Package 'PEcAnRTM'

Title: PEcAn Functions Used for Radiative Transfer Modeling
Description: Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.
Authors: Mike Dietze [aut], Shawn Serbin [aut], Alexey Shiklomanov [aut, cre], University of Illinois, NCSA [cph]
Maintainer: Alexey Shiklomanov <[email protected]>
License: BSD_3_clause + file LICENSE
Version: 1.7.3.9000
Built: 2024-12-17 18:33:35 UTC
Source: https://github.com/PecanProject/pecan

Help Index

Select spectra

Description

Select spectra

Usage

## S3 method for class 'spectra'
spectra[i, j, drop = FALSE]

Arguments

spectra

Object of class spectra
i, j

indices specifying elements to extract or replace. See base::Extract for details.
drop

Coerce result to the lowest dimension possible? See base::drop() for details.

Select spectra by wavelength

Description

Select spectra by wavelength

Usage

## S3 method for class 'spectra'
spectra[[wavelength, j]]

Arguments

spectra

Object of class spectra
wavelength

Wavelength vector to select
j

index specifying elements to extract or replace. See base::Extract for details.

Assign values to spectra by wavelength

Description

Assign values to spectra by wavelength

Usage

## S3 replacement method for class 'spectra'
spectra[[wavelength, j]] <- value

Arguments

spectra

Object of class spectra
wavelength

Wavelength vector to select
j

index specifying elements to extract or replace. See base::Extract for details.
value

Vector or matrix of values to assign

Burn-in and thinning of MCMC samples

Description

Burn-in and thinning of MCMC samples

Usage

burnin.thin(
  samples,
  target = 5000,
  burnin.ratio = 2,
  auto = TRUE,
  burnin = NULL,
  thin = NULL
)

Arguments

samples

Matrix of MCMC samples
target

Target number of samples (default = 5000). Only applicable if auto=TRUE.
burnin.ratio

Fraction of samples to burn-in; i.e. 2 means to remove first 1/2 of samples, 3 means 1/3, etc. (default = 2). Only applicable if auto=TRUE.
auto

Whether or not to perform automatic burnin and thin based on target number of samples.
burnin

Number of samples to discard as burnin (auto must be FALSE)
thin

Thinning interval (auto must be FALSE)

Combine spectra by wavelength

Description

Combine spectra by wavelength

Usage

## S3 method for class 'spectra'
cbind(...)

Arguments

...

Spectra to combine

Check convergence of multiple MCMC chains

Description

Uses Gelman multivariate Gelman-Rubin diagnostic to check if multiple MCMC chains have converged

Usage

check.convergence(jags_out, threshold = 1.1, verbose = TRUE, ...)

Arguments

jags_out

mcmc.list object (from coda package) containing samples from MCMC chains.
threshold

Gelman-Rubin diagnostic parameter threshold. Default = 1.1
verbose

If TRUE, print convergence result. Default = TRUE
...

Additional arguments to gelman.diag (from coda package)

Value

List length 3 containing the following: * convergence: Logical. Whether or not convergence was achieved. * diagnostics: Numerical value of Gelman-Rubin diagnostics for each parameter and multivariate diagnostic * error: Logical. Whether or not an error occured in the Gelman-Rubin calculation.

Defult inversion settings for PROSPECT 5 models

Description

Default settings for PROSPECT inversion

Usage

default.settings.prospect

Format

An object of class list of length 18.

Get default parameters

Description

Extract default parameter values from model.list

Usage

defparam(modname)

Arguments

modname

Model name. Must match modname in model.list

Value

Named vector of default parameter values

Truncated normal distribution density

Description

Calculates the log density of a univariate truncated normal variable

Usage

dtnorm(x, mu, sd, MIN)

Arguments

x

A random variable
mu

The mean parameter of the distribution; NOTE this is not equal to the mean
sd

The standard deviation parameter of the distribution
MIN

Value at which the truncation takes place

Value

Numeric; log density of the distribution, or -1e15 if the x < MIN

Author(s)

Alexey Shiklomanov

ED radiative transfer module (EDR) wrapper function

Description

This function provides a convenient way to call the ED radiative transfer module (EDR, which simulates full spectral return of an ED patch for a given point in time) directly from R.

