PROSAIL.R
+# ============================================================================= =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr >
+# Florian de BOISSIEU <fdeboiss@gmail.com >
+# copyright 2019 / 11 Jean-Baptiste FERET
+# ============================================================================= =
+# This Library includes functions dedicated to PROSAIL simulation
+# SAIL versions available are 4SAIL and 4SAIL2
+# ============================================================================= =
+
+#" computes bidirectional reflectance factor based on outputs from PROSAIL and sun position
+#"
+#" The direct and diffuse light are taken into account as proposed by:
+#" Francois et al. (2002) conversion of 400-1100 nm vegetation albedo
+#" measurements into total shortwave broadband albedo using a canopy
+#" radiative transfer model,  Agronomie
+#" Es = direct
+#" Ed = diffuse
+#"
+#" @param rdot numeric. Hemispherical-directional reflectance factor in viewing direction
+#" @param rsot numeric. Bi-directional reflectance factor
+#" @param tts numeric. Solar zenith angle
+#" @param specatm_sensor list. direct and diffuse radiation for clear conditions
+#" @return brf numeric. Bidirectional reflectance factor
+#" @export
+compute_brf  <- function(rdot, rsot, tts, specatm_sensor) {
+
+  ############################## #
+  ##	direct  /  diffuse light	##
+  ############################## #
+  es <- specatm_sensor$Direct_Light
+  ed <- specatm_sensor$Diffuse_Light
+  rd <- pi / 180
+  skyl <- 0.847 - 1.61 * sin((90 - tts) * rd) +  1.04 * sin((90 - tts) * rd) * sin((90 - tts) * rd) # diffuse radiation (Francois et al.,  2002)
+  pardiro <- (1 - skyl) * es
+  pardifo <- skyl * ed
+  brf <- (rdot * pardifo + rsot * pardiro) / (pardiro + pardifo)
+  return(brf)
+}
+
+#" Performs PROSAIL simulation based on a set of combinations of input parameters
+#" @param spec_sensor list. Includes optical constants required for PROSPECT
+#" refractive index,  specific absorption coefficients and corresponding spectral bands
+#" @param input_prospect  list. PROSPECT input variables
+#" @param n numeric. Leaf structure parameter
+#" @param chl numeric. chlorophyll content (microg.cm-2)
+#" @param car numeric. carotenoid content (microg.cm-2)
+#" @param ant numeric. anthocyain content (microg.cm-2)
+#" @param brown numeric. brown pigment content (Arbitrary units)
+#" @param ewt numeric. Equivalent Water Thickness (g.cm-2)
+#" @param lma numeric. Leaf Mass per Area (g.cm-2)
+#" @param prot numeric. protein content  (g.cm-2)
+#" @param cbc numeric. nonprotcarbon-based constituent content (g.cm-2)
+#" @param alpha numeric. Solid angle for incident light at surface of leaf (simulation of roughness)
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param q numeric. Hot Spot parameter
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#" @param fraction_brown numeric. Fraction of brown leaf area
+#" @param diss numeric. Layer dissociation factor
+#" @param cv numeric. vertical crown cover percentage
+#" = % ground area covered with crowns as seen from nadir direction
+#" @param zeta numeric. Tree shape factor
+#" = ratio of crown diameter to crown height
+#" @param sailversion character. choose between 4SAIL and 4SAIL2
+#" @param brownvegetation list. Defines optical properties for brown vegetation,  if not nULL
+#" - WVL,  reflectance,  Transmittance
+#" - Set to nULL if use PROSPECT to generate it
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" @import prospect
+#" @export
+pro4sail  <- function(spec_sensor, input_prospect = nULL, n = 1.5, chl = 40.0,
+                     car = 8.0, ant = 0.0, brown = 0.0, ewt = 0.01,
+                     lma = 0.008, prot = 0.0, cbc = 0.0, alpha = 40.0,
+                     typelidf = 2, lidfa = nULL, lidfb = nULL, lai = nULL,
+                     q = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL,
+                     fraction_brown = 0.0,  diss = 0.0,  cv = 1, zeta = 1,
+                     sailversion = "4SAIL", brownvegetation = nULL) {
+
+  ############################ #
+  #	LEAF OPTICAL PROPERTIES	##
+  ############################ #
+  if (is.null(input_prospect)) {
+    input_prospect <- data.frame("chl" = chl, "car" = car, "ant" = ant, "brown" = brown, "ewt" = ewt,
+                                "lma" = lma, "prot" = prot, "cbc" = cbc, "n" = n, "alpha" = alpha)
+  }
+  greenvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor,
+                                        n = input_prospect$n[1],
+                                        chl = input_prospect$chl[1],
+                                        car = input_prospect$car[1],
+                                        ant = input_prospect$ant[1],
+                                        brown = input_prospect$brown[1],
+                                        ewt = input_prospect$ewt[1],
+                                        lma = input_prospect$lma[1],
+                                        prot = input_prospect$prot[1],
+                                        cbc = input_prospect$cbc[1],
+                                        alpha = input_prospect$alpha[1])
+
+  if (sailversion  ==  "4SAIL2") {
+    # 4SAIL2 requires one of the following combination of input parameters
+    # Case #1: valid optical properties for brown vegetation
+    if (!is.null(brownvegetation)) {
+      # need to define reflectance and Transmittance for brownvegetation
+      if (length(grep("reflectance", names(brownvegetation))) == 0 || length(grep("Transmittance", names(brownvegetation))) == 0) {
+        message("Please define brownvegetation as a list including reflectance and Transmittance")
+        stop()
+      }
+      # check if spectral domain for optical properties of brown vegetation match
+      # with spectral domain for optical properties of green vegetation
+      if (length(setdiff(spec_sensor$lambda, brownvegetation$wvl)) > 0) {
+        message("Please define same spectral domain for brownvegetation and SpecPROSPECT")
+        stop()
+      }
+      if (length(unique(lengths(input_prospect))) == 1) {
+        if (!unique(lengths(input_prospect)) == 1) {
+          message("brownvegetation defined along with multiple leaf chemical properties")
+          message("Only first set of leaf chemical properties will be used to simulate green vegetation")
+        }
+      }
+    # if no leaf optical properties brown vegetation defined
+    } else if (is.null(brownvegetation)) {
+      # if all PROSPECT input parameters have the same length
+      if (length(unique(lengths(input_prospect))) == 1) {
+        # if all PROSPECT input parameters are unique (no possibility to simulate 2 types of leaf optics)
+        if (unique(lengths(input_prospect)) == 1) {
+          # if fraction_brown set to 0,  then assign green vegetation optics to brown vegetation optics
+          if (fraction_brown == 0) {
+            brownvegetation <- greenvegetation
+          # else run 4SAIL
+          } else {
+            message("4SAIL2 needs two sets of optical properties for green and brown vegetation")
+            message("Currently one set is defined. will run 4SAIL instead of 4SAIL2")
+            sailversion <- "4SAIL"
+          }
+        # if all PROSPECT parameters have at least 2 elements
+        } else if (unique(lengths(input_prospect)) >= 2) {
+          # compute leaf optical properties
+          brownvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor,
+                                                n = input_prospect$n[2],
+                                                chl = input_prospect$chl[2],
+                                                car = input_prospect$car[2],
+                                                ant = input_prospect$ant[2],
+                                                brown = input_prospect$brown[2],
+                                                ewt = input_prospect$ewt[2],
+                                                lma = input_prospect$lma[2],
+                                                prot = input_prospect$prot[2],
+                                                cbc = input_prospect$cbc[2],
+                                                alpha = input_prospect$alpha[2])
+          if (unique(lengths(input_prospect)) > 2) {
+            message("4SAIL2 needs two sets of optical properties for green and brown vegetation")
+            message("Currently more than 2 sets are defined. will only use the first 2")
+          }
+        }
+      }
+    }
+  }
+  if (sailversion  ==  "4SAIL") {
+    if (length(unique(lengths(input_prospect))) == 1) {
+      if (unique(lengths(input_prospect)) > 1) {
+        message("4SAIL needs only one set of optical properties")
+        message("Currently more than one set of leaf chemical constituents is defined.")
+        message("Will run 4SAIL with the first set of leaf chemical constituents")
+      }
+    }
+  }
+
+  if (sailversion  ==  "4SAIL") {
+    # run 4SAIL
+    ref <- foursail(leafoptics = greenvegetation,
+                    typelidf,  lidfa,  lidfb,  lai,  q,  tts,  tto,  psi,  rsoil)
+  } else if (sailversion  ==  "4SAIL2") {
+    # run 4SAIL2
+    ref <- foursail2(leafgreen = greenvegetation,  leafbrown = brownvegetation,
+                     typelidf,  lidfa,  lidfb,  lai,  q,  tts,  tto,  psi,  rsoil,
+                     fraction_brown,  diss,  cv,  zeta)
+  }
+  return(ref)
+}
+
+#" Performs PROSAIL simulation based on a set of combinations of input parameters
+#" @param leafoptics list. Includes leaf optical properties (reflectance and transmittance)
+#" and corresponding spectral bands
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param q numeric. Hot Spot parameter
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" @export
+
+foursail  <- function(leafoptics,  typelidf = 2,  lidfa = nULL,  lidfb = nULL,  lai = nULL,
+                      q = nULL,  tts = nULL,  tto = nULL,  psi = nULL,  rsoil = nULL) {
+
+  ############################## #
+  #	LEAF OPTICAL PROPERTIES	##
+  ############################## #
+  rho <- leafoptics$Reflectance
+  tau <- leafoptics$Transmittance
+
+  #	Geometric quantities
+  rd <- pi / 180
+  cts <- cos(rd * tts)
+  cto <- cos(rd * tto)
+  ctscto <- cts * cto
+  tants <- tan(rd * tts)
+  tanto <- tan(rd * tto)
+  cospsi <- cos(rd * psi)
+  dso <- sqrt(tants * tants + tanto * tanto - 2. * tants * tanto * cospsi)
+
+  #	Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters
+  if (typelidf == 1) {
+    foliar_distrib <- dladgen(lidfa, lidfb)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+
+  } else if (typelidf == 2) {
+    foliar_distrib <- campbell(lidfa)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+  }
+
+  # angular distance,  compensation of shadow length
+  #	Calculate geometric factors associated with extinction and scattering
+  #	Initialise sums
+  ks <- 0
+  ko <- 0
+  bf <- 0
+  sob <- 0
+  sof <- 0
+
+  #	Weighted sums over LIDF
+  na <- length(litab)
+  for (i in 1:na) {
+    ttl <- litab[i]	    # leaf inclination discrete values
+    ctl <- cos(rd * ttl)
+    #	SAIL volume scattering phase function gives interception and portions to be
+    #	multiplied by rho and tau
+    resvolscatt <- volscatt(tts, tto, psi, ttl)
+    chi_s <- resvolscatt$chi_s
+    chi_o <- resvolscatt$chi_o
+    frho <- resvolscatt$frho
+    ftau <- resvolscatt$ftau
+
+    #********************************************************************************
+    #*                   SUITS SYSTEM coEFFICIEnTS
+    #*
