Merge branch 'dev'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: prospect
 Title: PROSPECT leaf radiative transfer model and inversion routines
-Version: 1.6.1
+Version: 1.6.2
 Authors@R: c(person(given = "Jean-Baptiste", 
 					    family = "Feret", 
 					    email = "jb.feret@teledetection.fr", 
@@ -20,12 +20,12 @@ LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.0
 Suggests: 
+    data.table,
     knitr,
     emojifont,
     rmarkdown,
     testthat (>= 3.0.0)
 Imports: 
-    data.table,
     dplyr,
     expint,
     future,

NAMESPACE

@@ -7,6 +7,7 @@ export(Invert_PROSPECT)
 export(Invert_PROSPECT_OPT)
 export(Invert_PROSPECT_subdomain)
 export(Merit_PROSPECT_RMSE)
+export(Merit_PROSPECT_dRMSE)
 export(PROSPECT)
 export(PROSPECT_LUT)
 export(SetInitParm)
@@ -22,7 +23,6 @@ export(print_msg)
 export(reshape_lop4inversion)
 export(tryInversion)
 import(dplyr)
-importFrom(data.table,fread)
 importFrom(expint,expint)
 importFrom(future,multisession)
 importFrom(future,plan)
@@ -33,4 +33,3 @@ importFrom(pracma,rmserr)
 importFrom(progress,progress_bar)
 importFrom(stats,lm)
 importFrom(stats,runif)
-importFrom(utils,tail)

NEWS.md

@@ -1,3 +1,13 @@
+# prospect v1.6.2
+## fix
+- fixed wrong merge for correction of JOSS paper
+
+## addition
+- added merit function Merit_PROSPECT_dRMSE to perform inversion based on 1st derivative
+
+## change
+- moved data.table from Imports to Suggestions
+
 # prospect v1.6.1
 ## addition
 - added input dataframe InputPROSPECT to function PROSPECT

R/Lib_PROSPECT.R

@@ -32,17 +32,23 @@
 #' @param PROT numeric. protein content  (g.cm-2)
 #' @param CBC numeric. NonProt Carbon-based constituent content (g.cm-2)
 #' @param alpha numeric. Solid angle for incident light at surface of leaf
+#' @param check boolean. set to TRUE to check input data format
 #'
 #' @return leaf directional-hemispherical reflectance and transmittance
 #' @importFrom expint expint
 #' @export
 PROSPECT <- function(SpecPROSPECT = NULL, Input_PROSPECT = NULL,
                      N = 1.5, CHL = 40.0, CAR = 8.0, ANT = 0.0, BROWN = 0.0,
-                     EWT = 0.01, LMA = NULL, PROT = 0, CBC = 0, alpha = 40.0) {
+                     EWT = 0.01, LMA = NULL, PROT = 0, CBC = 0, alpha = 40.0,
+                     check = TRUE) {
 
   # define PROSPECT input in a dataframe
-  Input_PROSPECT <- define_Input_PROSPECT(Input_PROSPECT, CHL, CAR, ANT, BROWN,
-                                          EWT, LMA, PROT, CBC, N, alpha)
+  Input_PROSPECT <- define_Input_PROSPECT(Input_PROSPECT, CHL, CAR,
+                                          ANT, BROWN, EWT, LMA, PROT,
+                                          CBC, N, alpha)
+  # if (check) Input_PROSPECT <- define_Input_PROSPECT(Input_PROSPECT, CHL, CAR,
+  #                                                    ANT, BROWN, EWT, LMA, PROT,
+  #                                                    CBC, N, alpha)
   # default: simulates leaf optics using full spectral range
   if (is.null(SpecPROSPECT)) SpecPROSPECT <- prospect::SpecPROSPECT_FullRange
   # compute total absorption corresponding to each homogeneous layer
@@ -260,7 +266,6 @@ define_Input_PROSPECT <- function(Input_PROSPECT, CHL = NULL, CAR = NULL,
 #'
 #' @return list including spectral properties at the new resolution
 #' @import dplyr
-#' @importFrom utils tail
 #' @export
 
 FitSpectralData <- function(lambda, SpecPROSPECT = NULL,
@@ -269,8 +274,10 @@ FitSpectralData <- function(lambda, SpecPROSPECT = NULL,
   # default: simulates leaf optics using full spectral range
   if (is.null(SpecPROSPECT)) SpecPROSPECT <- prospect::SpecPROSPECT_FullRange
   # convert Refl and Tran into dataframe if needed
-  if (class(Refl)[1]%in%c('numeric', 'matrix')) Refl <- data.frame(Refl)
-  if (class(Tran)[1]%in%c('numeric', 'matrix')) Tran <- data.frame(Tran)
+  if (inherits(Refl, what = c('numeric', 'matrix'))) Refl <- data.frame(Refl)
+  if (inherits(Tran, what = c('numeric', 'matrix'))) Tran <- data.frame(Tran)
+  # if (class(Refl)[1]%in%c('numeric', 'matrix')) Refl <- data.frame(Refl)
+  # if (class(Tran)[1]%in%c('numeric', 'matrix')) Tran <- data.frame(Tran)
   # Adjust LOP: check common spectral domain between PROSPECT and leaf optics
   if (!is.null(Refl)) Refl <- Refl %>% filter(lambda%in%SpecPROSPECT$lambda)
   if (!is.null(Tran)) Tran <- Tran %>% filter(lambda%in%SpecPROSPECT$lambda)
@@ -356,7 +363,6 @@ PROSPECT_LUT <- function(Input_PROSPECT, SpecPROSPECT = NULL) {
 #' @param dbName character. name of the database available online
 #'
 #' @return list. Includes leaf chemistry, refl, tran & number of samples
-#' @importFrom data.table fread
 #' @export
 