Usage

EDR(
  img_path,
  ed2in_path,
  spectra_list,
  trait.values,
  soil_reflect_path = system.file("extdata", "soil_reflect_par.dat", package =
    "PEcAnRTM"),
  wood_reflect_path = system.file("extdata", "wood_reflect_par.dat", package =
    "PEcAnRTM"),
  par.wl = 400:2499,
  nir.wl = 2500,
  edr_exe_path = NULL,
  output.path = dirname(normalizePath(ed2in_path, mustWork = TRUE)),
  settings = list(model = list(revision = "git", config.header = NULL)),
  singularity_args = list(),
  clean = FALSE,
  stderr = TRUE,
  verbose_error = TRUE,
  ...
)

Arguments

img_path

Path to Singularity container (usually a .simg file)
ed2in_path

Path to ED2IN file.
spectra_list

List of spectral data matrices. Names must exactly match the PFTs given in trait.values. Each item must be a matrix of reflectance (labeled 'R' or 'reflectance') and transmittance (labeled 'T' or 'transmittance') values. If the matrix is not of class spectra (see spectra()), it must also have a column of wavelength values (labeled 'wl'). Such a matrix is returned by default by all versions of PROSPECT in this package.
trait.values

Named, hierarchical list of trait values for generating config.xml file. Names must be PFTs, and must exactly match names of spectra_list.
soil_reflect_path

Path to soil reflectance file (defaults to spectra in package extdata)
wood_reflect_path

Path to wood reflectance file (defaults to spectra in package extdata)
par.wl

Vector of wavelengths defining PAR region
nir.wl

Vector of wavelengths defining NIR region
edr_exe_path

If img_path is NULL, a full path to the EDR executable. Ignored otherwise.
output.path

Directory in which to execute the run. Defaults to dirname(ed2in_path).
settings

PEcAn settings list. Default is list(model = list(revision = "git", config.header = NULL)).
singularity_args

Additional arguments to be passed to ⁠singularity run⁠ (before)
clean

Logical. If TRUE, remove all files generated by this function (e.g. cloned history file, ED2IN, output HDF files).
stderr

Logical. If TRUE (default), internalize system2 results as R character vector. TRUE is recommended because it allows EDR to check its execution and to run more quietly.
verbose_error

Logical. If TRUE (default), spit out the full ED if EDR execution fails.
...

Additional arguments to system2

Author(s)

Alexey Shiklomanov

Preprocess history file for EDR

Description

Locate history file based on path and prefix, copy to specified output directory, and rename to correct time.

Usage

EDR.preprocess.history(
  history.path,
  output.path,
  datetime,
  history.prefix = "history"
)

Arguments

history.path

Path to directory containing history file.
output.path

Directory in which to execute the run.
datetime

POSIX date and time for run
history.prefix

String describing the history file prefix in history.path. Default = 'history'

List to FORTRAN data module

Description

Convert R list to a Fortran ⁠/data/⁠ module block

Usage

fortran_data_module(dat, types, modname, fname = paste0(modname, ".f90"))

Arguments

dat

Named list object for creating module. List names will be used to initialize FORTRAN variabiles.
types

Character vector of FORTAN types (e.g. real*8, integer)
modname

Name of the module. We suggest the format 'MOD_yourmodname'.
fname

Output file name. Defaults to 'yourmodname.f90'

Details

For models with large constants (e.g. absorption features in the PROSPECT model), it may be preferable to store these in FORTRAN90 modules. However, manually creating and formatting these files is tedious. This script allows you to automatically generate module files from R lists. It automatically interprets the object lengths as array dimensions (only vectors are supported right now – higher dimension arrays may be in the future) and splits long data into rows of 10. Currently, only numeric data are supported (i.e. no characters).