+    #*	ks  : Extinction coefficient for direct solar flux
+    #*	ko  : Extinction coefficient for direct observed flux
+    #*	att : Attenuation coefficient for diffuse flux
+    #*	sigb : Backscattering coefficient of the diffuse downward flux
+    #*	sigf : Forwardscattering coefficient of the diffuse upward flux
+    #*	sf  : Scattering coefficient of the direct solar flux for downward diffuse flux
+    #*	sb  : Scattering coefficient of the direct solar flux for upward diffuse flux
+    #*	vf   : Scattering coefficient of upward diffuse flux in the observed direction
+    #*	vb   : Scattering coefficient of downward diffuse flux in the observed direction
+    #*	w   : Bidirectional scattering coefficient
+    #********************************************************************************
+
+    #	Extinction coefficients
+    ksli <- chi_s / cts
+    koli <- chi_o / cto
+
+    #	Area scattering coefficient fractions
+    sobli <- frho * pi / ctscto
+    sofli <- ftau * pi / ctscto
+    bfli <- ctl * ctl
+    ks <- ks + ksli * lidf[i]
+    ko <- ko + koli * lidf[i]
+    bf <- bf + bfli * lidf[i]
+    sob <- sob + sobli * lidf[i]
+    sof <- sof + sofli * lidf[i]
+  }
+
+  #	Geometric factors to be used later with rho and tau
+  sdb <- 0.5 * (ks + bf)
+  sdf <- 0.5 * (ks - bf)
+  dob <- 0.5 * (ko + bf)
+  dof <- 0.5 * (ko - bf)
+  ddb <- 0.5 * (1. + bf)
+  ddf <- 0.5 * (1. - bf)
+
+  #	Here rho and tau come in
+  sigb <- ddb * rho + ddf * tau
+  sigf <- ddf * rho + ddb * tau
+  att <- 1 - sigf
+  m2 <- (att + sigb) * (att - sigb)
+  m2[which(m2 <= 0)] <- 0
+  m <- sqrt(m2)
+
+  sb <- sdb * rho + sdf * tau
+  sf <- sdf * rho + sdb * tau
+  vb <- dob * rho + dof * tau
+  vf <- dof * rho + dob * tau
+  w <- sob * rho + sof * tau
+
+  #	Here the LAI comes in
+  #   Outputs for the case LAI = 0
+  if (lai < 0) {
+    tss <- 1
+    too <- 1
+    tsstoo <- 1
+    rdd <- 0
+    tdd <- 1
+    rsd <- 0
+    tsd <- 0
+    rdo <- 0
+    tdo <- 0
+    rso <- 0
+    rsos <- 0
+    rsod <- 0
+
+    rddt <- rsoil
+    rsdt <- rsoil
+    rdot <- rsoil
+    rsodt <- 0 * rsoil
+    rsost <- rsoil
+    rsot <- rsoil
+  } else {
+    #	Other cases (LAI  >  0)
+    e1 <- exp(-m * lai)
+    e2 <- e1 * e1
+    rinf <- (att - m) / sigb
+    rinf2 <- rinf * rinf
+    re <- rinf * e1
+    denom <- 1. - rinf2 * e2
+
+    j1ks <- jfunc1(ks, m, lai)
+    j2ks <- jfunc2(ks, m, lai)
+    j1ko <- jfunc1(ko, m, lai)
+    j2ko <- jfunc2(ko, m, lai)
+
+    ps <- (sf + sb * rinf) * j1ks
+    qs <- (sf * rinf + sb) * j2ks
+    pv <- (vf + vb * rinf) * j1ko
+    qv <- (vf * rinf + vb) * j2ko
+
+    rdd <- rinf * (1. - e2) / denom
+    tdd <- (1. - rinf2) * e1 / denom
+    tsd <- (ps - re * qs) / denom
+    rsd <- (qs - re * ps) / denom
+    tdo <- (pv - re * qv) / denom
+    rdo <- (qv - re * pv) / denom
+
+    tss <- exp(-ks * lai)
+    too <- exp(-ko * lai)
+    z <- jfunc3(ks, ko, lai)
+    g1 <- (z - j1ks * too) / (ko + m)
+    g2 <- (z - j1ko * tss) / (ks + m)
+
+    tv1 <- (vf * rinf + vb) * g1
+    tv2 <- (vf + vb * rinf) * g2
+    t1 <- tv1 * (sf + sb * rinf)
+    t2 <- tv2 * (sf * rinf + sb)
+    t3 <- (rdo * qs + tdo * ps) * rinf
+
+    #	Multiple scattering contribution to bidirectional canopy reflectance
+    rsod <- (t1 + t2 - t3) / (1. - rinf2)
+
+    #	Treatment of the hotspot-effect
+    alf <- 1e6
+    #	Apply correction 2 / (K + k) suggested by F.-M. Breon
+    if (q > 0) {
+      alf <- (dso / q) * 2. / (ks + ko)
+    }
+    if (alf > 200) {
+      # inserted H. Bach 1 / 3 / 04
+      alf <- 200
+    }
+    if (alf == 0) {
+      #	The pure hotspot - no shadow
+      tsstoo <- tss
+      sumint <- (1 - tss) / (ks * lai)
+    } else {
+      #	Outside the hotspot
+      fhot <- lai * sqrt(ko * ks)
+      #	Integrate by exponential Simpson method in 20 steps
+      #	the steps are arranged according to equal partitioning
+      #	of the slope of the joint probability function
+      x1 <- 0
+      y1 <- 0
+      f1 <- 1
+      fint <- (1. - exp(-alf)) * 0.05
+      sumint <- 0
+      for (i in 1:20) {
+        if (i < 20) {
+          x2 <- -log(1. - i * fint) / alf
+        } else {
+          x2 <- 1
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1. - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        sumint <- sumint + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      tsstoo <- f1
+    }
+    #	Bidirectional reflectance
+    #	Single scattering contribution
+    rsos <- w * lai * sumint
+    #	Total canopy contribution
+    rso <- rsos + rsod
+    #	Interaction with the soil
+    dn <- 1. - rsoil * rdd
+    # rddt: bi-hemispherical reflectance factor
+    rddt <- rdd + tdd * rsoil * tdd / dn
+    # rsdt: directional-hemispherical reflectance factor for solar incident flux
+    rsdt <- rsd + (tsd + tss) * rsoil * tdd / dn
+    # rdot: hemispherical-directional reflectance factor in viewing direction
+    rdot <- rdo + tdd * rsoil * (tdo + too) / dn
+    # rsot: bi-directional reflectance factor
+    rsodt <- rsod + ((tss + tsd) * tdo + (tsd + tss * rsoil * rdd) * too) * rsoil / dn
+    rsost <- rsos + tsstoo * rsoil
+    rsot <- rsost + rsodt
+  }
+  my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt)
+  return(my_list)
+}
+
+#" Performs pro4sail2 simulation based on a set of combinations of input parameters
+#" @param leafgreen list. includes relfectance and transmittance for vegetation #1 (e.g. green vegetation)
+#" @param leafbrown list. includes relfectance and transmittance for vegetation #2 (e.g. brown vegetation)
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param hot numeric. Hot Spot parameter = ratio of the correlation length of leaf projections in the horizontal plane and the canopy height (doi:10.1016 / j.rse.2006.12.013)
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#" @param fraction_brown numeric. Fraction of brown leaf area
+#" @param diss numeric. Layer dissociation factor
+#" @param cv numeric. vertical crown cover percentage
+#" = % ground area covered with crowns as seen from nadir direction
+#" @param zeta numeric. Tree shape factor
+#" = ratio of crown diameter to crown height
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" alfast: canopy absorptance for direct solar incident flux
+#" alfadt: canopy absorptance for hemispherical diffuse incident flux
+#" @export
+
+foursail2  <- function(leafgreen,  leafbrown,
+                       typelidf = 2, lidfa = nULL, lidfb = nULL,
+                       lai = nULL,  hot = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL,
+                       fraction_brown = 0.5,  diss = 0.5,  cv = 1, zeta = 1) {
+
+  #	This version does not include non-Lambertian soil properties.
+  #	original codes do,  and only need to add the following variables as input
+  rddsoil <- rdosoil <- rsdsoil <- rsosoil <- rsoil
+
+  #	Geometric quantities
+  rd <- pi / 180
+
+  #	Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters
+  if (typelidf == 1) {
+    foliar_distrib <- dladgen(lidfa, lidfb)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+
+  } else if (typelidf == 2) {
+    foliar_distrib <- campbell(lidfa)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+  }
+
+  if (lai < 0) {
+    message("Please define positive LAI value")
+    rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil
+    alfast <- alfadt <- 0 * rsoil
+  } else if (lai == 0) {
+    tss <- too <- tsstoo <- tdd <- 1.0
+    rdd <- rsd <- tsd <- rdo <- tdo <- 0.0
+    rso <- rsos <- rsod <- rsodt <- 0.0
+    rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil
+    alfast <- alfadt <- 0 * rsoil
+  } else if (lai > 0) {
+    cts <- cos(rd * tts)
+    cto <- cos(rd * tto)
+    ctscto <- cts * cto
+    tants <- tan(rd * tts)
+    tanto <- tan(rd * tto)
+    cospsi <- cos(rd * psi)
+    dso <- sqrt(tants * tants + tanto * tanto - 2.0 * tants * tanto * cospsi)
+
+    # Clumping effects
+    cs <- co <- 1.0
+    if (cv <= 1.0) {
+      cs <- 1.0 - (1.0 - cv)^(1.0 / cts)
+      co <- 1.0 - (1.0 - cv)^(1.0 / cto)
+    }
+    overlap <- 0.0
+    if (zeta > 0.0) {
+      overlap <- min(cs * (1.0 - co), co * (1.0 - cs)) * exp(-dso / zeta)
+    }
+    fcd <- cs * co + overlap
+    fcs <- (1.0 - cs) * co - overlap
+    fod <- cs * (1.0 - co) - overlap
+    fos <- (1.0 - cs) * (1.0 - co) + overlap
+    fcdc <- 1.0 - (1.0 - fcd)^(0.5 / cts + 0.5 / cto)
+
+    #	Part depending on diss,  fraction_brown,  and leaf optical properties
+    #	First save the input fraction_brown as the old fraction_brown,  as the following change is only artificial
+    # Better define an fraction_brown that is actually used: fb,  so that the input is not modified!
+
+    fb <- fraction_brown
+    # if only green leaves
+    if (fraction_brown == 0.0) {
+      fb <- 0.5
+      leafbrown$Reflectance <- leafgreen$Reflectance
+      leafbrown$Transmittance <- leafgreen$Transmittance
+    }
+    if (fraction_brown == 1.0) {
+      fb <- 0.5
+      leafgreen$Reflectance <- leafbrown$Reflectance
+      leafgreen$Transmittance <- leafbrown$Transmittance
+    }
+    s <- (1.0 - diss) * fb * (1.0 - fb)
+    # rho1 && tau1 : green foliage
+    # rho2 && tau2 : brown foliage (bottom layer)
+    rho1 <- ((1 - fb - s) * leafgreen$Reflectance + s * leafbrown$Reflectance) / (1 - fb)
+    tau1 <- ((1 - fb - s) * leafgreen$Transmittance + s * leafbrown$Transmittance) / (1 - fb)
+    rho2 <- (s * leafgreen$Reflectance + (fb - s) * leafbrown$Reflectance) / fb
+    tau2 <- (s * leafgreen$Transmittance + (fb - s) * leafbrown$Transmittance) / fb
+
+    # angular distance,  compensation of shadow length
+    #	Calculate geometric factors associated with extinction and scattering
+    #	Initialise sums
+    ks <- ko <- bf <- sob <- sof <- 0
+
+    # Weighted sums over LIDF
+
+    for (i in 1:seq_along(litab)) {
+      ttl <- litab[i]
+      ctl <- cos(rd * ttl)
+      # SAIL volscatt function gives interception coefficients
+      # and two portions of the volume scattering phase function to be
+      # multiplied by rho and tau,  respectively
+      resvolscatt <- volscatt(tts, tto, psi, ttl)
+      chi_s <- resvolscatt$chi_s
+      chi_o <- resvolscatt$chi_o
+      frho <- resvolscatt$frho
+      ftau <- resvolscatt$ftau
+      # Extinction coefficients
+      ksli <- chi_s / cts
+      koli <- chi_o / cto
+      # Area scattering coefficient fractions
+      sobli <- frho * pi / ctscto
+      sofli <- ftau * pi / ctscto
+      bfli <- ctl * ctl
+      ks <- ks + ksli * lidf[i]
+      ko <- ko + koli * lidf[i]
+      bf <- bf + bfli * lidf[i]
+      sob <- sob + sobli * lidf[i]
+      sof <- sof + sofli * lidf[i]
+    }
+    # Geometric factors to be used later in combination with rho and tau
+    sdb <- 0.5 * (ks + bf)
+    sdf <- 0.5 * (ks - bf)
+    dob <- 0.5 * (ko + bf)
+    dof <- 0.5 * (ko - bf)
+    ddb <- 0.5 * (1. + bf)
+    ddf <- 0.5 * (1. - bf)
+
+    # LAIs in two layers
+    lai1 <- (1 - fb) * lai
+    lai2 <- fb * lai
+
+    tss <- exp(-ks * lai)
+    ck <- exp(-ks * lai1)
+    alf <- 1e6
+    if (hot > 0.0) {
+      alf <- (dso / hot) * 2.0 / (ks + ko)
+    }
+    if (alf > 200.0) {
+      alf <- 200.0     # inserted H. Bach 1 / 3 / 04
+    }
+    if (alf == 0.0) {
+      # The pure hotspot
+      tsstoo <- tss
+      s1 <- (1 - ck) / (ks * lai)
+      s2 <- (ck - tss) / (ks * lai)
+    } else {
+      # Outside the hotspot
+      fhot <- lai * sqrt(ko * ks)
+      # Integrate 2 layers by exponential simpson method in 20 steps
+      # the steps are arranged according to equal partitioning
+      # of the derivative of the joint probability function
+      x1 <- y1 <- 0.0
+      f1 <- 1.0
