Package index • ToolsRTM

All functions

calctav(): Computation of transmissivity of a dielectric plane surface, averaged over all directions of incidence and over all polarizations.
calc_fluspect_bcar(): calc_fluspect_bcar function
campbell(): Campbell function
color.gradient(): Gradient scale Map a numeric vector to a color gradient
Compute_BRF(): Computes bidirectional reflectance factor based on outputs from PRO-4SAIL and sun position
compute_stats(): Compute statistical metrics for model performance
ConservativeScattering(): computes conservative scattering conditions
correlatedValue(): Generate a variable correlated with an existing one
COST_4Fluspect(): Cost function for Fluspect parameter fitting
dataspec.liberty: Liberty leaf model specific absorption coefficients
dataSpec_PDB: PROSPECT-family leaf optical specific absorption coefficients (PDB set)
dataSpec_PRO: PROSPECT leaf optical specific absorption coefficients (PRO set)
dcum(): dcum function
define_bands(): define spectral region
define_constants(): Define constant for FLuxPesp
dladgen(): Computes the leaf angle distribution function value (freq)
EnMap.characteristics: EnMAP hyperspectral sensor band characteristics
ephoton(): Function for calculating the energy of a single photon given its wavelength.
exp1_base(): Compute Exponential Integral E1(x)
extract_dates_from_tiff_files(): Extract acquisition dates from a set of TIFF file names
Extraterrestrial_irradiance: ASTM E490 extraterrestrial solar irradiance spectrum
filter_outliers(): remove outliers
foursail.inf(): foursail.inf The foursail (or 4SAIL) radiative transfer model is a RT model uses for simulating bidirectional reflectance distribution functions within vegetation canopies
foursail.inform(): Performs PROSAIL simulation based on a set of combinations of input parameters for estimating understorey reflectance
foursail(): fourSAIL model coupled with several leaf models
foursail2(): Performs fourSAIL2 + PROSPECT simulation based on a set of combinations of input parameters
foursail_t_o(): Performs PROSAIL simulation based on a set of combinations of input parameters
foursail_t_s(): Performs PROSAIL simulation based on a set of combinations of input parameters
fwhm.prisma: PRISMA hyperspectral sensor band FWHM table
gauss_byMin_Max(): Gaussion distribution
get.Altitude2Pa(): Get atmospheric pressure (in hPa) as a function of altitude
get.cars.pls(): Perform CARS-PLS analysis.
get.coef.SMAC(): Get the coefficients for the SMAC model
get.Extraterrestrial_radiance(): get.Extraterrestrial_radiance
get.indices.v2(): Compute vegetation indices from reflectance spectra (v2, expanded index set)
get.inversion(): get.inversion of the plant traits using different machine learning models
get.inversionOpt(): LUT Inversion Using a Radiative Transfer Model (RTM)
get.LUTfromRanges(): Get a LUT based on a table with Min and Max ranges
get.marmit.rsoil(): Build a canopy-model-ready soil reflectance spectrum from MARMIT
get.marmit1(): Calculate Spectral Reflectance for Wet Soils Using MARMIT-1
get.marmit2(): Simulation of Spectral Reflectance of Wet Soils in the Solar Domain using MARMIT-2
get.packages(): Get the packages
get.plot.cars.pls(): Generate plot for CARSPLS analysis.
get.plot.ML(): This function plots predictions from a machine learning (ML) model on both training and testing datasets.
get.plot.plsr(): Plot Predictions from PLSR Model
get.plots(): Get a plot with simulations
get.RTM(): get.RTM - Run Radiative Transfer Model (RTM)
get.satellite_collection(): Retrieve Satellite Collections
get.sentinel2_cube(): Create and Process a Sentinel-2 Data Cube
get.smac(): Apply an atmospheric correction using SMAC method (Rahman and Dedieu, 1994)
get.sobol.indices(): Get first and total Sobol indices by Carlos Camino
get.spectra.convolved(): Get Convolved Spectra
get.spectra.spart(): Get Spectral Characteristics for the SPART Model
get.spectral.convolution.gaussian(): Convolve reflectance onto a sensor using only NOMINAL band characteristics (center wavelength + FWHM, or center + band edges) – for sensors with no real measured per-nm SRF curve available at all, approximated as a Gaussian response (optionally truncated to a published band edge range).