Author(s)

Alexey Shiklomanov

Examples

w <- 3.2
      x <- 1:5
      y <- 6:15
      z <- seq(exp(1), pi, length.out=42)
      l <- list(x=x, y=y, z=z) ## NOTE that names must be explicitly declared
      l.types <- c('real','integer', 'real*4', 'real*8')
      fortran_data_module(l, l.types, 'testmod',
        file.path(tempdir(), "testmod.f90"))

      x <- runif(10)
      y <- rnorm(10)
      z <- rgamma(10, 3)
      d <- data.frame(x,y,z) ## NOTE that data.frames are just named lists
      d.types <- rep('real*8', ncol(d))
      fortran_data_module(d, d.types, 'random',
        file.path(tempdir(), "random.f90"))

SAIL model

Description

R wrapper for 4SAIL model

Usage

foursail(refl, tran, rsoil, param)

Arguments

refl

input leaf reflectance from 400-2500nm (can be measured or modeled)
tran

input leaf transmittance from 400-2500nm (can be measured or modeled)
rsoil

input soil reflectance spectra from 400-2500nm (can be measured or modeled)
param

Vector of SAIL parameter values: * LIDFa: Leaf angle distribution function - parameter a * LIDFb: Leaf angle distribution function - parameter b * TypeLIDF: Leaf angle distribution function type (1 or 2) * LAI: Leaf area index * q: Hot spot effect parameter * tts: Solar zenith angle * tto: Observer zenith angle * psi: Sun-sensor azimuth angle

Value

Spectra matrix (see spectra()) (2101 x 4) of reflectance factors for wavelengths 400 to 2500nm: * bi-hemispherical reflectance (rddt) * hemispherical directional (rsdt) * directional hemispherical (rdot) * bi-directional (rsot)

Author(s)

Shawn Serbin

Alexey Shiklomanov

Generalized plate model

Description

This is the fundamental physical model underlying the PROSPECT family of leaf RTMs.

Usage

generalized_plate_model(k, refractive, N)

Arguments

k

Specific absorption coefficient (400 - 2500nm)
refractive

Refractive index (400 - 2500nm)
N

Effective number of mesophyll layers (see prospect())

Generate autocorrelated spectral noise

Description

Generate autocorrelated spectral noise

Usage

generate.noise(n = 2101, sigma = 1e-04, fw = 201, fsd = 6)

Arguments

n

Length of output vector (default = 2101)
sigma

Gaussian noise standard deviation (default=1e-4)
fw

Filter width. Will be coerced to an odd number if even (default = 201).
fsd

Scaling factor for filter standard deviation (default = 6)

Read EDR output

Description

Read EDR output

Usage

get.EDR.output(path = getwd())

Arguments

path

Path to directory containing albedo_par/nir.dat files

Perform Bayesian inversion using BayesianTools package

Description

Use samplers from the BayesianTools package to fit models to data. Like invert.auto, this will continue to run until convergence is achieved (based on Gelman diagnostic) and the result has enough samples (as specified by the user; see Details).

Usage

invert_bt(observed, model, prior, custom_settings = list(), loglike = NULL)

Arguments

observed

Vector of observations. Ignored if loglike is not NULL.
model

Function called by log-likelihood. Must be ⁠function(params)⁠ and return a vector equal to length(observed) or nrow(observed). Ignored if loglike is not NULL.
prior

BayesianTools prior object.
custom_settings

Nested settings list. See Details.
loglike

Custom log likelihood function. If NULL, use rtm_loglike() with provided observed and model.

Details

custom_settings is a list of lists, containing the following:

  • common – BayesianTools settings common to both the initial and subsequent samples.

  • init – BayesianTools settings for just the first round of sampling. This is most common for the initial number of iterations, which is the minimum expected for convergence.

  • loop – BayesianTools settings for iterations inside the convergence checking while loop. This is most commonly for setting a smaller iteration count than in init.

  • other – Miscellaneous (non-BayesianTools) settings, including:

    • sampler – String describing which sampler to use. Default is DEzs

    • use_mpsrf – Use the multivariate PSRF to check convergence. Default is FALSE because it may be an excessively conservative diagnostic.

    • min_samp – Minimum number of samples after burnin before stopping. Default is 5000.

    • max_iter – Maximum total number of iterations. Default is 1e6.

    • lag.max – Maximum lag to use for autocorrelation normalization. Default is 10 * log10(n) (same as stats::acf function).

