# Enhancing PROSAIL Inversion: Key Considerations and Practical Insights

Vitória Barbosa Ferreira, Jean-Baptiste Féret, Clement Atzberger, Joannès Guillemot, Otavio Campoe, Guerric Le Maire

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Vitória Barbosa Ferreira, Jean-Baptiste Féret, Clement Atzberger, Joannès Guillemot, Otavio Campoe, et al.. Enhancing PROSAIL Inversion: Key Considerations and Practical Insights. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 19, pp.1378 - 1390. 10.1109/jstars.2025.3635001. hal-05504167

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# Enhancing PROSAIL Inversion: Key Considerations and Practical Insights

Vitória Barbosa Ferreira ID, *Graduate Student Member, IEEE*, Jean-Baptiste Féret ID, Clement Atzberger ID, Joannès Guillemot ID, Otávio Campoe ID, and Guerric le Maire ID

**Abstract**—Accurate estimation of plant biophysical properties is essential for understanding ecosystem functioning, monitoring forest health, productivity, and assessing the risk of disturbances. However, retrieval accuracy is often constrained by uncertainties in model parameterization, soil background effects, and spectral redundancy. This study systematically evaluates the influence of parameter range, band selection, and soil background on the inversion of leaf area index, chlorophyll content, leaf mass per area, and equivalent water thickness in *Eucalyptus* plantations throughout an entire growing season. Retrievals were conducted by inverting the PROSAIL model. Our approach led to three innovative findings: 1) the integration of trait-informed parameter ranges, tailored to *Eucalyptus* plantations, which substantially improves retrieval accuracy, especially for leaf mass per area; 2) the demonstration that band selection can reduce root mean square error by up 58% for Chlorophyll content, 52% leaf mass per area and 69% for equivalent water thickness; and 3) the performance of soil background datasets strongly depends on the sampling strategy used and whether band selection was applied. These findings highlight that combining spectral optimization with contextual prior knowledge provides a robust framework for improving PROSAIL inversion in forest ecosystems, advancing remote sensing applications for plantation monitoring and sustainable management of natural resources. Moreover, this work presents a rarely explored application of PROSAIL inversion to forest plantations, extending its use beyond crop studies.

**Index Terms**—Biophysical properties retrieval, PROSAIL, radiative transfer model (RTM) inversion.

Received 8 July 2025; revised 31 October 2025; accepted 11 November 2025. Date of publication 20 November 2025; date of current version 16 December 2025. The work was supported in part by the PNTS under Grant PNTS-2020-14, in part by the CNES under the TO S CA project SYLVOSANSAT, in part by the Agence Nationale de la Recherche (ANR) through the BioCop project (ANR-17-CE32-0001-01), and in part by the government funding managed by ANR under the France 2030 program as part of the "Forest Resilience" research program (PEPR FORESTT), Projet ciblé MONITOR, reference number ANR-24-PEFO-0003. The work of V. B. Ferreira was supported in part by the French Space Agency (CNES) and in part by the French Agricultural Research Centre for International Development (CIRAD). (*Corresponding author:* Vitória Barbosa Ferreira.)

Vitória Barbosa Ferreira, Joannès Guillemot, and Guerric le Maire are with CIRAD, ECO&SOLS, INRAE, IRD, Institut Agro, Université Montpellier, 34090 Montpellier, France (e-mail: ferreiravitoriabarbosa@gmail.com; joannes.guillemot@cirad.fr; guerric.le_maire@cirad.fr).

Jean-Baptiste Féret is with TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Université Montpellier, 34090 Montpellier, France (e-mail: jean-baptiste.feret@teledetection.fr).

Clement Atzberger is with dClimate Labs, New York, NY 10022 USA (e-mail: clement@cyclops.ai).

Otávio Campoe is with the Department of Forestry Sciences, Federal University of Lavras, Lavras 37203-202, Brazil (e-mail: otavio.campoe@ufla.br).

This article has supplementary downloadable material available at https://doi.org/10.1109/JSTARS.2025.3635001, provided by the authors.