 download_LeafDB <- function(urldb = NULL,

R/Lib_PROSPECT_Inversion.R

@@ -84,6 +84,8 @@ Invert_PROSPECT <- function(SpecPROSPECT = NULL,
                                         Est_alpha = Est_alpha,
                                         xlub = xlub,
                                         InitValues = InitValues)
+  # complement initial values
+  parms_checked$InitValues <- define_Input_PROSPECT(Input_PROSPECT = parms_checked$InitValues)
   # check if data class is compatible and convert into data.frame
   RT <- reshape_lop4inversion(Refl = Refl,
                               Tran = Tran,
@@ -253,20 +255,53 @@ tryInversion <- function(x0, MeritFunction, SpecPROSPECT, Refl, Tran,
 #'
 #' @return fc estimates of the parameters
 #' @export
+
 Merit_PROSPECT_RMSE <- function(x, SpecPROSPECT,
                                 Refl, Tran,
                                 Input_PROSPECT,
                                 Parms2Estimate) {
   x[x < 0] <- 0
   Input_PROSPECT[Parms2Estimate] <- x
   RT <- do.call("PROSPECT", c(list(SpecPROSPECT = SpecPROSPECT), Input_PROSPECT))
+  # RT <- do.call("PROSPECT", args = list(SpecPROSPECT = SpecPROSPECT,
+  #                                       Input_PROSPECT = Input_PROSPECT,
+  #                                       check = F))
   fcr <- fct <- 0
   if (!is.null(Refl)) fcr <- sqrt(sum((Refl - RT$Reflectance)**2) / length(RT$Reflectance))
   if (!is.null(Tran)) fct <- sqrt(sum((Tran - RT$Transmittance)**2) / length(RT$Transmittance))
   fc <- fcr + fct
   return(fc)
 }
 
+
+#' Merit function for PROSPECT inversion
+#'
+#' @param x numeric. Vector of input variables to estimate
+#' @param SpecPROSPECT list. Includes optical constants refractive index,
+#' specific absorption coefficients and corresponding spectral bands
+#' @param Refl  numeric. measured reflectance data
+#' @param Tran  numeric. measured Transmittance data
+#' @param Input_PROSPECT dataframe. set of PROSPECT input variables
+#' @param Parms2Estimate  numeric. location of variables from Input_PROSPECT
+#' to be estimated through inversion
+#'
+#' @return fc estimates of the parameters
+#' @export
+
+Merit_PROSPECT_dRMSE <- function(x, SpecPROSPECT,
+                                Refl, Tran,
+                                Input_PROSPECT,
+                                Parms2Estimate) {
+  x[x < 0] <- 0
+  Input_PROSPECT[Parms2Estimate] <- x
+  RT <- do.call("PROSPECT", c(list(SpecPROSPECT = SpecPROSPECT), Input_PROSPECT))
+  fcr <- fct <- 0
+  if (!is.null(Refl)) fcr <- sqrt(sum((diff(Refl) - diff(RT$Reflectance))**2) / (length(RT$Reflectance)-1))
+  if (!is.null(Tran)) fct <- sqrt(sum((diff(Tran) - diff(RT$Transmittance))**2) / (length(RT$Transmittance)-1))
+  fc <- fcr + fct
+  return(fc)
+}
+
 #' This function defines a regression model to estimate N from R only or T only
 #' @param lambda  numeric. spectral bands corresponding to experimental data
 #' @param SpecPROSPECT list. Includes optical constants refractive index,

paper/paper.md

@@ -56,16 +56,16 @@ Hence, it is a key component for remote sensing applications dedicated to
 vegetation monitoring. 
 
 Multiple versions have been released since its first version [@jacquemoud1990].
-[@feret2008] introduced carotenoids and [@feret2017] introduced anthocyanins, 
+@feret2008 introduced carotenoids and @feret2017 introduced anthocyanins, 
 to simulate leaf optical properties from juvenile to mature and senescent 
 development stages. 
-[@feret2021] introduced PROSPECT-PRO, the latest version separating dry matter 
+@feret2021 introduced PROSPECT-PRO, the latest version separating dry matter 
 constituents into proteins and carbon based constituents. 
 In parallel with updated versions of the model, model inversion strategies 
 have been introduced to improve the assessment of leaf chemical constituents 
 [@feret2019; @spafford2021].
 
-PROSPECT implementations since [@feret2008] can be found at 
+PROSPECT implementations since @feret2008 can be found at 
 [this webpage](http://teledetection.ipgp.jussieu.fr/prosail/).
 This includes distributions in matlab, R and fortran programming languages. 
 PROSPECT is coupled with vegetation models, including COSINE dedicated to