    • save_progress – File name for saving samples between loop iterations. If NULL (default), do not save progress samples.

    • threshold – Threshold for Gelman PSRF convergence diagnostic. Default is 1.1.

    • verbose_loglike – Diagnostic messages in log likelihood output. Default is TRUE.

See the BayesianTools sampler documentation for what can go in the BayesianTools settings lists.

Inversion with automatic convergence checking

Description

Inversion with automatic convergence checking

Usage

invert.auto(
  observed,
  invert.options,
  return.samples = TRUE,
  save.samples = NULL,
  quiet = FALSE,
  parallel = TRUE,
  parallel.cores = NULL,
  parallel.output = "/dev/null"
)

Arguments

observed

Vector, matrix, or data frame (coerced to matrix) of observed values. For spectral data, wavelengths are rows and spectra are columns. Dimensions must align with the output of model.
invert.options

Parameters related to inversion.
return.samples

Include full samples list in output. Default = TRUE.
save.samples

Save samples to file as the inversion proceeds (useful for debugging). If NULL, do not save samples. Default = NULL.
quiet

Suppress progress bar and status messages. Default=FALSE
parallel

Logical. Whether or not to run multiple chains in parallel on multiple cores (default = TRUE).
parallel.cores

Number of cores to use for parallelization. If NULL (default), allocate one fewer than detected number of cores.
parallel.output

Filename (or ” for stdout) for printing parallel outputs. Use with caution. Default = '/dev/null'.

Details

Performs an inversion via the invert.custom function with multiple chains and automatic convergence checking. Convergence checks are performed using the multivariate Gelman-Rubin diagnostic.

Parameters specific to invert.auto are described here. For the remaining parameters, see invert.custom().

  • model – The model to be inverted. This should be an R function that takes params as input and returns one column of observed (nrows should be the same). Constants should be implicitly included here.

  • nchains – Number of independent chains.

  • inits.function – Function for generating initial conditions.

  • ngibbs.max – Maximum number of total iterations (per chain). DEFAULT = 5e6

  • ngibbs.min – Minimum number of total iterations (per chain). DEFAULT = 5000.

  • ngibbs.step – Number of iterations between convergence checks. Default = 1000.

  • run_first – Function to run before running sampling. Takes parallel inputs list containing runID, initial values, and resume (NULL) as an argument.

  • calculate.burnin – If TRUE, use PEcAn.assim.batch::autoburin function to calculate burnin. Otherwise, assume burnin is min(niter/2, iter_conv_check).

  • threshold – Maximum value of the Gelman-Rubin diagnostic for determining convergence. Default = 1.1

Value

List including results (summary statistics), samples (mcmc.list object, or NA if return.samples=FALSE), and other information.

Bayesian inversion of a model

Description

Performs an inversion of an arbitrary model using a modified Metropolis Hastings algorithm with block sampling. This may be slightly slower than the implementation in Fortran, but is much more customizable, as the model can be any R function.

Usage

invert.custom(
  observed,
  invert.options,
  quiet = FALSE,
  return.resume = FALSE,
  runID = NULL
)

Arguments

observed

Vector, matrix, or data frame (coerced to matrix) of observed values. For spectral data, wavelengths are rows and spectra are columns. Dimensions must align with the output of model.
invert.options

R list object containing inversion settings. See details.
quiet

Suppress progress bar and status messages. Default=FALSE
return.resume

If TRUE, return results as list that includes current Jump distribution (useful for continuing an ongoing run) and acceptance rate. Default = FALSE.
runID

Run-unique ID. Useful for parallel runs. Default=NULL

Details

inversion.options contains the following:

  • inits – Vector of initial values of model parameters to be inverted.

  • ngibbs – Number of MCMC iterations

  • prior.function – Function for use as prior. Should take a vector of parameters as input and return a single value – the sum of their log-densities – as output.

  • param.mins – Vector of minimum values for inversion parameters

  • param.maxs – Vector of minimum values for inversion parameters

  • model – The model to be inverted. This should be an R function that takes params and runID as input and returns one column of observed (nrows should be the same). Constants should be implicitly included here.