Digital Object Identifier 10.1109/JSTARS.2025.3635001

## I. INTRODUCTION

MONITORING forest biophysical properties over space and time is crucial for understanding how forest ecosystems adapt to increasingly warmer, drier, and more volatile scenarios [1], [2]. Large-scale information on vegetation biophysical characteristics, such as leaf area index (LAI), leaf Chlorophyll a and b content (Cab, µg/cm²), leaf mass per area (LMA, g/m²), and equivalent water thickness (EWT, g/m²), provides insights into forest health and productivity. As a result of their (bio)physical foundation, they can also be readily incorporated into models evaluating forest water and carbon cycles [3], [4] forest growth [5], and assessing the impact of fire behavior [6], among other applications. Physically based methods are among the most efficient and generic approaches for retrieving vegetation properties. Such techniques rely on radiative transfer models (RTMs) that describe the interactions between electromagnetic radiation and matter, allowing for the simulation of the optical properties of a medium or surface [7] (e.g., vegetation canopy). These simulations can subsequently be used to estimate key plant characteristics (e.g., through inversion of RTMs), such as LAI, Cab, LMA, and EWT. PROSAIL [8] (a coupling of the leaf RTM PROSPECT [7] and the canopy RTM SAIL [9]) is one of the most widely used RTMs to retrieve vegetation biophysical properties. Nevertheless, PROSAIL inversion is highly sensitive to assumptions regarding prior parameter distributions, spectral band selection, soil background, simulation noise, and other factors, which can substantially affect the retrieval of targeted biophysical variables.

Prior parameter distributions play a central role, directly affecting how PROSAIL handles the inversion problem's ill-posed nature. In most real-world applications, the inversion of PROSAIL is an ill-posed problem. A problem is ill-posed if one or more of the following statements do not hold: the solution to the problem must exist, be unique, and be continuously dependent on the data [10], [11]. The inversion of a canopy RTM, such as PROSAIL, is considered an ill-posed problem because different combinations of input variables can yield nearly identical spectral responses [10]. Several studies have addressed the ill-posed issue by using various regularization techniques for inversion, such as prior information to minimize the range of variation for specific input variables [10], [12], [13], [14]. Field measurements are considered one of the most valuable sources of prior knowledge [10], but their availability is limited. Zurita-Milla et al. [12] argue that the more we can constrain the
range of input parameters in vegetation RTMs, the higher the reliability and accuracy of the inversion results will be. However, most studies demonstrating that prior knowledge improves the retrieval accuracy of biophysical variables typically have access to limited field data, often restricted to two to three vegetation characteristics (e.g., ground measurements of LAI, Cab, and EWT). In addition, these measurements typically encompass a single image acquisition, rather than an entire season or rotation [13], [14], [15]. It is therefore essential to evaluate the performance of PROSAIL inversion using the most comprehensive datasets available.

Band selection is an essential factor in RTM inversion. First, each biophysical variable is influenced by specific regions of the reflected spectra rather than the full spectrum [16]. Including less relevant spectral information can introduce noise, leading to less accurate retrieval. Second, some spectral bands might be poorly modeled by PROSAIL [17], which can introduce errors and variability, potentially compromising the accuracy of the estimation. Indeed, the identification of the optimal spectral subdomains or bands used to invert the RTM has proven to be a critical step at the leaf scale, notably for retrieving LMA and EWT [18], [19], [20], [39], [40], [41] and at the canopy scale [17], [21], [22], [23], [24]. Meroni et al. [22] and Ravi et al. [25] highlight the absence of a universal criterion/method for band selection, mainly because the subset of optimal bands can vary depending on the specific criterion used for selection, making it challenging to establish a general guideline [25]. In summary, performing band selection is a crucial step in improving the accuracy of inversion retrievals, as it reduces band redundancy and unnecessary noise in the training data.

Background reflectance is another possible confounding factor for which prior information is highly desirable. The influence of such information on the retrieval accuracy of vegetation parameters is discussed in studies [26] and [27]. As canopy reflectance is highly influenced by the soil or understory background, the reflectance of the soil background is a key input for canopy reflectance models, such as SAIL [28]. Jiang et al. [26] demonstrated that soil reflectance strongly influenced simulated vegetation reflectance, especially when the vegetation canopy was sparse. As a result, the retrieval accuracy of LAI varied significantly across different soil backgrounds. Nonetheless, despite its non-negligible influence on canopy reflectance, few studies have examined the effect of employing different soil reflectance on PROSAIL inversion [26], [29], [30], [31], [32]. Therefore, investigating how various soil reflectance schemes affect the accuracy of vegetation feature retrieval is essential.

