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Expand Up @@ -24,12 +24,11 @@ bibliography: paper.bib

# Summary

PROSPECT simulates leaf optical properties based on a limited set of light
absorbing chemical constituents, and a unique leaf structure parameter to
account for light scattering.
We present `prospect`, an R package which includes the most recent versions
of the model, complemented with multiple inversion routines to assess leaf
chemistry from leaf optical properties.
PROSPECT simulates optical properties for a leaf based on its content
in absorbing constituents, and a structure parameter accounting for light scattering.
We present `prospect`, an R package aiming at providing the most recent versions
of the model, along with inversion routines to assess leaf
chemistry from optical properties.

# Statement of need

Expand All @@ -38,69 +37,60 @@ biophysical and chemical properties is crucial to better understand ecosystem
and agrosystem functions, as well as carbon, water and energy budgets.
At leaf scale, these vegetation traits are linked to their
optical properties through absorption and scattering mechanisms.
Techniques based on spectroscopy have developed in the past decades to provide
rapid, accurate and non-destructive assessment of leaf chemical composition.
Spectroscopic techniques provide rapid, accurate and non-destructive assessment
of leaf chemical composition.
Physical models aim at simulating optical properties of leaves from their
chemical composition, and they are also used to assess chemical composition
from leaf optical properties based on inversion techniques.
chemical composition.
Inversion techniques take advantage of these models to assess leaf chemical
composition from optical properties.
The model PROSPECT (leaf optical PROperties SPECtra) is currently the most popular
physical model used for the simulation of leaf optical properties.
physical model for the simulation of leaf optical properties.
PROSPECT simulates leaf directional-hemispherical reflectance and transmittance
from a combination of chemical constituents and their corresponding specific
absorption coefficients.
It uses a simplified representation of leaf structure through the generalised
plate model [@allen1970] to account for scattering.
PROSPECT is also coupled with canopy reflectance models to analyze Earth
observation data, such as satellite imagery. Hence, it is a key component for
PROSPECT can be coupled with canopy reflectance models to analyze Earth
observation data. Hence, it is a key component for
remote sensing applications dedicated to vegetation monitoring.

Multiple versions aiming at expanding the pool of chemical constituents accounted
for by PROSPECT have been released since its first version [@jacquemoud1990].
Successive versions introduced carotenoids [@feret2008] and anthocyanins [@feret2017],
Multiple versions have been released since its first version [@jacquemoud1990].
[@feret2008] introduced carotenoids and [@feret2017] introduced anthocyanins,
to simulate leaf optical properties from juvenile to mature and senescent
development stages.
The latest version, PROSPECT-PRO, separates dry matter constituents into
proteins and carbon based constituents [@feret2021].
[@feret2021] introduced PROSPECT-PRO, the latest version separating dry matter
constituents into proteins and carbon based constituents.
In parallel with the updated versions of the model, model inversion strategies
are also developed to improve the assessment of leaf chemical constituents
were introduced to improve the assessment of leaf chemical constituents
[@feret2019; @spafford2021].

An overview of different implementations of PROSPECT since [@feret2008] is
currently maintained at [this webpage](http://teledetection.ipgp.jussieu.fr/prosail/).
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.
It also provides links to distribution corresponding to the coupling of PROSPECT
with vegetation models such as COSINE dedicated to close-range imaging
spectroscopy [@jay_physically-based_2016] and PROSAIL for canopy reflectance modeling
[verhoef_coupled_2007, @verhoef_unified_2007, @jacquemoud_prospect+_2009].
A version of this R package `prospect` coupled with the SAIL canopy model
is available in the R package [`prosail`](https://jbferet.gitlab.io/prosail/index.html).
PROSPECT is coupled with vegetation models, including COSINE dedicated to
close-range imaging spectroscopy [@jay_physically-based_2016] and SAIL
for canopy reflectance modeling [verhoef_coupled_2007, @verhoef_unified_2007, @jacquemoud_prospect+_2009].
A version of `prospect` coupled with SAIL is available in the R package
[`prosail`](https://jbferet.gitlab.io/prosail/index.html).
Note that PROSPECT and PROSAIL are also available in packages written in
[python](https://github.com/earth-chris/xleaf),
[Julia](https://github.com/RemoteSensingTools/CanopyOptics.jl) and [R](https://github.com/ashiklom/rrtm).

PROSPECT versions 4 and 5 developed by [@feret2008] are deprecated.
Therefore PROSPECT-D and PROSPECT-PRO are recommended versions.
The R package `prospect` includes recent versions of PROSPECT (D and PRO),
and aims at providing up-to-date developments in terms of future model version
and inversion strategies.
This includes parameterizable inversion routines, to ease physical model inversion
for beginners, and to help advanced users in designing and testing their own
inversion strategy.

# Overview

## PROSPECT simulation in forward mode

The application of PROSPECT in forward mode results in the simulation of leaf
directional-hemispherical reflectance and transmittance from a set of
chemical constituents and a unique leaf structure parameter usually identified as `N`
and corresponding to the number of homogeneous layers introduced in the
In forward mode, PROSPECT simulates leaf directional-hemispherical reflectance
and transmittance from a set of chemical constituents and a leaf structure
parameter identified as `N`.
`N` represents the number of homogeneous layers introduced in the
generalized plate model.