  • adapt – Number of steps for adapting covariance matrix (i.e. adapt every 'n' steps). Default=100

  • adj_min – Minimum threshold for rescaling Jump standard deviation. Default = 0.1.

  • target – Target acceptance rate. Default=0.234, based on recommendation for multivariate block sampling in Haario et al. 2001

  • do.lsq – Perform least squares optimization first (see invert.lsq), and use outputs to initialize Metropolis Hastings. This may improve mixing time, but risks getting caught in a local minimum. Default=FALSE

  • catch_error – If TRUE (default), wrap model in tryCatch to prevent sampling termination on model execution error.

References

  • Haario, Heikki; Saksman, Eero; Tamminen, Johanna. An adaptive Metropolis algorithm. Bernoulli 7 (2001), no. 2, 223–242. http://projecteuclid.org/euclid.bj/1080222083.

Least squares model inversion

Description

Performs a least-squares inversion of an arbitrary radiative transfer model (passed as an R function). The inversion attempts to minimize the sum of residual least squares between modeled and observed spectra via the Levenberg-Marquardt algorithm (nls.lm function from the minpack.lm package).

Usage

invert.lsq(observed, inits, model, lower = NULL, upper = NULL)

Arguments

observed

Vector of observations (e.g. a reflectance spectrum).
inits

Vector of initial conditions for the parameters.
model

An R function that calls the RTM and returns the error to be minimized. Be sure to include constants here.
lower

Lower bounds on parameters (default=NULL, which means -Inf).
upper

Upper bounds on parameters (default=NULL, which means +Inf).

Author(s)

Alexey Shiklomanov

Load object from an RData file

Description

Load object from an RData file

Usage

load.from.name(filename, filepath = ".")

Arguments

filename

Full name (without path!) of RData file
filepath

Path of RData file (default='.')

Functions for default priors Lognormal mean parameters

Description

Functions for default priors Lognormal mean parameters

Usage

lognorm.mu(mean, sd)

Arguments

mean

Sample mean
sd

Sample standard deviation

Lognormal sigma parameter

Description

Lognormal sigma parameter

Usage

lognorm.sigma(mean, sd)

Arguments

mean

Sample mean
sd

Sample standard deviation

Matplot generic method

Description

Matplot generic method

Usage

matplot(...)

Arguments

...

arguments passed to methods

Matplot default method

Description

Matplot default method

Usage

## Default S3 method:
matplot(...)

Arguments

...

arguments passed to matplot

Plot multiple spectra on same graph

Description

Plot multiple spectra on same graph

Usage

## S3 method for class 'spectra'
matplot(spectra, ...)

Arguments

spectra

Vector (length = length(wavelengths)) or matrix (ncol = length(wavelengths))
...

Additional arguments to matplot

Effective sample size

Description

Calculate effective sample size of vector based on its autocorrelation.

Usage

neff(x, ...)

Arguments

x

A vector or time series
...

additional arguments passed to methods

PROSPECT 4 parameters

Description

Shortcut lists for PROSPECT parameter names

Usage

params.prospect4

Format

An object of class character of length 4.

PROSPECT 5 parameters

Description

PROSPECT 5 parameters

Usage

params.prospect5

Format

An object of class character of length 5.

PROSPECT 5B parameters

Description

PROSPECT 5B parameters

Usage

params.prospect5b

Format

An object of class character of length 6.

PROSPECT D parameters

Description

PROSPECT D parameters

Usage

params.prospectd

Format

An object of class character of length 7.

Convert named parameter vector to EDR-compatible inputs

Description

Creates a nested list whose components are suitable for passing to EDR.

Usage

params2edr(params, sep = ".", prospect = TRUE, version = 5)

Arguments

params

Named parameter vector
sep

Separator between PFT name and trait name. Must be a single character (default = ".").
prospect

Logical. If TRUE (default), scan for PROSPECT traits and pass them to PROSPECT.
version

PROSPECT version

Details

If prospect = TRUE, parameters prefixed with prospect_ are passed to prospect with the specified version, and other parameters are passed to trait.values.