+      ca <- exp(alf * (fb - 1.0))
+      fint <- (1.0 - ca) * .05
+      s1 <- 0.0
+      for (istep in 1:20) {
+        if (istep < 20) {
+          x2 <- -log(1. - istep * fint) / alf
+        } else {
+          x2 <- 1. - fb
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        s1 <- s1 + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      fint <- (ca - exp(-alf)) * .05
+      s2 <- 0.0
+      for (istep in 1:20) {
+        if (istep < 20) {
+          x2 <- -log(ca - istep * fint) / alf
+        } else {
+          x2 <- 1.0
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        s2 <- s2 + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      tsstoo <- f1
+    }
+
+    # Calculate reflectances and transmittances
+    # Bottom layer
+    tss <- exp(-ks * lai2)
+    too <- exp(-ko * lai2)
+    sb <- sdb * rho2 + sdf * tau2
+    sf <- sdf * rho2 + sdb * tau2
+
+    vb <- dob * rho2 + dof * tau2
+    vf <- dof * rho2 + dob * tau2
+
+    w2 <- sob * rho2 + sof * tau2
+
+    sigb <- ddb * rho2 + ddf * tau2
+    sigf <- ddf * rho2 + ddb * tau2
+    att <- 1.0 - sigf
+    m2 <- (att + sigb) * (att - sigb)
+    m2[m2 < 0] <- 0
+    m <- sqrt(m2)
+    which_ncs <- which(m > 0.01)
+    which_cs <- which(m <= 0.01)
+
+    tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m
+    rsod <- 0 * m
+    if (length(which_ncs) > 0) {
+      resncs <- nonconservativescattering(m[which_ncs], lai2, att[which_ncs], sigb[which_ncs],
+                                          ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too)
+      tdd[which_ncs] <- resncs$tdd
+      rdd[which_ncs] <- resncs$rdd
+      tsd[which_ncs] <- resncs$tsd
+      rsd[which_ncs] <- resncs$rsd
+      tdo[which_ncs] <- resncs$tdo
+      rdo[which_ncs] <- resncs$rdo
+      rsod[which_ncs] <- resncs$rsod
+    }
+    if (length(which_cs) > 0) {
+      rescs <- conservativescattering(m[which_cs], lai2, att[which_cs], sigb[which_cs],
+                                      ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too)
+      tdd[which_cs] <- rescs$tdd
+      rdd[which_cs] <- rescs$rdd
+      tsd[which_cs] <- rescs$tsd
+      rsd[which_cs] <- rescs$rsd
+      tdo[which_cs] <- rescs$tdo
+      rdo[which_cs] <- rescs$rdo
+      rsod[which_cs] <- rescs$rsod
+    }
+
+    # Set background properties equal to those of the bottom layer on a black soil
+    rddb <- rdd
+    rsdb <- rsd
+    rdob <- rdo
+    rsodb <- rsod
+    tddb <- tdd
+    tsdb <- tsd
+    tdob <- tdo
+    toob <- too
+    tssb <- tss
+    # Top layer
+    tss <- exp(-ks * lai1)
+    too <- exp(-ko * lai1)
+
+    sb <- sdb * rho1 + sdf * tau1
+    sf <- sdf * rho1 + sdb * tau1
+
+    vb <- dob * rho1 + dof * tau1
+    vf <- dof * rho1 + dob * tau1
+
+    w1 <- sob * rho1 + sof * tau1
+
+    sigb <- ddb * rho1 + ddf * tau1
+    sigf <- ddf * rho1 + ddb * tau1
+    att <- 1.0 - sigf
+
+    m2 <- (att + sigb) * (att - sigb)
+    m2[m2 < 0] <- 0
+    m <- sqrt(m2)
+    which_ncs <- which(m > 0.01)
+    which_cs <- which(m <= 0.01)
+
+    tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m
+    rsod <- 0 * m
+    if (length(which_ncs) > 0) {
+      resncs <- nonconservativescattering(m[which_ncs], lai1, att[which_ncs], sigb[which_ncs],
+                                          ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too)
+      tdd[which_ncs] <- resncs$tdd
+      rdd[which_ncs] <- resncs$rdd
+      tsd[which_ncs] <- resncs$tsd
+      rsd[which_ncs] <- resncs$rsd
+      tdo[which_ncs] <- resncs$tdo
+      rdo[which_ncs] <- resncs$rdo
+      rsod[which_ncs] <- resncs$rsod
+    }
+    if (length(which_cs) > 0) {
+      rescs <- conservativescattering(m[which_cs], lai1, att[which_cs], sigb[which_cs],
+                                      ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too)
+      tdd[which_cs] <- rescs$tdd
+      rdd[which_cs] <- rescs$rdd
+      tsd[which_cs] <- rescs$tsd
+      rsd[which_cs] <- rescs$rsd
+      tdo[which_cs] <- rescs$tdo
+      rdo[which_cs] <- rescs$rdo
+      rsod[which_cs] <- rescs$rsod
+    }
+
+    # combine with bottom layer reflectances and transmittances (adding method)
+    rn <- 1.0 - rdd * rddb
+    tup <- (tss * rsdb + tsd * rddb) / rn
+    tdn <- (tsd + tss * rsdb * rdd) / rn
+    rsdt <- rsd + tup * tdd
+    rdot <- rdo + tdd * (rddb * tdo + rdob * too) / rn
+    rsodt <- rsod + (tss * rsodb + tdn * rdob) * too + tup * tdo
+
+    rsost <- (w1 * s1 + w2 * s2) * lai
+
+    rsot <- rsost + rsodt
+
+    # Diffuse reflectances at the top and the bottom are now different
+    rddt_t <- rdd + tdd * rddb * tdd / rn
+    rddt_b <- rddb + tddb * rdd * tddb / rn
+
+    # Transmittances of the combined canopy layers
+    tsst <- tss * tssb
+    toot <- too * toob
+    tsdt <- tss * tsdb + tdn * tddb
+    tdot <- tdob * too + tddb * (tdo + rdd * rdob * too) / rn
+    tddt <- tdd * tddb / rn
+
+    # Apply clumping effects to vegetation layer
+    rddcb <- cv * rddt_b
+    rddct <- cv * rddt_t
+    tddc <- 1 - cv + cv * tddt
+    rsdc <- cs * rsdt
+    tsdc <- cs * tsdt
+    rdoc <- co * rdot
+    tdoc <- co * tdot
+    tssc <- 1 - cs + cs * tsst
+    tooc <- 1 - co + co * toot
+
+    # new weight function fcdc for crown contribution (W. Verhoef,  22-05-08)
+    rsoc <- fcdc * rsot
+    tssooc <- fcd * tsstoo + fcs * toot + fod * tsst + fos
+    # Canopy absorptance for black background (W. Verhoef,  02-03-04)
+    alfas <- 1. - tssc - tsdc - rsdc
+    alfad <- 1. - tddc - rddct
+    # Add the soil background
+    rn <- 1 - rddcb * rddsoil
+    tup <- (tssc * rsdsoil + tsdc * rddsoil) / rn
+    tdn <- (tsdc + tssc * rsdsoil * rddcb) / rn
+
+    rddt <- rddct + tddc * rddsoil * tddc / rn
+    rsdt <- rsdc + tup * tddc
+    rdot <- rdoc + tddc * (rddsoil * tdoc + rdosoil * tooc) / rn
+    rsot <- rsoc + tssooc * rsosoil + tdn * rdosoil * tooc + tup * tdoc
+
+    # Effect of soil background on canopy absorptances (W. Verhoef,  02-03-04)
+    alfast <- alfas + tup * alfad
+    alfadt <- alfad * (1. + tddc * rddsoil / rn)
+  }
+  my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt,
+                  "alfast" = alfast,  "alfadt" = alfadt)
+  return(my_list)
+}
+
+
+
+#" computes non conservative scattering conditions
+#" @param m numeric.
+#" @param lai numeric. Leaf Area Index
+#" @param att numeric.
+#" @param sigb numeric.
+#" @param ks numeric.
+#" @param ko numeric.
+#" @param sf numeric.
+#" @param sb numeric.
+#" @param vf numeric.
+#" @param vb numeric.
+#" @param tss numeric.
+#" @param too numeric.
+#"
+#" @return list. tdd,  rdd,  tsd,  rsd,  tdo,  rdo,  rsod
+#"
+#" @export
+nonconservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) {
+
+  e1 <- exp(-m * lai)
+  e2 <- e1 * e1
+  rinf <- (att - m) / sigb
+  rinf2 <- rinf * rinf
+  re <- rinf * e1
+  denom <- 1. - rinf2 * e2
+
+  j1ks <- jfunc1(ks, m, lai)
+  j2ks <- jfunc2(ks, m, lai)
+  j1ko <- jfunc1(ko, m, lai)
+  j2ko <- jfunc2(ko, m, lai)
+
+  ps <- (sf + sb * rinf) * j1ks
+  qs <- (sf * rinf + sb) * j2ks
+  pv <- (vf + vb * rinf) * j1ko
+  qv <- (vf * rinf + vb) * j2ko
+
+  tdd <- (1. - rinf2) * e1 / denom
+  rdd <- rinf * (1. - e2) / denom
+  tsd <- (ps - re * qs) / denom
+  rsd <- (qs - re * ps) / denom
+  tdo <- (pv - re * qv) / denom
+  rdo <- (qv - re * pv) / denom
+
+  z <- jfunc2(ks, ko, lai)
+  g1 <- (z - j1ks * too) / (ko + m)
+  g2 <- (z - j1ko * tss) / (ks + m)
+
+  tv1 <- (vf * rinf + vb) * g1
+  tv2 <- (vf + vb * rinf) * g2
+
+  t1 <- tv1 * (sf + sb * rinf)
+  t2 <- tv2 * (sf * rinf + sb)
+  t3 <- (rdo * qs + tdo * ps) * rinf
+
+  # Multiple scattering contribution to bidirectional canopy reflectance
+  rsod <- (t1 + t2 - t3) / (1. - rinf2)
+  my_list <- list("tdd" = tdd,  "rdd" = rdd,  "tsd" = tsd,
+                  "rsd" = rsd,  "tdo" = tdo,  "rdo" = rdo,
+                  "rsod" = rsod)
+  return(my_list)
+}
+
+#" computes conservative scattering conditions
+#" @param m numeric.
+#" @param lai numeric. Leaf Area Index
+#" @param att numeric.
+#" @param sigb numeric.
+#" @param ks numeric.
+#" @param ko numeric.
+#" @param sf numeric.
+#" @param sb numeric.
+#" @param vf numeric.
+#" @param vb numeric.
+#" @param tss numeric.
+#" @param too numeric.
+#"
+#" @return list. tdd,  rdd,  tsd,  rsd,  tdo,  rdo,  rsod
+#"
+#" @export
+conservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) {
+
+  # near or complete conservative scattering
+  j4 <- jfunc4(m, lai)
+  amsig <- att - sigb
+  apsig <- att + sigb
+  rtp <- (1 - amsig * j4) / (1 + amsig * j4)
+  rtm <- (-1 + apsig * j4) / (1 + apsig * j4)
+  rdd <- 0.5 * (rtp + rtm)
+  tdd <- 0.5 * (rtp - rtm)
+
+  dns <- ks * ks - m * m
+  dno <- ko * ko - m * m
+  cks <- (sb * (ks - att) - sf * sigb) / dns
+  cko <- (vb * (ko - att) - vf * sigb) / dno
+  dks <- (-sf * (ks + att) - sb * sigb) / dns
+  dko <- (-vf * (ko + att) - vb * sigb) / dno
+  ho <- (sf * cko + sb * dko) / (ko + ks)
+
+  rsd <- cks * (1 - tss * tdd) - dks * rdd
+  rdo <- cko * (1 - too * tdd) - dko * rdd
+  tsd <- dks * (tss - tdd) - cks * tss * rdd
+  tdo <- dko * (too - tdd) - cko * too * rdd
+  # Multiple scattering contribution to bidirectional canopy reflectance
+  rsod <- ho * (1 - tss * too) - cko * tsd * too - dko * rsd
+
+  my_list <- list("tdd" = tdd,  "rdd" = rdd,  "tsd" = tsd,
+                  "rsd" = rsd,  "tdo" = tdo,  "rdo" = rdo,
+                  "rsod" = rsod)
+  return(my_list)
+}
+
+
+
+
+
+
+#" computes the leaf angle distribution function value (freq)
+#"
+#" Ellipsoidal distribution function characterised by the average leaf
+#" inclination angle in degree (ala)
+#" Campbell 1986
+#" @param ala average leaf angle
+#" @return foliar_distrib list. lidf and litab
+#" @export
+campbell  <- function(ala) {
+
+  tx1 <- c(10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88., 90.)
+  tx2 <- c(0., 10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88.)
+  litab <- (tx2 + tx1) / 2
+  n <- length(litab)
+  tl1 <- tx1 * (pi / 180)
+  tl2 <- tx2 * (pi / 180)
+  excent <- exp(-1.6184e-5 * ala**3 + 2.1145e-3 * ala**2 - 1.2390e-1 * ala + 3.2491)
+  sum0 <- 0
+
+  freq <- c()
+  for (i in 1:n) {
+    x1 <- excent / (sqrt(1. + excent**2. * tan(tl1[i])**2))
+    x2 <- excent / (sqrt(1. + excent**2. * tan(tl2[i])**2))
+    if (excent == 1) {
+      freq[i] <- abs(cos(tl1[i]) - cos(tl2[i]))
+    } else {
+      alpha <- excent / sqrt(abs(1 - excent**2))
+      alpha2 <- alpha**2
+      x12 <- x1**2
+      x22 <- x2**2
+      alpx1 <- 0 * alpha2
+      alpx2 <- 0 * alpha2
+      almx1 <- 0 * alpha2
+      almx2 <- 0 * alpha2
+      if (excent > 1) {
+        alpx1 <- sqrt(alpha2[excent > 1] + x12[excent > 1])
+        alpx2[excent > 1] <- sqrt(alpha2[excent > 1] + x22[excent > 1])
+        dum <- x1 * alpx1 + alpha2 * log(x1 + alpx1)
+        freq[i] <- abs(dum - (x2 * alpx2 + alpha2 * log(x2 + alpx2)))
+      } else {
+        almx1 <- sqrt(alpha2 - x12)
+        almx2 <- sqrt(alpha2 - x22)
+        dum <- x1 * almx1 + alpha2 * asin(x1 / alpha)
+        freq[i] <- abs(dum - (x2 * almx2 + alpha2 * asin(x2 / alpha)))
+      }
+    }
+  }
+  sum0 <- sum(freq)
+  freq0 <- freq / sum0
+  foliar_distrib <- list("lidf" = freq0, "litab" = litab)
+  return(foliar_distrib)
+}
+
+#" computes the leaf angle distribution function value (freq)
+#"
+#" Using the original bimodal distribution function initially proposed in SAIL
+#"  references
+#"  ----------
+#"  (Verhoef1998) Verhoef,  Wout. Theory of radiative transfer models applied
+#"  in optical remote sensing of vegetation canopies.
+#"  nationaal Lucht en Ruimtevaartlaboratorium,  1998.
+#"  http: /  / library.wur.nl / WebQuery / clc / 945481.