get.spectral.convolution(): get.spectral.convolution
get.spectral.convolution.rfl(): Get Spectral Convolution for Reflectance
get.spectral.convolution.srf(): Convolve reflectance onto a sensor using a plain per-band SRF table
get.spectral.derivative(): Calculate spectral derivative
get.spectral(): Get spectral bandset for interpolation
get.spectral.sensitivity(): Spectral global sensitivity analysis (Johnson relative importance per wavelength)
get.srf.from_fwhm(): Generate Spectral Response Function from Full Width at Half Maximum (FWHM)
get.stats.ind(): Get Statistical Scores between measurd and predicted single variable
get.stats.plsr(): Get Statistical Scores for PLSR Model
get.stats(): Get Statistical Scores for ML Models
getBSM.toolsRTM(): Brightness-Shape-Moisture soil model
getCor(): Generate multiple mutually-correlated variables at a target correlation level
getCWSI(): Get CWSI for almonds
getFLD2(): Get FLD2
getFLD2_SE(): Get FLD2
getFluspect.B(): Leaf FLUSPECT-B model
getFluspect.Cx(): Leaf FLUSPECT-B-Cx model
getIndices(): GetIndices: A Function to Estimate Common Spectral Indices
getIndicesSE2.ML(): extract spectral indices at Sentinel-2 resolution a sort number of indices
getIndicesSE2(): extract spectral indices at Sentinel-2 resolution
getKmeans(): K-means Classification on a RasterStack This function performs a k-means unsupervised classification of a stack of rasters. Number of clusters can be specified, as well as number of iterations and starting sets. An optional geographic weighting system can be turned on that constrains clusters to a geographic area, by including coordinates in the clustering. All variables are normalized before clustering is performed. https://rdrr.io/github/ozjimbob/ecbtools/man/raster.kmeans.html
getLUT(): Get LUT for radiative transfer models
getLUTs(): Get LUTs for radiative transfer models
getLUT_liberty(): Get LUT for radiative transfer models
getMLmodel(): getMLmodel with a prefixed configuration
getMLmodel.withRetrain(): getMLmodel is a function for retrain a deep model with a prefixed configuration
GetMosaics(): Get a Mosaic from TIFFs Tiles from (Forest type 2018 later from COPERNICUS)
getPredicts(): getPredicts
getReverse.trans(): Reverse a caret preProcess transformation and round the result
getSeries(): get series in a dataframe from rasters
getSim_fromLUT(): Simulate reflectance spectra while sweeping one trait, all others fixed
getSpatialTrait(): function for Spatial mapping of the main traits
getSpatial_index(): this function produces Spatial maps of the spectral index
getSpectraIndices(): this function produces Spatial maps of the spectral indices
GetSpectralseries(): Extract all bands from SE images (adapted to NETCDF dataset)
getSplitData(): getSplitData for ML models
getSplitData_noMessages(): getSplitData for ML models with no messages
getStacks(): Get TIFF from a folder and generate the stack.