The regular expression defining the separation is greedy, i.e. temperate.Early_Hardwood.SLA will separate into temperate.Early_Hardwood and SLA (assuming the default sep = "."). Therefore, it is crucial that trait names not contain any sep characters (neither ED nor PROSPECT parameters should anyway). If this is a problem, use an alternate separator (e.g. |).

Note that using sep = "." allows this function to directly invert the default behavior of unlist. That is, calling unlist(params2edr(params, prospect = FALSE)$trait.values) will return the input vector of trait values. This makes unlist a convenient way to go from a trait.values list to a properly formatted params vector.

Because unused ED parameters in the config.xml are ignored, the PROSPECT parameters are saved in the trait.values object as well, which may be useful for debugging.

Value

List containing spectra_list and trait.values, both objects needed by EDR.

Author(s)

Alexey Shiklomanov

Plot spectra vs. wavelength

Description

Plot spectra vs. wavelength

Usage

## S3 method for class 'spectra'
plot(spectra, type = "l", ...)

Arguments

spectra

Vector (length = length(wavelengths)) or matrix (ncol = length(wavelengths))
type

plot style, e.g. "l" for lines, "b" for lines and points
...

Additional arguments to plot

Neatly print inversion results summary

Description

Neatly print inversion results summary

Usage

print_results_summary(output)

Arguments

output

Output from invert.auto

Author(s)

Alexey Shiklomanov

Print method for spectra S3 class

Description

Print method for spectra S3 class

Usage

## S3 method for class 'spectra'
print(spectra, n = 10, ...)

Arguments

spectra

Object of class spectra
n

Max number of rows to print (show first n/2 and last n/2 rows)
...

Additional arguments to print

Default prior parameters for PROSPECT models

Description

Default prior parameters for PROSPECT models

Usage

prior.defaultvals.prospect(sd.inflate = 3)

Arguments

sd.inflate

Standard deviation multiplier (default = 3)

Default PROSPECT 5 prior function

Description

Default PROSPECT 5 prior function

Usage

priorfunc.prospect(pmu, psigma)

Arguments

pmu

Lognormal mu parameter
psigma

Lognormal sigma parameter

Details

Assumes lognormal distribution for all parameters. NOTE that prior on N is shifted by 1.

Coupled PROSPECT-Two-stream model

Description

Coupled PROSPECT-Two-stream model

Usage

pro2s(param, prospect.version = 5)

Arguments

param

Model parameters, in the following order: N, Cab, (Car, Cbrown), Cw, Cm, solar zenith angle, LAI, soil_moisture
prospect.version

Version of PROSPECT to use (4, 5, or '5B'; default=5)

Value

Spectra matrix (see spectra()) for wavelengths 400 to 2500nm containing the following columns:

  • alpha.c – Direct ("collimated") reflectance

  • alpha.i – Diffuse ("isotropic") reflectance

  • Tc – Direct transmittance to background

  • Ti – Diffuse reflectance to background

  • Ac – Direct absorbance by canopy

  • Ai – Diffuse absorbance by canopy

PRO4SAIL model

Description

R wrapper for PRO4SAIL model

Usage

pro4sail(param)

Arguments

param

Vector of PRO4SAIL parameter values: * N: Effective number of leaf layers (>1) * Cab: Leaf chlorophyll content (ug/cm2) (>0) * Car: Leaf carotenoid content (ug/cm2) (>0) * Cbrown: Leaf brown matter content (ug/cm2) (>0) * Cw: Leaf water content (cm) (>0) * Cm: Leaf dry matter content (ug/cm2) (>0) * LIDFa: Leaf angle distribution function - parameter a * LIDFb: Leaf angle distribution function - parameter b * TypeLIDF: Leaf angle distribution function type (1 or 2) * LAI: Leaf area index * q: Hot spot effect parameter * tts: Solar zenith angle * tto: Observer zenith angle * psi: Sun-sensor azimuth angle * psoil: Fraction of soil moisture

Value

Spectra matrix (see spectra()) (2101 x 4) of reflectance factors for wavelengths 400 to 2500nm: * bi-hemispherical reflectance (rddt) * hemispherical directional (rsdt) * directional hemispherical (rdot) * bi-directional (rsot)