+#" @param a controls the average leaf slope
+#" @param b controls the distribution"s bimodality
+#" LIDF type 		  a 		b
+#" Planophile 	  1		  0
+#" Erectophile    -1	 	0
+#" Plagiophile 	  0		  -1
+#" Extremophile 	0		  1
+#" Spherical 	    -0.35 -0.15
+#" Uniform        0     0
+#" requirement: ||lidfa||  +  ||lidfb|| < 1
+#"
+#" @return foliar_distrib list. lidf and litab
+#" @export
+dladgen  <- function(a, b) {
+  litab <- c(5., 15., 25., 35., 45., 55., 65., 75., 81., 83., 85., 87., 89.)
+  freq <- c()
+  for (i1 in 1:8) {
+    t <- i1 * 10
+    freq[i1] <- dcum(a, b, t)
+  }
+  for (i2 in 9:12) {
+    t <- 80. + (i2 - 8) * 2.
+    freq[i2] <- dcum(a, b, t)
+  }
+  freq[13] <- 1
+  for (i in 13:2) {
+    freq[i] <- freq[i] - freq[i - 1]
+  }
+  foliar_distrib <- list("lidf" = freq, "litab" = litab)
+  return(foliar_distrib)
+}
+
+#" dcum function
+#" @param a numeric. controls the average leaf slope
+#" @param b numeric. controls the distribution"s bimodality
+#" @param t numeric. angle
+#" @return f
+#" @export
+dcum <- function(a, b, t) {
+  rd <- pi / 180
+  if (a >= 1) {
+    f <- 1 - cos(rd * t)
+  } else {
+    eps <- 1e-8
+    delx <- 1
+    x <- 2 * rd * t
+    p <- x
+    while (delx  >= eps) {
+      y <- a * sin(x) + .5 * b * sin(2. * x)
+      dx <- .5 * (y - x + p)
+      x <- x + dx
+      delx <- abs(dx)
+    }
+    f <- (2. * y + p) / pi
+  }
+  return(f)
+}
+
+#" J1 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc1 <- function(k, l, t) {
+  # J1 function with avoidance of singularity problem
+  del <- (k - l) * t
+  jout <- 0 * l
+  jout[which(abs(del) > 1e-3)] <- (exp(-l[which(abs(del) > 1e-3)] * t) - exp(-k * t)) / (k - l[which(abs(del) > 1e-3)])
+  jout[which(abs(del) <= 1e-3)] <- 0.5 * t * (exp(-k * t) + exp(-l[which(abs(del) <= 1e-3)] * t)) * (1 - del[which(abs(del) <= 1e-3)] * del[which(abs(del) <= 1e-3)] / 12)
+  return(jout)
+}
+
+#" J2 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc2 <- function(k, l, t) {
+  #	J2 function
+  jout <- (1. - exp(-(k + l) * t)) / (k + l)
+  return(jout)
+}
+
+#" J3 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc3 <- function(k, l, t) {
+  out <- (1. - exp(-(k + l) * t)) / (k + l)
+  return(out)
+}
+
+
+#" j4 function for treating (near) conservative scattering
+#"
+#" @param m numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc4 <- function(m, t) {
+
+  del <- m * t
+  out <- 0 * del
+  out[del > 1e-3] <- (1 - exp(-del)) / (m * (1 + exp(-del)))
+  out[del <= 1e-3] <- 0.5 * t * (1. - del * del / 12.)
+  return(out)
+}
+
+
+#" compute volume scattering functions and interception coefficients
+#" for given solar zenith,  viewing zenith,  azimuth and leaf inclination angle.
+#"
+#" @param tts numeric. solar zenith
+#" @param tto numeric. viewing zenith
+#" @param psi numeric. azimuth
+#" @param ttl numeric. leaf inclination angle
+#" @return res list. includes chi_s,  chi_o,  frho,  ftau
+#" @export
+volscatt  <- function(tts, tto, psi, ttl) {
+  #********************************************************************************
+  #*	chi_s	= interception functions
+  #*	chi_o	= interception functions
+  #*	frho	= function to be multiplied by leaf reflectance rho
+  #*	ftau	= functions to be multiplied by leaf transmittance tau
+  #********************************************************************************
+  #	Wout Verhoef,  april 2001,  for CROMA
+
+  rd <- pi / 180
+  costs <- cos(rd * tts)
+  costo <- cos(rd * tto)
+  sints <- sin(rd * tts)
+  sinto <- sin(rd * tto)
+  cospsi <- cos(rd * psi)
+  psir <- rd * psi
+  costl <- cos(rd * ttl)
+  sintl <- sin(rd * ttl)
+  cs <- costl * costs
+  co <- costl * costo
+  ss <- sintl * sints
+  so <- sintl * sinto
+
+  #c ..............................................................................
+  #c     betas -bts- and betao -bto- computation
+  #c     Transition angles (beta) for solar (betas) and view (betao) directions
+  #c     if thetav + thetal > pi / 2,  bottom side of the leaves is observed for leaf azimut
+  #c     interval betao + phi<leaf azimut<2pi-betao + phi.
+  #c     if thetav + thetal<pi / 2,  top side of the leaves is always observed,  betao=pi
+  #c     same consideration for solar direction to compute betas
+  #c ..............................................................................
+
+  cosbts <- 5
+  if (abs(ss) > 1e-6) {
+    cosbts <- -cs / ss
+  }
+  cosbto <- 5
+  if (abs(so) > 1e-6) {
+    cosbto <- -co / so
+  }
+
+  if (abs(cosbts) < 1) {
+    bts <- acos(cosbts)
+    ds <- ss
+  } else {
+    bts <- pi
+    ds <- cs
+  }
+  chi_s <- 2. / pi * ((bts - pi * .5) * cs + sin(bts) * ss)
+  if (abs(cosbto) < 1) {
+    bto <- acos(cosbto)
+    doo <- so
+  } else if (tto < 90) {
+    bto <- pi
+    doo <- co
+  } else {
+    bto <- 0
+    doo <- -co
+  }
+  chi_o <- 2. / pi * ((bto - pi * .5) * co + sin(bto) * so)
+
+  #c ..............................................................................
+  #c   computation of auxiliary azimut angles bt1,  bt2,  bt3 used
+  #c   for the computation of the bidirectional scattering coefficient w
+  #c .............................................................................
+
+  btran1 <- abs(bts - bto)
+  btran2 <- pi - abs(bts + bto - pi)
+
+  if (psir <= btran1) {
+    bt1 <- psir
+    bt2 <- btran1
+    bt3 <- btran2
+  } else {
+    bt1 <- btran1
+    if (psir <= btran2) {
+      bt2 <- psir
+      bt3 <- btran2
+    } else {
+      bt2 <- btran2
+      bt3 <- psir
+    }
+  }
+  t1 <- 2. * cs * co + ss * so * cospsi
+  t2 <- 0
+  if (bt2 > 0) {
+    t2 <- sin(bt2) * (2. * ds * doo + ss * so * cos(bt1) * cos(bt3))
+  }
+
+  denom <- 2. * pi * pi
+  frho <- ((pi - bt2) * t1 + t2) / denom
+  ftau <- (-bt2 * t1 + t2) / denom
+
+  if (frho < 0) {
+    frho <- 0
+  }
+  if (ftau < 0) {
+    ftau <- 0
+  }
+  res <- list("chi_s" = chi_s, "chi_o" = chi_o, "frho" = frho, "ftau" = ftau)
+  return(res)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_PROSAIL_HybridInversion.R	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,554 @@
+# ============================================================================= =
+# prosail
+# Lib_PROSAIL_HybridInversion.R
+# ============================================================================= =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Florian de BOISSIEU <fdeboiss@gmail.com>
+# Copyright 2019 / 11 Jean-Baptiste FERET
+# ============================================================================= =
+# This Library includes functions dedicated to PROSAIL inversion using hybrid
+# approach based on SVM regression
+# ============================================================================= =
+
+
+#" This function applies SVR model on raster data in order to estimate
+#" vegetation biophysical properties
+#"
+#" @param raster_path character. path for a raster file
+#" @param hybridmodel list. hybrid models produced from train_prosail_inversion
+#" each element of the list corresponds to a set of hybrid models for a vegetation parameter
+#" @param pathout character. path for directory where results are written
+#" @param selectedbands list. list of spectral bands to be selected from raster (identified by name of vegetation parameter)
+#" @param bandname character. spectral bands corresponding to the raster
+#" @param maskraster character. path for binary mask defining ON (1) and OFF (0) pixels in the raster
+#" @param multiplyingfactor numeric. multiplying factor used to write reflectance in the raster
+#" --> PROSAIL simulates reflectance between 0 and 1,  and raster data expected in the same range
+#"
+#" @return None
+#" @importFrom progress progress_bar
+#" @importFrom stars read_stars
+#" @importFrom raster raster brick blockSize readStart readStop getValues writeStart writeStop writeValues
+#" @import rgdal
+#" @export
+apply_prosail_inversion <- function(raster_path,  hybridmodel,  pathout,
+                                    selectedbands,  bandname,
+                                    maskraster = FALSE, multiplyingfactor = 10000) {
+
+  # explain which biophysical variables will be computed
+  bpvar <- names(hybridmodel)
+  print("The following biophysical variables will be computed")
+  print(bpvar)
+
+  # get image dimensions
+  if (attr(rgdal::GDALinfo(raster_path, returnStats = FALSE),  "driver") == "ENVI") {
+    hdr <- read_envi_header(get_hdr_name(raster_path))
+    dimsraster <- list("rows" = hdr$lines, "cols" = hdr$samples, "bands" = hdr$bands)
+  } else {
+    dimsraster <- dim(read_stars(raster_path))
+    dimsraster <- list("rows" = as.numeric(dimsraster[2]), "cols" = as.numeric(dimsraster[1]), "bands" = as.numeric(dimsraster[3]))
+  }
+
+  # Produce a map for each biophysical property
+  for (parm in bpvar) {
+    print(paste("Computing", parm, sep = " "))
+    # read by chunk to avoid memory problem
+    blk <- blockSize(brick(raster_path))
+    # reflectance file
+    r_in <- readStart(brick(raster_path))
+    # mask file
+    r_inmask <- FALSE
+    if (maskraster == FALSE) {
+      selectpixels <- "ALL"
+    } else if (!maskraster == FALSE) {
+      if (file.exists(maskraster)) {
+        r_inmask <- readStart(raster(maskraster))
+      } else if (!file.exists(maskraster)) {
+        message("WARNING: Mask file does not exist:")
+        print(maskraster)
+        message("Processing all image")
+        selectpixels <- "ALL"
+      }
+    }
+    # initiate progress bar
+    pgbarlength <- length(hybridmodel[[parm]]) * blk$n
+    pb <- progress_bar$new(
+      format = "Hybrid inversion on raster [:bar] :percent in :elapsedfull, estimated time remaining :eta",
+      total = pgbarlength,  clear = FALSE,  width = 100)
+
+    # output files
+    bpvarpath <- file.path(pathout, paste(basename(raster_path), parm, sep = "_"))
+    bpvarsdpath <- file.path(pathout, paste(basename(raster_path), parm, "STD", sep = "_"))
+    r_outmean <- writeStart(raster(raster_path),  filename = bpvarpath, format = "ENVI",  overwrite = TRUE)
+    r_outsd <- writeStart(raster(raster_path),  filename = bpvarsdpath, format = "ENVI",  overwrite = TRUE)
+    selbands <- match(selectedbands[[parm]], bandname)
+
+    # loop over blocks
+    for (i in seq_along(blk$row)) {
+      # read values for block
+      # format is a matrix with rows the cells values and columns the layers
+      blockval <- getValues(r_in,  row = blk$row[i],  nrows = blk$nrows[i])
+      fulllength <- dim(blockval)[1]
+
+      if (typeof(r_inmask) == "logical") {
+        blockval <- blockval[, selbands]
+        # automatically filter pixels corresponding to negative values
+        selectpixels <- which(blockval[, 1] > 0)
+        blockval <- blockval[selectpixels, ]
+      } else if (typeof(r_inmask) == "S4") {
+        maskval <- getValues(r_inmask,  row = blk$row[i],  nrows = blk$nrows[i])
+        selectpixels <- which(maskval  == 1)
+        blockval <- blockval[selectpixels, selbands]
+      }
+      mean_estimatefull <- NA * vector(length = fulllength)
+      std_estimatefull <- NA * vector(length = fulllength)
+      if (length(selectpixels) > 0) {
+        blockval <- blockval / multiplyingfactor
+        modelsvr_estimate <- list()
+        for (modind in 1:seq_along(hybridmodel[[parm]])) {
+          pb$tick()
+          modelsvr_estimate[[modind]] <- predict(hybridmodel[[parm]][[modind]],  blockval)
+        }
+        modelsvr_estimate <- do.call(cbind, modelsvr_estimate)
+        # final estimated value = mean parm value for all models
+        mean_estimate <- rowMeans(modelsvr_estimate)
+        # "uncertainty" = STD value for all models
+        std_estimate <- rowSds(modelsvr_estimate)
+        mean_estimatefull[selectpixels] <- mean_estimate
+        std_estimatefull[selectpixels] <- std_estimate
+      } else {
+        for (modind in 1:seq_along(hybridmodel[[parm]])) {
+          pb$tick()
+        }
+      }
+      r_outmean <- writeValues(r_outmean,  mean_estimatefull,  blk$row[i], format = "ENVI",  overwrite = TRUE)
+      r_outsd <- writeValues(r_outsd,  std_estimatefull,  blk$row[i], format = "ENVI",  overwrite = TRUE)
+    }
+    # close files
+    r_in <- readStop(r_in)
+    if (typeof(r_inmask) == "S4") {
+      r_inmask <- readStop(r_inmask)
+    }
+    r_outmean <- writeStop(r_outmean)
+    r_outsd <- writeStop(r_outsd)
+    # write biophysical variable name in headers
+    hdr <- read_envi_header(get_hdr_name(bpvarpath))
+    hdr$`band names` <- paste("{", parm, "}", sep = "")
+    write_envi_header(hdr,  get_hdr_name(bpvarpath))
+  }
+  print("processing completed")
+  return(invisible())
+}
+
+#" get hdr name from image file name,  assuming it is BIL format
+#"
+#" @param impath path of the image
+#"
+#" @return corresponding hdr
+#" @importFrom tools file_ext file_path_sans_ext
+#" @export
+get_hdr_name <- function(impath) {
+  if (tools::file_ext(impath)  ==  "") {
+    impathhdr <- paste(impath,  ".hdr",  sep = "")
+  } else if (tools::file_ext(impath)  ==  "bil") {
+    impathhdr <- gsub(".bil",  ".hdr",  impath)
+  } else if (tools::file_ext(impath)  ==  "zip") {
+    impathhdr <- gsub(".zip",  ".hdr",  impath)
+  } else {
+    impathhdr <- paste(tools::file_path_sans_ext(impath),  ".hdr",  sep = "")
+  }
+
+  if (!file.exists(impathhdr)) {
+    message("WARNING : COULD NOT FIND hdr FILE")
+    print(impathhdr)
+    message("Process may stop")
+  }
+  return(impathhdr)
+}
+
+#" This function applies the regression models trained with prosail_hybrid_train
+#"
+#" @param regressionmodels list. List of regression models produced by prosail_hybrid_train
+#" @param refl numeric. LUT of bidirectional reflectances factors used for training
+#"
+#" @return hybridres list. Estimated values corresponding to refl. Includes
+#" - meanestimate = mean value for the ensemble regression model
+#" - stdestimate = std value for the ensemble regression model
+#" @importFrom stats predict
+#" @importFrom matrixStats rowSds
+#" @importFrom progress progress_bar
+#" @export
+
+prosail_hybrid_apply <- function(regressionmodels, refl) {
+
+  # make sure refl is right dimensions
+  refl <- t(refl)
+  nbfeatures <- regressionmodels[[1]]$dim
+  if (!ncol(refl) == nbfeatures && nrow(refl) == nbfeatures) {
+    refl <- t(refl)
+  }
+  nbensemble <- length(regressionmodels)
+  estimatedval <- list()
+  pb <- progress_bar$new(
+    format = "Applying SVR models [:bar] :percent in :elapsed",
+    total = nbensemble,  clear = FALSE,  width = 100)
+  for (i in 1:nbensemble) {
+    pb$tick()
+    estimatedval[[i]] <- predict(regressionmodels[[i]],  refl)
+  }
+  estimatedval <- do.call(cbind, estimatedval)
+  meanestimate <- rowMeans(estimatedval)
+  stdestimate <- rowSds(estimatedval)
+  hybridres <- list("meanestimate" = meanestimate, "stdestimate" = stdestimate)
+  return(hybridres)
+}
+
+#" This function trains a suppot vector regression for a set of variables based on spectral data
+#"
+#" @param brf_lut numeric. LUT of bidirectional reflectances factors used for training
+#" @param inputvar numeric. biophysical parameter corresponding to the reflectance
+#" @param figplot Boolean. Set to TRUE if you want a scatterplot
+#" @param nbensemble numeric. Number of individual subsets should be generated from brf_lut
+#" @param withreplacement Boolean. should subsets be generated with or without replacement?