Two versions of the model PROSPECT are implemented in `prospect`: PROSPECT-D
Two versions of PROSPECT are implemented in `prospect`: PROSPECT-D
[@feret2017] and PROSPECT-PRO [@feret2021].
Table \ref{table:1} lists the leaf chemical constituents included in the
different versions which can be specified when calling the function `PROSPECT`.
PROSPECT-4 and 5 are deprecated.
Table \ref{table:1} lists the input leaf chemical constituents and structure parameter
for versions D and PRO of the function `PROSPECT`.

| Version | `D` | `PRO` |
|--------- |:----: |:----: |
Expand All @@ -112,20 +102,20 @@ different versions which can be specified when calling the function `PROSPECT`.
| LMA | **X** | |
| PROT | | **X** |
| CBC | | **X** |
| N | **X** | **X** |

: Versions of the model PROSPECT available in the `prospect` package and
corresponding chemical constituents (CHL: chlorophylls; CAR: carotenoids;
ANT: anthocyanins; BROWN: brown pigments; EWT: equivalent water thickness;
LMA: leaf mass per area; PROT: proteins; CBC: carbon based constituents).\label{table:1}
: Input variables for the two versions of PROSPECT implemented in the `prospect`
package (CHL: chlorophylls; CAR: carotenoids; ANT: anthocyanins; BROWN: brown pigments;
EWT: equivalent water thickness; LMA: leaf mass per area; PROT: proteins;
CBC: carbon based constituents; N: leaf structure parameter).\label{table:1}

As PROSPECT is a relatively simple and computationally efficient model,
iterative optimization is the most widespread method to invert PROSPECT
PROSPECT is a relatively simple and computationally efficient model.
Hence iterative optimization can be considered to invert PROSPECT
and assess leaf chemistry and structure from their optical properties.
It usually takes less than 1 second to perform PROSPECT inversion, which is acceptable
when processing experimental datasets of leaf optical properties including 100-1000 samples.
Iterative optimization aims at minimizing a merit function comparing
measured and simulated leaf optical properties.
This procedure is based on the function `fmincon` included in the package `pracma`.
It usually takes less than 1 second to perform PROSPECT inversion.

Various inversion strategies using iterative optimization are described in the
literature [@colombo_estimation_2008, @li_retrieval_2011, @feret2019].
Expand All @@ -135,13 +125,12 @@ biophysical properties, or by the introduction of prior information.
The default merit function, `Merit_PROSPECT_RMSE`, corresponds to the root mean square
of the mean quadratic difference between measured and simulated leaf optical
properties (reflectance and/or transmittance).
Users can define their own merit function, as long as their function uses the
same input and output variables as `Merit_PROSPECT_RMSE`.
The online documentation of the package provides an example of alternative merit function.
Alternative merit functions using the same input and output variables as
`Merit_PROSPECT_RMSE` can be defined.
The online documentation provides an example of alternative merit function.

Table \ref{table:2} provides information on the optimal spectral range used to
assess leaf chemical constituents from their optical properties, as identified
by [@feret2019; @spafford2021].
Table \ref{table:2} details the optimal spectral domains to assess leaf chemical
constituents from their optical properties ([@feret2019; @spafford2021]).

| Constituent| Optimal spectral domain | Versions |
|----------- |--------------------------------- |:-----------: |
Expand All @@ -154,29 +143,23 @@ by [@feret2019; @spafford2021].
| PROT | 2100 -- 2139; 2160 -- 2179 | `PRO` |
| CBC | 1480 -- 1499; 1560 -- 1579; 1760 -- 1799; 2040 -- 2059; 2120 -- 2139; 2160 -- 2239; 2260 -- 2279; 2340 -- 2359; 2380 -- 2399 | `PRO` |

: Optimal spectral domains selected to assess vegetation chemical constituents
from leaf optical properties (CHL: chlorophylls; CAR: carotenoids;
ANT: anthocyanins; BROWN: brown pigments; EWT: equivalent water thickness;
LMA: leaf mass per area; PROT: proteins; CBC: carbon based constituents).\label{table:2}
: Optimal spectral domains to assess vegetation chemical constituents
from leaf optical properties.\label{table:2}

# Example 1: running PROSPECT in forward mode

PROSPECT simulates leaf directional-hemispherical reflectance and transmittance
from the leaf structure parameter and a combination of chemical constituents
when running in forward mode.
PROSPECT relies on specific absorption coefficients corresponding to each
chemical constituent accounted for by the model, and on
the leaf refractive index.
These optical constants are identical for all leaves, and are defined over the
Input parameters defined in Table \ref{table:1} are requiredto simulate leaf
optical properties with PROSPECT.
Optical constants corresponding to specific absorption coefficients and leaf
refractive index are identical for all leaves, and are defined over the
spectral domain ranging from 400 nm to 2500 nm.
They are accessible through the variable `SpecPROSPECT_FullRange`, a data frame
which is automatically loaded with the package.
automatically loaded with the package.
Multiple functions in the package `prospect` expect a variable `SpecPROSPECT` as input.
`SpecPROSPECT_FullRange` is used as default value when this input variable is not defined.
Users can adjust `SpecPROSPECT_FullRange` in order to adjust the spectral characteristics
for simulation, including spectral domain, sampling or sensor-specicif spectral response.
`SpecPROSPECT_FullRange` is used as default value for this input variable.
Simulated spectral spectral characteristics, including spectral domain, sampling
and sensor-specific spectral response can be derived from `SpecPROSPECT_FullRange`.