Author(s)

Alexey Shiklomanov

PRO4SAILD model

Description

R wrapper for PRO4SAILD model

Usage

pro4saild(param)

Arguments

param

Vector of PRO4SAIL parameter values: * N: Effective number of leaf layers (>1) * Cab: Leaf chlorophyll content (ug/cm2) (>0) * Car: Leaf carotenoid content (ug/cm2) (>0) * Canth: Leaf anthocyanin content (ug/cm2) (>0) * Cbrown: Leaf brown matter content (ug/cm2) (>0) * Cw: Leaf water content (cm) (>0) * Cm: Leaf dry matter content (ug/cm2) (>0) * LIDFa: Leaf angle distribution function - parameter a * LIDFb: Leaf angle distribution function - parameter b * TypeLIDF: Leaf angle distribution function type (1 or 2) * LAI: Leaf area index * q: Hot spot effect parameter * tts: Solar zenith angle * tto: Observer zenith angle * psi: Sun-sensor azimuth angle * psoil: Fraction of soil moisture

Value

Spectra matrix (see spectra()) (2101 x 4) of reflectance factors for wavelengths 400 to 2500nm: * bi-hemispherical reflectance (rddt) * hemispherical directional (rsdt) * directional hemispherical (rdot) * bi-directional (rsot)

Author(s)

Alexey Shiklomanov, Shawn Serbin

PROSPECT (4, 5, or 5B) model

Description

R wrapper for PROSPECT models

Usage

prospect(param, version)

Arguments

param

Vector of PROSPECT parameter values: * N: Effective number of leaf layers (>1) * Cab: Leaf chlorophyll content (ug/cm2) (>0) * (5) Car: Leaf carotenoid content (ug/cm2) (>0) * (D) Canth: Leaf anthocyanin content (ug/cm2) (>0) * (5B, D) Cbrown: Leaf brown matter content (ug/cm2) (>0) * Cw: Leaf water content (cm) (>0) * Cm: Leaf dry matter content (ug/cm2) (>0)
version

PROSPECT version: 4, 5, or '5B'

Value

Object of class spectra (see spectra()) with simulated reflectance (column 1) and transmittance (column 2) from 400 to 2500 nm

Author(s)

Alexey Shiklomanov

Quick BayesianTools prior creator for PROSPECT model

Description

Quick BayesianTools prior creator for PROSPECT model

Usage

prospect_bt_prior(version, custom_prior = list())

Arguments

version

PROSPECT version: 4, 5, or '5B'
custom_prior

List containing param_name, distn, parama, paramb, and lower

Read and process RSR data from directory

Description

Read and process RSR data from directory

Usage

read.rsr.folder(dir.path, type)

Arguments

dir.path

Directory containing RSR data
type

Type of sensor. Options are: landsat, avhrr

Resample vector, matrix, or spectra

Description

Convenient wrapper around base R's splinefun and approxfun. See stats::splinefun() and stats::approxfun() documentation for information on different spline methods and additional arguments.

Usage

resample(values, ...)

## Default S3 method:
resample(values, from, to, method = "fmm", ...)

## S3 method for class 'matrix'
resample(values, from, to, ...)

## S3 method for class 'spectra'
resample(values, to, ...)

Arguments

values

Vector or matrix of values, or object of class spectra(). Length of vector, or nrow of matrix must match length of from. For spectra, from argument is omitted because it is taken from wavelengths.
...

Additional arguments to stats::splinefun() or stats::approxfun()
from

X values for interpolation (for spectra objects, this is assumed to be the wavelengths attribute.)
to

Y values onto which to interpolate. For spectra objects, this should be new wavelengths.
method

One of the methods for stats::splinefun() (for polynomial and periodic splines) or stats::approxfun() (for constant or linear). Default is "fmm" (same as splinefun).

Value

Object of the same class as values, resampled to the to values.