+#"
+#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut
+#" @importFrom liquidSVM svmRegression
+#" @importFrom stats predict
+#" @importFrom progress progress_bar
+#" @importFrom graphics par
+#" @importFrom expandFunctions reset.warnings
+#" @importFrom stringr str_split
+#" @importFrom simsalapar tryCatch.W.E
+#" @import dplyr
+#" @import ggplot2
+# @" @import caret
+#" @export
+
+prosail_hybrid_train <- function(brf_lut, inputvar, figplot = FALSE, nbensemble = 20, withreplacement = FALSE) {
+
+  x <- y <- ymean <- ystdmin <- ystdmax <- NULL
+  # split the LUT into nbensemble subsets
+  nbsamples <- length(inputvar)
+  if (dim(brf_lut)[2] == nbsamples) {
+    brf_lut <- t(brf_lut)
+  }
+
+  # if subsets are generated from brf_lut with replacement
+  if (withreplacement == TRUE) {
+    subsets <- list()
+    samples_per_run <- round(nbsamples / nbensemble)
+    for (run in (1:nbensemble)) {
+      subsets[[run]] <- sample(seq(1, nbsamples),  samples_per_run,  replace = TRUE)
+    }
+  # if subsets are generated from brf_lut without replacement
+  } else if (withreplacement == FALSE) {
+    subsets <- split(sample(seq(1, nbsamples, by = 1)), seq(1, nbensemble, by = 1))
+  }
+
+  # run training for each subset
+  modelssvr <- list()
+  predictedyall <- list()
+  tunedmodelyall <- list()
+  pb <- progress_bar$new(
+    format = "Training SVR on subsets [:bar] :percent in :elapsed",
+    total = nbensemble,  clear = FALSE,  width = 100)
+  for (i in 1:nbensemble) {
+    pb$tick()
+    Sys.sleep(1  /  100)
+    trainingset <- list()
+    trainingset$X <- brf_lut[subsets[i][[1]], ]
+    trainingset$Y <- inputvar[subsets[i][[1]]]
+    # liquidSVM
+    r1 <- tryCatch.W.E(tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y))
+    if (!is.null(r1$warning)) {
+      msg <- r1$warning$message
+      valgamma <- str_split(string = msg, pattern = "gamma=")[[1]][2]
+      vallambda <- str_split(string = msg, pattern = "lambda=")[[1]][2]
+      if (!is.na(as.numeric(valgamma))) {
+        message("Adjusting Gamma accordingly")
+        valgamma <- as.numeric(valgamma)
+        tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y, min_gamma = valgamma)
+      }
+      if (!is.na(as.numeric(vallambda))) {
+        message("Adjusting Lambda accordingly")
+        vallambda <- as.numeric(vallambda)
+        tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y, min_lambda = vallambda)
+      }
+    }
+    modelssvr[[i]] <- tunedmodel
+  }
+
+  # if scatterplots needed
+  if (figplot == TRUE) {
+    # predict for full brf_lut
+    for (i in 1:nbensemble) {
+      tunedmodely <- stats::predict(modelssvr[[i]],  brf_lut)
+      tunedmodelyall <- cbind(tunedmodelyall, matrix(tunedmodely, ncol = 1))
+    }
+    # plot prediction
+    df <- data.frame(x = rep(1:nbsamples, nbensemble),  y = as.numeric(matrix(tunedmodelyall, ncol = 1)))
+    df_summary <- df %>%
+     dplyr::group_by(x) %>%
+      summarize(ymin = min(y), ystdmin = mean(y) - sd(y),
+                 ymax = max(y), ystdmax = mean(y) + sd(y),
+                 ymean = mean(y))
+    par(mar = rep(.1,  4))
+    p <- ggplot(df_summary,  aes(x = inputvar,  y = ymean))  +
+      geom_point(size = 2)  +
+      geom_errorbar(aes(ymin = ystdmin,  ymax = ystdmax))
+    meanpredict <- rowMeans(matrix(as.numeric(tunedmodelyall), ncol = nbensemble))
+    print(p)
+  }
+  return(modelssvr)
+}
+
+#" Reads ENVI hdr file
+#"
+#" @param hdrpath Path of the hdr file
+#"
+#" @return list of the content of the hdr file
+#" @export
+read_envi_header <- function(hdrpath) {
+  if (!grepl(".hdr$",  hdrpath)) {
+    stop("File extension should be .hdr")
+  }
+  hdr <- readLines(hdrpath)
+  ## check ENVI at beginning of file
+  if (!grepl("ENVI",  hdr[1])) {
+    stop("Not an ENVI header (ENVI keyword missing)")
+  } else {
+    hdr <- hdr [-1]
+  }
+  ## remove curly braces and put multi-line key-value-pairs into one line
+  hdr <- gsub("\\{([^}]*)\\}",  "\\1",  hdr)
+  l <- grep("\\{",  hdr)
+  r <- grep("\\}",  hdr)
+
+  if (length(l) != length(r)) {
+    stop("Error matching curly braces in header (differing numbers).")
+  }
+
+  if (any(r <= l)) {
+    stop("Mismatch of curly braces in header.")
+  }
+
+  hdr[l] <- sub("\\{",  "",  hdr[l])
+  hdr[r] <- sub("\\}",  "",  hdr[r])
+
+  for (i in rev(seq_along(l))) {
+    hdr <- c(
+      hdr [seq_len(l [i] - 1)],
+      paste(hdr [l [i]:r [i]],  collapse = "\n"),
+      hdr [-seq_len(r [i])]
+    )
+  }
+
+  ## split key = value constructs into list with keys as names
+  hdr <- sapply(hdr,  split_line,  " = ",  USE.NAMES = FALSE)
+  names(hdr) <- tolower(names(hdr))
+
+  ## process numeric values
+  tmp <- names(hdr) %in% c(
+    "samples",  "lines",  "bands",  "header offset",  "data type",
+    "byte order",  "default bands",  "data ignore value",
+    "wavelength",  "fwhm",  "data gain values"
+  )
+  hdr [tmp] <- lapply(hdr [tmp],  function(x) {
+    as.numeric(unlist(strsplit(x,  ",")))
+  })
+
+  return(hdr)
+}
+
+#" ENVI functions
+#"
+#" based on https: /  / github.com / cran / hyperSpec / blob / master / R / read.ENVI.R
+#" added wavelength,  fwhm,  ... to header reading
+#" Title
+#"
+#" @param x character.
+#" @param separator character
+#" @param trim_blank  boolean.
+#"
+#" @return list.
+#" @export
+split_line <- function(x,  separator,  trim_blank  = TRUE) {
+  tmp <- regexpr(separator,  x)
+  key <- substr(x,  1,  tmp - 1)
+  value <- substr(x,  tmp  +  1,  nchar(x))
+  if (trim_blank) {
+    blank_pattern <- "^[[:blank:]]*([^[:blank:]] + .*[^[:blank:]] + )[[:blank:]]*$"
+    key <- sub(blank_pattern,  "\\1",  key)
+    value <- sub(blank_pattern,  "\\1",  value)
+  }
+  value <- as.list(value)
+  names(value) <- key
+  return(value)
+}
+
+#" This function performs full training for hybrid invrsion using SVR with
+#" values for default parameters
+#"
+#" @param minval list. minimum value for input parameters sampled to produce a training LUT
+#" @param maxval list. maximum value for input parameters sampled to produce a training LUT
+#" @param typedistrib  list. Type of distribution. Either "Uniform" or "Gaussian"
+#" @param gaussiandistrib  list. Mean value and STD corresponding to the parameters sampled with gaussian distribution
+#" @param parmset list. list of input parameters set to a specific value
+#" @param nbsamples numeric. number of samples in training LUT
+#" @param nbsamplesperrun numeric. number of training sample per individual regression model
+#" @param nbmodels numeric. number of individual models to be run for ensemble
+#" @param replacement bolean. is there replacement in subsampling?