These two examples illustrate how to run PROSPECT.
The function `PROSPECT` identifies the version to be used: PROSPECT-D is used
if LMA is defined, while PROSPECT-PRO is used if proteins (PROT) and carbon
based constituents (CBC) are defined.
Expand Down Expand Up @@ -215,11 +198,9 @@ LRT_VNIR <- PROSPECT(SpecPROSPECT = VNIR$SpecPROSPECT,

# Example 2: PROSPECT inversion using iterative optimization

We will illustrate different types of inversion with an experimental database
named __ANGERS__.
This database was used to calibrate the model PROSPECT, and is among the most
popular public data sets in the domain of leaf spectroscopy.
The function `download_LeafDB` allows users to directly download this database
Inversion is illustated with the experimental database __ANGERS__.
This database is a publicly available dataset used to calibrate PROSPECT.
The function `download_LeafDB` directly downloads this database
from a gitlab repository.

```r
Expand All @@ -237,7 +218,6 @@ for `SpecPROSPECT` and for the leaf optical properties.
Here the following R code aims at adjusting `SpecPROSPECT` based on the spectral
domain covered by the leaf optical properties.


```r
# Adjust spectral domain for SpecPROSPECT to fit leaf optical properties
SubData <- FitSpectralData(SpecPROSPECT = SpecPROSPECT_FullRange,
Expand All @@ -248,19 +228,17 @@ SubData <- FitSpectralData(SpecPROSPECT = SpecPROSPECT_FullRange,
```

The main inversion procedure is called with the function `Invert_PROSPECT`,
which minimizes a merit function (defined by `Merit_PROSPECT_RMSE` as default option).
which minimizes a merit function defined by `Merit_PROSPECT_RMSE` as default option.

The full set or a selection of chemical constituents can be assessed from
PROSPECT inversion.
This list of PROSPECT parameters assess from PROSPECT inversion is defined with
the variable `Parms2Estimate`.
Part or all parameters defined in Table \ref{table:1} can be assessed.
Parameters assess from PROSPECT inversion are defined with a list defined
a input in `Parms2Estimate`.
The default parameterization of PROSPECT inversion assesses all parameters,
including the N structure parameter and all chemical constituents listed in
Table \ref{table:1} except BROWN.
BROWN which can also be assessed by setting `Est_Brown_Pigments = TRUE` as
input for `Invert_PROSPECT`.
The value set for parameters which are not assessed is then defined with
the `InitValues` input variable.
The value of unassessed parameters is then defined in `InitValues`.

```r
# Assess all parameters using PROSPECT inversion applied to full spectral data
Expand All @@ -272,10 +250,10 @@ res_all_WL <- Invert_PROSPECT(SpecPROSPECT = SubData$SpecPROSPECT,
## PROSPECT inversion using optimal spectral domains for each constituent

The function `Invert_PROSPECT_OPT` performs PROSPECT inversion using optimal
spectral domains defined in [@feret2019; @spafford2021].
The optimal spectral domains are specific to each constituent, and the function
automatically adjusts the spectral domain of the leaf optical properties provided
by user.
spectral domains ([@feret2019; @spafford2021]).
These domains are specific to each constituent.
The function automatically adjusts the spectral domain of the leaf optical
properties provided as input.

```r
# Assess a set of parameters using PROSPECT inversion with optimal spectral domains
Expand All @@ -296,21 +274,19 @@ leaf chemical constituent.

# Conclusion

We have described `prospect`, an R package dedicated to the PROSPECT leaf model.
`prospect` can run different versions of the model in direct mode to simulate
We introduce `prospect`, an R package dedicated to the PROSPECT leaf model.
`prospect` runs various versions of the model in direct mode to simulate
directional-hemispherical reflectance and transmittance.
`prospect` also includes inversion routines to assess leaf structure and chemical
constituent content either from directional-hemispherical reflectance and
transmittance, or from reflectance or transmittance only.
It also includes inversion routines to assess leaf structure and chemical
constituent content either from reflectance and/or transmittance.
`prospect` provides latest advances in terms of model version and inversion
procedures to the leaf spectroscopy community.
`prospect` is coupled with the canopy model SAIL through the R package
[`prosail`](https://jbferet.gitlab.io/prosail/index.html).
`prosail` is dedicated to applications focusing on Earth observation imagery analysis
and allows simulation of canopy reflectance for multispectral and hyperspectral sensors.
Hybrid inversions based on physical modeling and machine learning are also implemented
in `prosail` to assess vegetation traits from imagery data.

in `prosail` to assess vegetation traits at canopy scale from imagery data.

# Availability

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