Generate relative spectral response (RSR) matrix based on FWHM data

Description

Generate relative spectral response (RSR) matrix based on FWHM data

Usage

rsr.from.fwhm(wavelength, fwhm)

Arguments

wavelength

Vector of average band widths, as reported in FWHM data.
fwhm

Vector of full-width half maximum (FWHM) bandwidths, as reported in FWHM data.

Generic log-likelihood generator for RTMs

Description

Generic log-likelihood generator for RTMs

Usage

rtm_loglike(nparams, model, observed, lag.max = NULL, verbose = TRUE, ...)

Arguments

nparams

number of parameters in model
model

function to minimize
observed

vector of observations
lag.max

passed to neff()
verbose

logical: print extra diagnostics during run?
...

additional arguments passed to model function

Random sampling from one-sided truncated normal distribution

Description

Random sampling from one-sided truncated normal distribution

Usage

rtnorm(mu, sd, MIN)

Arguments

mu

The mean parameter of the truncated normal. NOTE that this is NOT equal to the mean of the distribution
sd

The standard deviation parameter of the truncated normal. Again, NOTE tht this is not the SD of the distribution.
MIN

The minimum value, which defines the truncation.

Details

Draws a random number and, if it doesn't fall within the specified range, resample using an adjusted Normal CDF. This isn't performed immediately because CDF sampling calls three functions – qnorm, runif, and pnorm–and therefore is much less efficient than a simple random sample.

Value

Numeric length one.

Author(s)

Alexey Shiklomanov

Sensor spectral response functions

Description

Sensor spectral response functions

Usage

sensor.list

Format

An object of class character of length 12.

Sensor list with proper names

Description

Sensor list with proper names

Usage

sensor.proper

Format

An object of class character of length 12.

Setup EDR run

Description

Using an existing ED2IN file as a template, create a new ED2IN and history file configured for running EDR.

Usage

setup_edr(
  ed2in,
  output_dir,
  datetime = ISOdatetime(ed2in[["IYEARA"]], ed2in[["IMONTHA"]], ed2in[["IDATEA"]], 12, 0,
    0, tz = "UTC"),
  ...
)

Arguments

ed2in

ED2IN list object (see PEcAn.ED2::read_ed2in).
output_dir

Directory in which run files will be stored
datetime

Date time object (or compliant string) at which to run EDR. Defaults to 12 noon on start date in ED2IN.
...

Additional arguments passed on to PEcAn.ED2::modify_ed2in

Value

Path to EDR-configured ED2IN file.

Author(s)

Alexey Shiklomanov

Spectra S3 class

Description

Spectra S3 class

Usage

spectra(spectra, wavelengths = 400:2500)

is_spectra(spectra)

Arguments

spectra

Vector (length = length(wavelengths)) or matrix (ncol = length(wavelengths))
wavelengths

Wavelengths of spectra.

Functions

  • is_spectra(): Test if object is class spectra

Convolution of spectra to sensor RSR

Description

Convolution of spectra to sensor RSR

Usage

spectral.response(spec, sensor)

Arguments

spec

Full (1 nm) spectrum (vector)
sensor

Sensor name (string). See sensor.list

Structure of spectra object

Description

Structure of spectra object

Usage

## S3 method for class 'spectra'
str(spectra, ...)

Arguments

spectra

Object of class spectra
...

additional arguments, currently ignored

Multivariate normal fit

Description

Fit multivariate normal to samples. Return means and covariance matrix as a long list (for easy construction of data.tables)

Usage

summary_mvnorm(samples)

Arguments

samples

Matrix of MCMC samples.

Simple summary statistics on MCMC samples

Description

Calculate simple univariate summary statistics and return as named list

Usage

summary_simple(samples)

Arguments

samples

Matrix of MCMC samples

Trim RSR matrix to wavelength limits

Description

Trim RSR matrix to wavelength limits

Usage

trim.rsr(rsr, wl.min = 400, wl.max = 2500)

Arguments

rsr

RSR matrix
wl.min

Minimum wavelength (inclusive, default = 400)
wl.max

Maximum wavelength (inclusive, default = 2500)

Retrieve wavelengths from spectra object

Description

Retrieve wavelengths from spectra object

Usage

wavelengths(spectra)

Arguments

spectra

Object of class spectra