+#" @param sailversion character. Either 4SAIL or 4SAIL2
+#" @param parms2estimate list. list of input parameters to be estimated
+#" @param bands2select list. list of bands used for regression for each input parameter
+#" @param noiselevel list. list of noise value added to reflectance (defined per input parm)
+#" @param specprospect list. Includes optical constants required for PROSPECT
+#" @param specsoil list. Includes either dry soil and wet soil,  or a unique soil sample if the psoil parameter is not inverted
+#" @param specatm list. Includes direct and diffuse radiation for clear conditions
+#" @param path_results character. path for results
+#" @param figplot boolean. Set TRUE to get scatterplot of estimated biophysical variable during training step
+#" @param force4lowlai boolean. Set TRUE to artificially reduce leaf chemical constituent content for low LAI
+#"
+#"
+#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut
+#" @export
+
+train_prosail_inversion <- function(minval = NULL, maxval = NULL,
+                                    typedistrib = NULL, gaussiandistrib = NULL, parmset = NULL,
+                                    nbsamples = 2000, nbsamplesperrun = 100, nbmodels = 20, replacement = TRUE,
+                                    sailversion = "4SAIL",
+                                    parms2estimate = "lai", bands2select = NULL, noiselevel = NULL,
+                                    specprospect = NULL,  specsoil = NULL,  specatm = NULL,
+                                    path_results = "./", figplot = FALSE, force4lowlai = TRUE) {
+
+  ###===================================================================###
+  ###           1- PRODUCE A LUT TO TRAIN THE HYBRID INVERSION          ###
+  ###===================================================================###
+  # Define sensor characteristics
+  if (is.null(specprospect)) {
+    specprospect <- prosail::specprospect
+  }
+  if (is.null(specsoil)) {
+    specsoil <- prosail::specsoil
+  }
+  if (is.null(specprospect)) {
+    specatm <- prosail::specatm
+  }
+  # define distribution for parameters to be sampled
+  if (is.null(typedistrib)) {
+    typedistrib <- data.frame("CHL" = "Uniform",  "CAR" = "Uniform", "EWT" = "Uniform", "ANT" = "Uniform", "LMA" = "Uniform", "N" = "Uniform",  "BROWN" = "Uniform",
+                              "psoil" = "Uniform", "LIDFa" = "Uniform",  "lai" = "Uniform", "q" = "Uniform", "tto" = "Uniform", "tts" = "Uniform",  "psi" = "Uniform")
+  }
+  if (is.null(gaussiandistrib)) {
+    gaussiandistrib <- list("Mean" = NULL, "Std" = NULL)
+  }
+  if (is.null(minval)) {
+    minval <- data.frame("CHL" = 10, "CAR" = 0, "EWT" = 0.01, "ANT" = 0, "LMA" = 0.005, "N" = 1.0, "psoil" = 0.0,  "BROWN" = 0.0,
+                         "LIDFa" = 20,  "lai" = 0.5, "q" = 0.1, "tto" = 0, "tts" = 20,  "psi" = 80)
+  }
+  if (is.null(maxval)) {
+    maxval <- data.frame("CHL" = 75, "CAR" = 15, "EWT" = 0.03, "ANT" = 2, "LMA" = 0.03, "N" = 2.0,  "psoil" = 1.0,  "BROWN" = 0.5,
+                         "LIDFa" = 70,  "lai" = 7, "q" = 0.2, "tto" = 5, "tts" = 30,  "psi" = 110)
+  }
+  # define min and max values
+  # fixed parameters
+  if (is.null(parmset)) {
+    parmset <- data.frame("TypeLidf" = 2,  "alpha" = 40)
+  }
+  # produce input parameters distribution
+  if (sailversion == "4SAIL") {
+    inputprosail <- get_distribution_input_prosail(minval, maxval, parmset, nbsamples,
+                                                   typedistrib = typedistrib,
+                                                   Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std,
+                                                   force4lowlai = force4lowlai)
+  } else if (sailversion == "4SAIL2") {
+    inputprosail <- get_distribution_input_prosail2(minval, maxval, parmset, nbsamples,
+                                                    typedistrib = typedistrib,
+                                                    Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std,
+                                                    force4lowlai = force4lowlai)
+  }
+  if (sailversion == "4SAIL2") {
+    # Definition of Cv && update LAI
+    maxlai <- min(c(maxval$lai), 4)
+    inputprosail$Cv <- NA * inputprosail$lai
+    inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1
+    inputprosail$Cv[which(inputprosail$lai <= maxlai)] <- (1 / maxlai) + inputprosail$lai[which(inputprosail$lai <= maxlai)] / (maxlai + 1)
+    inputprosail$Cv <- inputprosail$Cv * matrix(rnorm(length(inputprosail$Cv), mean = 1, sd = 0.1))
+    inputprosail$Cv[which(inputprosail$Cv < 0)] <- 0
+    inputprosail$Cv[which(inputprosail$Cv > 1)] <- 1
+    inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1
+    inputprosail$fraction_brown <- 0 + 0 * inputprosail$lai
+    inputprosail$diss <- 0 + 0 * inputprosail$lai
+    inputprosail$Zeta <- 0.2 + 0 * inputprosail$lai
+    inputprosail$lai <- inputprosail$lai * inputprosail$Cv
+  }
+
+  # generate LUT of BRF corresponding to inputprosail,  for a sensor
+  brf_lut <- Generate_LUT_BRF(sailversion = sailversion, inputprosail = inputprosail,
+                              specprospect = specprospect, specsoil = specsoil, specatm = specatm)
+
+  # write parameters LUT
+  output <- matrix(unlist(inputprosail),  ncol = length(inputprosail),  byrow = FALSE)
+  filename <- file.path(path_results, "PROSAIL_LUT_InputParms.txt")
+  write.table(x = format(output,  digits = 3), file = filename, append = FALSE,  quote = FALSE,
+              col.names = names(inputprosail),  row.names = FALSE, sep = "\t")
+  # Write BRF LUT corresponding to parameters LUT
+  filename <- file.path(path_results, "PROSAIL_LUT_reflectance.txt")
+  write.table(x = format(t(brf_lut),  digits = 5), file = filename, append = FALSE,  quote = FALSE,
+              col.names = specprospect$lambda,  row.names = FALSE, sep = "\t")
+
+  # Which bands will be used for inversion?
+  if (is.null(bands2select)) {
+    bands2select <- list()
+    for (parm in parms2estimate) {
+      bands2select[[parm]] <- seq(1, length(specprospect$lambda))
+    }
+  }
+  # Add gaussian noise to reflectance LUT: one specific LUT per parameter
+  if (is.null(noiselevel)) {
+    noiselevel <- list()
+    for (parm in parms2estimate) {
+      noiselevel[[parm]] <- 0.01
+    }
+  }
+
+  # produce LIT with noise
+  brf_lut_noise <- list()
+  for (parm in parms2estimate) {
+    brf_lut_noise[[parm]] <- brf_lut[bands2select[[parm]], ] + brf_lut[bands2select[[parm]], ] * matrix(rnorm(nrow(brf_lut[bands2select[[parm]], ]) * ncol(brf_lut[bands2select[[parm]], ]),
+                                                                                                        0, noiselevel[[parm]]), nrow = nrow(brf_lut[bands2select[[parm]], ]))
+  }
+
+  ###===================================================================###
+  ###                     PERFORM HYBRID INVERSION                      ###
+  ###===================================================================###
+  # train SVR for each variable and each run
+  modelsvr <- list()
+  for (parm in parms2estimate) {
+    colparm <- which(parm == names(inputprosail))
+    inputvar <- inputprosail[[colparm]]
+    modelsvr[[parm]] <- prosail_hybrid_train(brf_lut = brf_lut_noise[[parm]], inputvar = inputvar,
+                                             figplot = figplot, nbensemble = nbmodels, withreplacement = replacement)
+  }
+  return(modelsvr)
+}
+
+#" writes ENVI hdr file
+#"
+#" @param hdr content to be written
+#" @param hdrpath Path of the hdr file
+#"
+#" @return None
+#" @importFrom stringr str_count
+#" @export
+write_envi_header <- function(hdr, hdrpath) {
+  h <- lapply(hdr, function(x) {
+    if (length(x) > 1 || (is.character(x) && stringr::str_count(x,  "\\w + ") > 1)) {
+      x <- paste0("{",  paste(x,  collapse = ","),  "}")
+    }
+    # convert last numerics
+    x <- as.character(x)
+  })
+  writeLines(c("ENVI",  paste(names(hdr),  h,  sep = "=")),  con = hdrpath)
+  return(invisible())
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_SpectralIndices.R	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,679 @@
+# ============================================================================== =
+# prosail
+# Lib_spectralindices.R
+# ============================================================================== =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Florian de BOISSIEU <fdeboiss@gmail.com>
+# Copyright 2019/11 Jean-Baptiste FERET
+# ============================================================================== =
+# This Library includes aims at computing spectral indices from reflectance data
+# ============================================================================== =
+
+#" This function computes Area under curve for continuum removed reflectances
+#" See Malenovský et al. (2013) for details
+#" http://dx.doi.org/10.1016/j.rse.2012.12.015
+#"
+#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param aucminmax list. wavelengths of lower and upper boundaries ("CRmin" and "CRmax")
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image (==10000 for S2)
+#"
+#" @return aucval raster
+#" @export
+auc <- function(refl, sensorbands, aucminmax, reflfactor = 1) {
+
+  aucbands <- list()
+  aucbands[["CRmin"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmin"]])]
+  aucbands[["CRmax"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmax"]])]
+  bands <- get_closest_bands(sensorbands, aucbands)
+  for (i in bands[["CRmin"]]:bands[["CRmax"]]) {
+    if (is.na(match(i, bands))) {
+      aucbands[[paste("B", i, sep = "")]] <- sensorbands[i]
+    }
+  }
+  # compute continuum removal for all spectral bands
+  cr <- cr_wl(refl = refl, sensorbands = sensorbands,
+              crbands = aucbands, reflfactor = reflfactor)
+
+  wl <- sort(unlist(aucbands), decreasing = FALSE)
+  aucval <- 0.5 * (1 - cr[[1]]) * (wl[2] - wl[1])
+  for (i in 2:length(cr)) {
+    aucval <- aucval + 0.5 * (2 - cr[[i - 1]] - cr[[i]]) * (wl[i + 1] - wl[i])
+  }
+  aucval <- aucval + 0.5 * (1 - cr[[length(cr)]]) * (wl[i + 2] - wl[i + 1])
+  return(aucval)
+}
+
+#" This function extracts boundaries to be used to compute continuum from reflectance data
+#"
+#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return crminmax list. list of rasters corresponding to minimum and maximum wavelengths
+#" @export
+crbound <- function(refl,  sensorbands,  crbands,  reflfactor = 1) {
+
+  # get closest spectral bands from CR1 and CR2
+  bands <- get_closest_bands(sensorbands, list(crbands[["CRmin"]], crbands[["CRmax"]]))
+  wl <- sensorbands[bands]
+  # get equation for line going from CR1 to CR2
+  crminmax <- readrasterbands(refl = refl,  bands = bands,  reflfactor = reflfactor)
+  names(crminmax) <- paste("wl_", as.character(wl), sep = "")
+  return(crminmax)
+}
+
+#" This function extracts boundaries to be used to compute continuum from reflectance data
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2
+#" @importFrom progress progress_bar
+#" @export
+cr_wl <- function(refl,  sensorbands,  crbands,  reflfactor = 1) {
+
+  # Make sure CRmin and CRmax are correctly defined
+  if (is.na(match("CRmin", names(crbands))) || is.na(match("CRmax", names(crbands)))) {
+    stop("Please define CRmin and CRmax (CRmin<CRmax) as spectral bands in crbands")
+  }
+  if (crbands[["CRmax"]] < crbands[["CRmin"]]) {
+    stop("Please define CRmin < CRmax in crbands")
+  }
+  # extract CRmin and CRmax
+  crminmax <- crbound(refl,  sensorbands,  crbands,  reflfactor = reflfactor)
+  # extract other bands and compute CR
+  crmin <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmin"]])]
+  crmax <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmax"]])]
+  crbands[["CRmin"]] <- NULL
+  crbands[["CRmax"]] <- NULL
+  cr <- list()
+  # initiate progress bar
+  pgbarlength <- length(crbands)
+  pb <- progress_bar$new(
+    format = "Computing continuum removal [:bar] :percent in :elapsedfull, estimated time remaining :eta",
+    total = pgbarlength,  clear = FALSE,  width = 100)
+  # computation for each band
+  for (band in crbands) {
+    pb$tick()
+    bandrank <- get_closest_bands(sensorbands, band)
+    raster2cr <- readrasterbands(refl = refl,  bands = bandrank,  reflfactor = reflfactor)
+    cr[[as.character(band)]] <- computecr(wlmin = crmin,  wlmax = crmax,
+                                          wltarget = band,  boundaries = crminmax,
+                                          target = raster2cr)
+  }
+  return(cr)
+}
+
+#" This function computes continuum removal value for a spectral band of interest,
+#" based on lower and upper wavelengths corresponding to boundaries of the continuum
+#"
+#" @param wlmin numeric. wavelength of the spectral band corresponding to minimum boundary
+#" @param wlmax numeric. wavelength of the spectral band corresponding to maximum boundary
+#" @param wltarget numeric. wavelength of the spectral band for which cr is computed
+#" @param boundaries list. raster objects corresponding to minimum and maximum wavelengths
+#" @param target list. raster object corresponding target wavelength
+#"
+#" @return cr list. raster object corresponding to continuum removed value
+#" @export
+computecr <- function(wlmin,  wlmax,  wltarget,  boundaries,  target) {
+
+  cr <- target / (boundaries[[1]] + (wltarget - wlmin) * (boundaries[[2]] - boundaries[[1]]) / (wlmax - wlmin))
+  return(cr)
+}
+
+#" this function produces a spectral index from an expression defining a spectral index
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl.
+#" @param expressindex  character. expression corresponding to the spectral index to compute
+#" @param listbands list. list of spectral bands defined in the "expressindex" variable
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#" @param nameindex character. name for the index to be computed,  provided in the raster layer
+#"
+#" @return numeric. band numbers of original sensor corresponding to S2
+#" @export
+spectralindices_fromexpression <- function(refl,  sensorbands,  expressindex,  listbands,  reflfactor = 1, nameindex = NULL) {
+
+  # define which bands to be used in the spectral index
+  bands <- get_closest_bands(sensorbands, listbands)
+
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      refl <- refl[bands]
+    } else {
+      refl <- raster::subset(refl, bands)
+    }
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else if (is.list(refl)) {
+    refl <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack,  RasterBrick,  Stars object or a list of rasters")
+  }
+  names(refl) <- gsub(pattern = "B", replacement = "Band", x = names(bands))
+
+  nbbands <- unique(as.numeric(gsub(pattern = "B",
+                                    replacement = "",
+                                    x =  unlist(regmatches(expressindex,
+                                                           gregexpr("B[[:digit:]] + ",
+                                                                    expressindex))))))
+  sortband <- sort(nbbands, index.return = TRUE, decreasing = TRUE)
+  matches <- unique(unlist(regmatches(expressindex,  gregexpr("B[[:digit:]] + ",  expressindex))))[sortband$ix]
+  replaces <- paste("refl[['Band", gsub(pattern = "B", replacement = "", x = matches), "']]", sep = "")
+  expressindex_final <- expressindex
+  for (bb in 1:seq_along(matches)) {
+    expressindex_final <- gsub(pattern = matches[bb],  replacement = replaces[bb],  x = expressindex_final)
+  }
+  si <- eval(parse(text = expressindex_final))
+  if (!is.null(nameindex)) {
+    names(si) <- nameindex
+  }
+  return(si)
+}
+
+#" this function aims at computing spectral indices from Sensor reflectance data in raster object
+#" it computes the spectral indices based on their computation with Sentinel-2
+#" and assumes that the bands of the S2 data follow this order
+#" wavelength	= {496.6,  560.0,  664.5,  703.9,  740.2,  782.5,  835.1,  864.8,  1613.7,  2202.4}
+#" Full description of the indices can be found here:
+#" https://www.sentinel-hub.com/eotaxonomy/indices
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl.