getTIFFs(): Extract all bands from SE images (adapted to NETCDF dataset)
getVIF(): Calculate Variance Inflation Factor (VIF)
get_append(): Append tables
get_bounding_box(): Define a Bounding Box Around a Scenario Centroid
get_data(): get random plant traits using a uniform distribution
get_distributionLUT(): Build a LUT with a per-trait distribution choice (Uniform or Gaussian) and an optional Car~Cab correlation
get_response.R(): spectral response function
get_simulations(): Simulate spectra with RT models
hybrid_inversion(): Inversion of plant traits using ML models
hybrid_inversionE(): Inversion of plant traits using ML models (Ensemble variant)
inform(): The INvertible FOrest Reflectance Model coupled with leaf models
Jfunc1(): edit 2017 12 28: change sampling of angles to match with Jfunc1.m
jfunc1_(): Title
Jfunc2(): J2 function with avoidance of singularity problem edit 2017 12 28: change sampling of angles to match with Jfunc2.m
Jfunc3(): J3 function with avoidance of singularity problem edit 2017 12 28: change sampling of angles to match with Jfunc3.m
Jfunc4(): J4 function for treating (near) conservative scattering edit 2021 11 28: change sampling of angles to match with Jfunc4
LeafDB.Angers: Angers leaf biochemistry/optics reference database
leaf_spectrum: Example measured leaf reflectance/transmittance spectrum
liberty(): LIBERTY model
MAE(): Function that returns Mean Absolute Error
NonConservativeScattering(): computes non conservative scattering conditions
normalize(): Normalize
numjacobian(): num-jacobian function
optipar.2015: Fluspect specific absorption coefficients (2015 parameterisation)
optipar: Fluspect specific absorption coefficients (2003/2008 parameterisation)
plotspectra(): Plot one or more spectra from a data frame
plotspectra_image(): Plot the spectra in points over the image
plotspectra_SE(): Plot spectra as discrete points at Sentinel band positions
prospect_DB(): PROSPECT-Dynamic with brown pigments
prospect_PRO(): PROSPECT-PRO
rbinomcor(): Generate a vector from a binomial distribution correlated with another distribution
rchisqcor(): Generate a vector from a chi-square distribution correlated with another distribution
resample_fun(): Generate a normalized Gaussian spectral response function
rgammacor(): Generate a vector from a Gamma distribution correlated with another distribution
rlnormcor(): Generate a single variable from a distribution correlated with another distribution
RMSE(): Function that returns Root Mean Square Error
rnegbinomcor(): Generate a vector from a negative binomial distribution correlated with another distribution
rnormcor(): Generate a single variable from a distribution correlated with another distribution
rnormcorV(): Generate a vector from a normal distribution correlated with another distribution
rpoiscor(): Generate a vector from a Poisson distribution correlated with another distribution
inputsFlUSPECT inputsINFORM inputsLiberty inputsPROSAIL inputsSPART inputsRTMs: RTM parameter ranges and prior distributions (leaf/canopy models, shared)
runifcor.cor(): Generate a vector from a uniform distribution correlated with another distribution
run_simulation(): Run a vegetation reflectance/energy-balance simulation, choosing models interactively
rweibullcor(): Generate a vector from a Weibull distribution correlated with another distribution
LANDSAT4.TM LANDSAT5.TM LANDSAT7.ETM LANDSAT8.OLI Sentinel2A.MSI Sentinel2B.MSI Sentinel3A.OLCI Sentinel3B.OLCI TerraAqua.MODIS: Satellite sensor band, SMAC atmospheric-correction and SRF metadata
sensor.characteristics: Multi-sensor spectral band centre/bounds lookup table
sigmoid.soil(): Calculate Soil Moisture Content Using a Sigmoid Model
simulate_RTM(): Simulate canopy reflectance with any supported leaf model + any supported canopy model
Sint(): Simpson integration
soilwat(): soilwat function soilwat In this model it is assumed that the water film area is built up
SPART(): Compute Top-of-Canopy and Top-of-Atmosphere Reflectance and Radiance
SPART.simN(): Compute Top-of-Canopy and Top-of-Atmosphere Reflectance and Radiance with Simulations
srf.sentinel2a srf.sentinel2b: Sentinel-2A/2B MSI measured spectral response functions
srf.prisma: PRISMA measured spectral response functions
volscatt(): Compute volume scattering functions and interception coefficients for given solar zenith, viewing zenith, azimuth and leaf inclination angle.