+#" @param sel_indices  list. list of spectral indices to be computed
+#" @param stackout logical. If TRUE returns a stack,  otherwise a list of rasters.
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return list. includes
+#" - spectralindices: List of spectral indices computed from the reflectance initially provided
+#" - listindices: list of spectral indices computable with the function
+#" @importFrom methods is
+#" @importFrom raster stack brick
+#" @export
+
+computespectralindices_raster <- function(refl,  sensorbands,  sel_indices = "ALL",  stackout = TRUE, reflfactor = 1) {
+
+  s2bands <- c("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2,
+               "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+
+  spectralindices <- list()
+  sen2s2 <- get_closest_bands(sensorbands, s2bands)
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      refl <- refl[sen2s2]
+    } else {
+      refl <- raster::subset(refl, sen2s2)
+    }
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else if (is.list(refl)) {
+    refl <- raster::stack(refl[sen2s2]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters")
+  }
+  names(refl) <- names(sen2s2)
+
+  indexall <- list()
+
+  # inelegant but meeeeh
+  listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CCCI", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2",
+                      "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2",
+                      "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDSI", "NDVI", "NDVI_G",
+                      "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP",
+                      "SAVI", "SIPI", "SR", "CR_SWIR")
+  if (sel_indices[1] == "ALL") {
+    sel_indices <- listindices
+  }
+  if ("ARI1" %in% sel_indices) {
+    ari1 <- (1 / refl[["B3"]]) - (1 / refl[["B5"]])
+    spectralindices$ARI1 <- ari1
+  }
+  if ("ARI2" %in% sel_indices) {
+    ari2 <- (refl[["B8"]] / refl[["B2"]]) - (refl[["B8"]] / refl[["B3"]])
+    spectralindices$ARI2 <- ari2
+  }
+  if ("ARVI" %in% sel_indices) {
+    arvi <- (refl[["B8"]] - (2 * refl[["B4"]] - refl[["B2"]])) / (refl[["B8"]] + (2 * refl[["B4"]] - refl[["B2"]]))
+    spectralindices$ARVI <- arvi
+  }
+  if ("BAI" %in% sel_indices) {
+    bai <- (1 / ((0.1 - refl[["B4"]])**2 + (0.06 - refl[["B8"]])**2))
+    spectralindices$BAI <- bai
+  }
+  if ("BAIS2" %in% sel_indices) {
+    bais2 <-  (1 - ((refl[["B6"]] * refl[["B7"]] * refl[["B8A"]]) / refl[["B4"]])**0.5) * ((refl[["B12"]] - refl[["B8A"]]) / ((refl[["B12"]] + refl[["B8A"]])**0.5) + 1)
+    spectralindices$BAIS2 <- bais2
+  }
+  if ("CCCI" %in% sel_indices) {
+    ccci <- ((refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]])) / ((refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]]))
+    spectralindices$CCCI <- ccci
+  }
+  if ("CHL_RE" %in% sel_indices) {
+    chl_re <- refl[["B5"]] / refl[["B8"]]
+    spectralindices$CHL_RE <- chl_re
+  }
+  if ("CRI1" %in% sel_indices) {
+    cri1 <- (1 / refl[["B2"]]) - (1 / refl[["B3"]])
+    spectralindices$CRI1 <- cri1
+  }
+  if ("CRI2" %in% sel_indices) {
+    cri2 <- (1 / refl[["B2"]]) - (1 / refl[["B5"]])
+    spectralindices$CRI2 <- cri2
+  }
+  if ("EVI" %in% sel_indices) {
+    evi <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / ((refl[["B8"]] + 6 * refl[["B4"]] - 7.5 * refl[["B2"]] + 1))
+    spectralindices$EVI <- evi
+  }
+  if ("EVI2" %in% sel_indices) {
+    evi2 <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + 2.4 * refl[["B4"]] + 1)
+    spectralindices$EVI2 <- evi2
+  }
+  if ("GRVI1" %in% sel_indices) {
+    grvi1 <- (refl[["B4"]] - refl[["B3"]]) / (refl[["B4"]] + refl[["B3"]])
+    spectralindices$GRVI1 <- grvi1
+  }
+  if ("GNDVI" %in% sel_indices) {
+    gndvi <- (refl[["B8"]] - refl[["B3"]]) / (refl[["B8"]] + refl[["B3"]])
+    spectralindices$GNDVI <- gndvi
+  }
+  if ("IRECI" %in% sel_indices) {
+    ireci <- (refl[["B7"]] - refl[["B4"]]) * (refl[["B6"]] / (refl[["B5"]]))
+    spectralindices$IRECI <- ireci
+  }
+  if ("LAI_SAVI" %in% sel_indices) {
+    lai_savi <- - log(0.371  +  1.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] +  refl[["B4"]] +  0.5)) / 2.4
+    spectralindices$LAI_SAVI <- lai_savi
+  }
+  if  ("MCARI" %in% sel_indices) {
+    mcari <- (1 - 0.2 * (refl[["B5"]] - refl[["B3"]]) / (refl[["B5"]] - refl[["B4"]]))
+    spectralindices$MCARI <- mcari
+  }
+  if ("mNDVI705" %in% sel_indices) {
+    mndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]] - 2 * refl[["B2"]])
+    spectralindices$mNDVI705 <- mndvi705
+  }
+  if ("MSAVI2" %in% sel_indices) {
+    msavi2 <- ((refl[["B8"]] + 1) - 0.5 * sqrt(((2 * refl[["B8"]]) - 1)**2 + 8 * refl[["B4"]]))
+    spectralindices$MSAVI2 <- msavi2
+  }
+  if ("MSI" %in% sel_indices) {
+    msi <- refl[["B11"]] / refl[["B8A"]]
+    spectralindices$MSI <- msi
+  }
+  if ("mSR705" %in% sel_indices) {
+    msr705 <- (refl[["B6"]] - refl[["B2"]]) / (refl[["B5"]] - refl[["B2"]])
+    spectralindices$mSR705 <- msr705
+  }
+  if ("MTCI" %in% sel_indices) {
+    mtci <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B5"]] + refl[["B4"]])
+    spectralindices$MTCI <- mtci
+  }
+  if ("nBR_RAW" %in% sel_indices) {
+    nbr_raw <- (refl[["B8"]] - refl[["B12"]]) / (refl[["B8"]] + refl[["B12"]])
+    spectralindices$nBR_RAW <- nbr_raw
+  }
+  if ("NDI_45" %in% sel_indices) {
+    ndi_45 <- (refl[["B5"]] - refl[["B4"]]) / (refl[["B5"]] + refl[["B4"]])
+    spectralindices$NDI_45 <- ndi_45
+  }
+  if ("NDII" %in% sel_indices) {
+    ndii <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]])
+    spectralindices$NDII <- ndii
+  }
+  if ("NDSI" %in% sel_indices) {
+    ndsi <- (refl[["B3"]] - refl[["B11"]]) / (refl[["B3"]] + refl[["B11"]])
+    spectralindices$NDSI <- ndsi
+  }
+  if ("NDVI" %in% sel_indices) {
+    ndvi <- (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]])
+    spectralindices$NDVI <- ndvi
+  }
+  if ("NDVI_G" %in% sel_indices) {
+    ndvi_g <- refl[["B3"]] * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]])
+    spectralindices$NDVI_G <- ndvi_g
+  }
+  if ("NDVI705" %in% sel_indices) {
+    ndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]])
+    spectralindices$NDVI705 <- ndvi705
+  }
+  if ("NDWI" %in% sel_indices) {
+    ndwi <- (refl[["B3"]] - refl[["B8"]]) / (refl[["B3"]] + refl[["B8"]])
+    spectralindices$NDWI <- ndwi
+  }
+  if ("NDWI1" %in% sel_indices) {
+    ndwi1 <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]])
+    spectralindices$NDWI1 <- ndwi1
+  }
+  if ("NDWI2" %in% sel_indices) {
+    ndwi2 <- (refl[["B8A"]] - refl[["B12"]]) / (refl[["B8A"]] + refl[["B12"]])
+    spectralindices$NDWI2 <- ndwi2
+  }
+  if ("PSRI" %in% sel_indices) {
+    psri <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B5"]])
+    spectralindices$PSRI <- psri
+  }
+  if ("PSRI_NIR" %in% sel_indices) {
+    psri_nir <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B8"]])
+    spectralindices$PSRI_NIR <- psri_nir
+  }
+  if ("RE_NDVI" %in% sel_indices) {
+    re_ndvi <- (refl[["B8"]] - refl[["B6"]]) / (refl[["B8"]] + refl[["B6"]])
+    spectralindices$RE_NDVI <- re_ndvi
+  }
+  if ("RE_NDWI" %in% sel_indices) {
+    re_ndwi <- (refl[["B4"]] - refl[["B6"]]) / (refl[["B4"]] + refl[["B6"]])
+    spectralindices$RE_NDWI <- re_ndwi
+  }
+  if ("S2REP" %in% sel_indices) {
+    s2rep <- 705 + 35 * (0.5 * (refl[["B8"]] + refl[["B5"]]) - refl[["B6"]]) / (refl[["B7"]] - refl[["B6"]])
+    spectralindices$S2REP <- s2rep
+  }
+  if ("SAVI" %in% sel_indices) {
+    savi <- 1.5 * (refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]] + 0.5)
+    spectralindices$SAVI <- savi
+  }
+  if ("SIPI" %in% sel_indices) {
+    sipi <- (refl[["B8"]] - refl[["B2"]]) / (refl[["B8"]] - refl[["B4"]])
+    spectralindices$SIPI <- sipi
+  }
+  if ("SR" %in% sel_indices) {
+    sr <- refl[["B8"]] / refl[["B4"]]
+    spectralindices$SR <- sr
+  }
+  if ("TCARI" %in% sel_indices) {
+    sr <- refl[["B8"]] / refl[["B4"]]
+    spectralindices$SR <- sr
+  }
+  if ("CR_SWIR" %in% sel_indices) {
+    cr_swir <- refl[["B11"]] / (refl[["B8A"]] + (s2bands["B11"] - s2bands["B8A"]) * (refl[["B12"]] - refl[["B8A"]]) / (s2bands["B12"] - s2bands["B8A"]))
+    spectralindices$CR_SWIR <- cr_swir
+  }
+
+  if (stackout)
+    spectralindices <- raster::stack(spectralindices)
+
+  res <- list("spectralindices" = spectralindices, "listindices" = listindices)
+  return(res)
+}
+
+#" this function aims at computing spectral indices from Sensor reflectance data.
+#" it computes the spectral indices based on their computation with Sentinel-2
+#" and assumes that the bands of the S2 data follow this order
+#" wavelength	= {496.6,  560.0,  664.5,  703.9,  740.2,  782.5,  835.1,  864.8,  1613.7,  2202.4}
+#" Full description of the indices can be found here:
+#" https://www.sentinel-hub.com/eotaxonomy/indices
+#"
+#" @param refl numeric. reflectance dataset defined in matrix
+#" @param sel_indices list. list of spectral indices to be computed
+#" @param sensorbands numeric. wavelength of the spectral bands corresponding to the spectral index
+#"
+#" @return list. includes
+#" - spectralindices: List of spectral indices computed from the reflectance initially provided
+#" - listindices: list of spectral indices computable with the function
+#" @export
+
+computespectralindices_hs <- function(refl, sensorbands, sel_indices = "ALL") {
+
+  spectralindices <- list()
+  s2bands <- data.frame("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2,
+               "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+
+  sen2s2 <- get_closest_bands(sensorbands, s2bands)
+  indexall <- list()
+  # set zero vaues to >0 in order to avoid problems
+  selzero <- which(refl == 0)
+  refl[selzero] <- 0.005
+  if (dim(refl)[1] == length(sensorbands)) {
+    refl <- t(refl)
+  }
+
+  # inelegant but meeeeh
+  listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2",
+                      "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2",
+                      "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDVI", "NDVI_G",
+                      "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP",
+                      "SAVI", "SIPI", "SR", "CR_SWIR")
+  if (sel_indices == "ALL") {
+    sel_indices <- listindices
+  }
+  if ("ARI1" %in% sel_indices) {
+    ari1 <- (1 / refl[, sen2s2[["B3"]]]) - (1 / refl[, sen2s2[["B5"]]])
+    spectralindices$ARI1 <- ari1
+  }
+  if ("ARI2" %in% sel_indices) {
+    ari2 <- (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B2"]]]) - (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B3"]]])
+    spectralindices$ARI2 <- ari2
+  }
+  if ("ARVI" %in% sel_indices) {
+    arvi <- (refl[, sen2s2[["B8"]]] - (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) / (refl[, sen2s2[["B8"]]] + (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]))
+    spectralindices$ARVI <- arvi
+  }
+  if ("BAI" %in% sel_indices) {
+    bai <- (1 / ((0.1 - refl[, sen2s2[["B4"]]])**2 + (0.06 - refl[, sen2s2[["B8"]]])**2))
+    spectralindices$BAI <- bai
+  }
+  if ("BAIS2" %in% sel_indices) {
+    bais2 <-  (1 - ((refl[, sen2s2[["B6"]]] * refl[, sen2s2[["B7"]]] * refl[, sen2s2[["B8A"]]]) / refl[, sen2s2[["B4"]]])**0.5) * ((refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / ((refl[, sen2s2[["B12"]]] + refl[, sen2s2[["B8A"]]])**0.5) + 1)
+    spectralindices$BAIS2 <- bais2
+  }
+  if ("CCCI" %in% sel_indices) {
+    ccci <- ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]])) / ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]))
+    spectralindices$CCCI <- ccci
+  }
+  if ("CHL_RE" %in% sel_indices) {
+    chl_re <- refl[, sen2s2[["B5"]]] / refl[, sen2s2[["B8"]]]
+    spectralindices$CHL_RE <- chl_re
+  }
+  if ("CRI1" %in% sel_indices) {
+    cri1 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B3"]]])
+    spectralindices$CRI1 <- cri1
+  }
+  if ("CRI2" %in% sel_indices) {
+    cri2 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B5"]]])
+    spectralindices$CRI2 <- cri2
+  }
+  if ("EVI" %in% sel_indices) {
+    evi <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / ((refl[, sen2s2[["B8"]]] + 6 * refl[, sen2s2[["B4"]]] - 7.5 * refl[, sen2s2[["B2"]]] + 1))
+    spectralindices$EVI <- evi
+  }
+  if ("EVI2" %in% sel_indices) {
+    evi2 <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + 2.4 * refl[, sen2s2[["B4"]]] + 1)
+    spectralindices$EVI2 <- evi2
+  }
+  if ("GRVI1" %in% sel_indices) {
+    grvi1 <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B3"]]])
+    spectralindices$GRVI1 <- grvi1
+  }
+  if ("GNDVI" %in% sel_indices) {
+    gndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B3"]]])
+    spectralindices$GNDVI <- gndvi
+  }
+  if ("IRECI" %in% sel_indices) {
+    ireci <- (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B4"]]]) * (refl[, sen2s2[["B6"]]] / (refl[, sen2s2[["B5"]]]))
+    spectralindices$IRECI <- ireci
+  }
+  if ("LAI_SAVI" %in% sel_indices) {
+    lai_savi <- - log(0.371  +  1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] +  refl[, sen2s2[["B4"]]] +  0.5)) / 2.4
+    spectralindices$LAI_SAVI <- lai_savi
+  }
+  if  ("MCARI" %in% sel_indices) {
+    mcari <- (1 - 0.2 * (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]))
+    spectralindices$MCARI <- mcari
+  }
+  if ("mNDVI705" %in% sel_indices) {
+    mndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]] - 2 * refl[, sen2s2[["B2"]]])
+    spectralindices$mNDVI705 <- mndvi705
+  }
+  if ("MSAVI2" %in% sel_indices) {
+    msavi2 <- ((refl[, sen2s2[["B8"]]] + 1) - 0.5 * sqrt(((2 * refl[, sen2s2[["B8"]]]) - 1)**2 + 8 * refl[, sen2s2[["B4"]]]))
+    spectralindices$MSAVI2 <- msavi2
+  }
+  if ("MSI" %in% sel_indices) {
+    msi <- refl[, sen2s2[["B11"]]] / refl[, sen2s2[["B8"]]]
+    spectralindices$MSI <- msi
+  }
+  if ("mSR705" %in% sel_indices) {
+    msr705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B2"]]])
+    spectralindices$mSR705 <- msr705
+  }
+  if ("MTCI" %in% sel_indices) {
+    mtci <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$MTCI <- mtci
+  }
+  if ("nBR_RAW" %in% sel_indices) {
+    nbr_raw <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B12"]]])
+    spectralindices$nBR_RAW <- nbr_raw
+  }
+  if ("NDI_45" %in% sel_indices) {
+    ndi_45 <- (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDI_45 <- ndi_45
+  }
+  if ("NDII" %in% sel_indices) {
+    ndii <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDII <- ndii
+  }
+  if ("NDSI" %in% sel_indices) {
+    ndisi <- (refl[, sen2s2[["B3"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B3"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDSI <- ndsi
+  }
+  if ("NDVI" %in% sel_indices) {
+    ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDVI <- ndvi
+  }
+  if ("NDVI_G" %in% sel_indices) {
+    ndvi_g <- refl[, sen2s2[["B3"]]] * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDVI_G <- ndvi_g
+  }
+  if ("NDVI705" %in% sel_indices) {
+    ndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]])
+    spectralindices$NDVI705 <- ndvi705
+  }
+  if ("NDWI1" %in% sel_indices) {
+    ndwi1 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDWI1 <- ndwi1
+  }
+  if ("NDWI2" %in% sel_indices) {
+    ndwi2 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B12"]]])
+    spectralindices$NDWI2 <- ndwi2
+  }
+  if ("PSRI" %in% sel_indices) {
+    psri <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]])
+    spectralindices$PSRI <- psri
+  }
+  if ("PSRI_NIR" %in% sel_indices) {
+    psri_nir <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]])
+    spectralindices$PSRI_NIR <- psri_nir
+  }
+  if ("RE_NDVI" %in% sel_indices) {
+    re_ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B6"]]])
+    spectralindices$RE_NDVI <- re_ndvi
+  }
+  if ("RE_NDWI" %in% sel_indices) {
+    re_ndwi <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B6"]]])
+    spectralindices$RE_NDWI <- re_ndwi
+  }
+  if ("S2REP" %in% sel_indices) {
+    s2rep <- 705 + 35 * (0.5 * (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]]) - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B6"]]])
+    spectralindices$S2REP <- s2rep
+  }
+  if ("SAVI" %in% sel_indices) {
+    savi <- 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]] + 0.5)
+    spectralindices$SAVI <- savi
+  }
+  if ("SIPI" %in% sel_indices) {
+    sipi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]])
+    spectralindices$SIPI <- sipi
+  }
+  if ("SR" %in% sel_indices) {
+    sr <- refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B4"]]]
+    spectralindices$SR <- sr
+  }
+  if ("CR_SWIR" %in% sel_indices) {
+    cr_swir <- refl[, sen2s2[["B11"]]] / (refl[, sen2s2[["B8A"]]] + (s2bands$B11 - s2bands$B8A) * (refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / (s2bands$B12 - s2bands$B8A))
+    spectralindices$CR_SWIR <- cr_swir
+  }
+  res <- list("spectralindices" = spectralindices, "listindices" = listindices)
+  return(res)
+}
+
+#" this function identifies the bands of a given sensor with closest match to its spectral characteristics
+#"
+#" @param sensorbands numeric. wavelength in nanometer of the sensor of interest
+#" @param listbands numeric or list. Named vector or list of spectral bands corresponding to sensor
+#"
+#" @return numeric. band numbers of original sensor
+#" @export
+get_closest_bands <- function(sensorbands, listbands) {
+  sapply(listbands,  function(x) {
+    b <- which.min(abs(sensorbands - x))
+    names(b) <- ""
+    b
+  })
+}
+
+#" This function computes interquartile range (IQR) criterion,  which can be used
+#" as a criterion for outlier detection
+#"
+#" @param distval numeric. vector of distribution of values
+#" @param weightirq numeric. weighting factor appplied to IRQ to define lower and upper boudaries for outliers
+#"
+#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2
+#" @importFrom stats IQR quantile
+#" @export
+iqr_outliers <- function(distval, weightirq = 1.5) {
+  iqr <- IQR(distval,  na.rm = TRUE)
+  range_iqr <- c(quantile(distval,  0.25, na.rm = TRUE), quantile(distval,  0.75, na.rm = TRUE))
+  outlier_iqr <- c(range_iqr[1] - weightirq * iqr, range_iqr[2] + weightirq * iqr)
+  return(outlier_iqr)
+}
+
+#" This function selects bands from a raster or stars object
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param bands numeric. rank of bands to be read in refl
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return robj list. R object (default is raster,  stars if refl is stars object)
+#" @importFrom raster subset stack
+#" @export
+readrasterbands <- function(refl,  bands,  reflfactor = 1) {
+
+  # get equation for line going from CR1 to CR2
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      robj <- refl[bands]
+    } else {
+      robj <- raster::subset(refl, bands)
+    }
+    if (!reflfactor == 1) {
+      robj <- robj / reflfactor
+    }
+  } else if (is.list(refl)) {
+    robj <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      robj <- robj / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters")
+  }
+  return(robj)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,661 @@
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+Relevés des adventices dans 28 parcelles de maïs pour séparer les effets de la séquence des cultures de l'hétérogénéité spatio-temporelle du paysage des 10 dernières années. 
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+Ce projet a été financé en partenariat par : 
+MINCyT: Ministerio de ciencia, tecnologia e innovation productiva (Argentine)
+ECOS-SUD (France)</gco:CharacterString>
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+                           <gmd:date>
+                              <gmd:CI_Date>
+                                 <gmd:date>
+                                    <gco:Date>2013-10-21</gco:Date>
+                                 </gmd:date>
+                                 <gmd:dateType>
+                                    <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode"
+                                                         codeListValue="publication"/>
+                                 </gmd:dateType>
+                              </gmd:CI_Date>
+                           </gmd:date>
+                        </gmd:CI_Citation>
+                     </gmd:specification>
+                     <gmd:explanation gco:nilReason="missing">
+                        <gco:CharacterString/>
+                     </gmd:explanation>
+                     <gmd:pass>
+                        <gco:Boolean>true</gco:Boolean>
+                     </gmd:pass>
+                  </gmd:DQ_ConformanceResult>
+               </gmd:result>
+            </gmd:DQ_DomainConsistency>
+         </gmd:report>
+         <gmd:lineage>
+            <gmd:LI_Lineage>
+               <gmd:statement>
+                  <gco:CharacterString>Relevé sur le terrain des adventices du maïs</gco:CharacterString>
+               </gmd:statement>
+            </gmd:LI_Lineage>
+         </gmd:lineage>
+      </gmd:DQ_DataQuality>
+  </gmd:dataQualityInfo>
+</gmd:MD_Metadata>
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Metadata_validation.txt	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,5 @@
+
+Validation of metadata according to ISO 19139
+ 
+TRUE
+ according to ISO 19139 XML schemas! 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mission.csv	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,2 @@
+"","x"
+"1","SAFE"
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/NDVI.tabular	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,10001 @@
+S2A_Subset	longitude	latitude	NDVI
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Binary file test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip has changed
Binary file test-data/S2A_Subset has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/S2A_Subset.hdr	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,35 @@
+ENVI
+description = {
+  File Resize Result, x resize factor: 1.000000, y resize factor: 1.000000.
+  [Thu Jul 11 09:00:28 2019]}
+samples = 1000
+lines   = 1000
+bands   = 10
+header offset = 0
+file type = ENVI Standard
+data type = 2
+interleave = bil
+sensor type = Unknown
+byte order = 0
+x start = 998
+y start = 766
+map info = {UTM, 1.000, 1.000, 356780.000, 351500.000, 1.0000000000e+001, 1.0000000000e+001, 33, North, WGS-84, units=Meters}
+coordinate system string = {PROJCS["WGS_1984_UTM_Zone_33N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",15.0],PARAMETER["scale_factor",0.9996],PARAMETER["latitude_of_origin",0.0],UNIT["Meter",1.0]]}
+wavelength units = Nanometers
+z plot range = {-32768.00, 32767.00}
+data ignore value = 0
+default stretch = 0.000000 1000.000000 linear
+band names = {
+ Resize (band 02:S2A_T33NUD_20180104_Subset), 
+ Resize (band 03:S2A_T33NUD_20180104_Subset), 
+ Resize (band 04:S2A_T33NUD_20180104_Subset), 
+ Resize (band 05:S2A_T33NUD_20180104_Subset), 
+ Resize (band 06:S2A_T33NUD_20180104_Subset), 
+ Resize (band 07:S2A_T33NUD_20180104_Subset), 
+ Resize (band 08:S2A_T33NUD_20180104_Subset), 
+ Resize (band 08A:S2A_T33NUD_20180104_Subset), 
+ Resize (band 11:S2A_T33NUD_20180104_Subset), 
+ Resize (band 12:S2A_T33NUD_20180104_Subset)}
+wavelength = {
+  496.600006,  560.000000,  664.500000,  703.900024,  740.200012,  782.500000,
+  835.099976,  864.799988, 1613.699951, 2202.399902}
Binary file test-data/S2A_T33NUD_Plots.zip has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/val_metadata.r	Mon Jan 09 13:36:02 2023 +0000
@@ -0,0 +1,41 @@
+#Rscript
+
+############################################
+##  Validate ISO 19139 metadata documen   ##
+############################################
+
+#####Packages : ncdf4,
+#               geometa,
+#               httr
+#               xml
+#               xml2
+library(geometa)
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    input_data <- args[1]
+}
+
+##------------------------------------------##
+##      Read ISO 19139 from a file or url   ##
+##------------------------------------------##
+
+# Test depuis catalogue Indores http://indores-tmp.in2p3.fr/geonetwork/srv/fre/catalog.search#/metadata/112ebeea-e79c-422c-8a43-a5a8323b446b
+# <!--ISO 19139 XML compliance: NO-->
+input_data <- xml2::read_xml(input_data)
+
+dir.create("results")
+file.create("results/meta.xml")
+
+xml2::write_xml(input_data, file = "results/meta.xml")
+
+md <- geometa::readISO19139("results/meta.xml")
+
+
+# validate iso
+cat("\nValidation of metadata according to ISO 19139\n", md$validate(), file = "Metadata_validation.txt", fill = 1, append = FALSE)