Previous studies investigated the influence of PROSAIL input parameters, band selection, and soil background separately [12], [24], [26]. In contrast, this study systematically examines the individual and combined effects of narrowing the range of PROSAIL input parameters, applying band selection, and using different soil background datasets. Our objectives are to identify the optimal configuration for retrieving LAI, Cab, LMA, and EWT and to provide insights into how each factor contributes to retrieval performance. By addressing the combined influence of these factors, which has scarcely been systematically evaluated before, this study aims to improve the accuracy and reliability of RTM-based inversion approaches.

## II. MATERIALS AND METHODS

### A. Ground Measurements

The ground data used in this study were acquired at the EU-CFLUX site [see Fig. 1(a)], located in Itatinga, São Paulo, Brazil (22°58'04''S, 48°43'40''W). The study area comprises 268 ha of *Eucalyptus* plantations. The study design consists of five blocks [see Fig. 1(b)], each containing 25 *Eucalyptus* genotypes planted in November 2018. The genotypes were grown in lines at a density of 1666 trees per hectare, each on a plot of 36 m x 32 m in the grid [33]. In addition, the experiment consisted of four plots surrounding a flux-tower observatory, where a single genotype (G12) was planted.

Le Maire et al. [33] comprehensively describe the measurement of biophysical and biochemical variables, including LAI, Cab, LMA, EWT, and average leaf angle (ALA, °). This dataset is hereafter referred to as the EUCFLUX dataset. Destructive measurements were performed regularly in the experimental block and the flux tower inventory plots (see Fig. 1). First, six to ten *Eucalyptus* trees per genotype were felled along the plot borders, while the central 100 trees were left intact. Next, the canopy of each fallen tree was divided into three equal vertical thirds (top, middle, and bottom). Leaf angles were measured using a clinometer on 24 randomly selected leaves from the three canopy thirds. To do so, each section was held upright to match the position of the canopy before it fell. In the sequence, 20 leaves were randomly removed from each third of the canopy (hereafter referred to as leaf samples).

The leaf samples were fresh-weighted in the field, preserved in cold storage, and sent to the laboratory, where their area and dry weight were measured. LMA (g/m²) was determined as the ratio between the sample dry mass and leaf area per third (1). EWT (g/m²) was calculated by subtracting the sample dry weight from the fresh weight and dividing the result by the sample area, per third (2)

$$LMA = \frac{DW}{A} \qquad (1)$$

$$EWT = \frac{FW - DW}{A} \qquad (2)$$

where DW (g) is the sample dry weight, A (m²) is the sample leaf area, and FW (g) is the sample fresh weight. Cab, LMA, and EWT were calculated for the three canopy thirds.

SPAD values were measured from the leaf samples using the SPAD-502 Plus meter [34]. To accurately estimate chlorophyll content (Cab, µg/cm²) from SPAD (unitless) measurements, a regression model was calibrated for *Eucalyptus* using the ITATINGA dataset [18]. This dataset includes 415 leaves from 16 genotypes at different developmental stages and crown positions acquired in 2015 in our experimental site (see Fig. 1). The chlorophyll content of these leaves was measured in the laboratory following the protocol described in study [35]. Linear, exponential, and second-degree polynomial equations were tested to relate SPAD values to laboratory-measured chlorophyll content, as done in study [36]. Model performance was evaluated using tenfold cross-validation, considering root mean square error (RMSE), R², and the akaike information criterion (AIC) [37]. The second-degree polynomial model provided the
<figure><img src="image_2.png" /><figcaption>Fig. 1. Study area. (a) Study site in Itatinga, Sao Paulo, Brazil, indicates the location of five genotype blocks where 25 genotypes are planted in 25 plots of 36x32 m, and four flux tower inventory plots, where a single genotype (G12) is planted. The background image source is Esri (CRS: EPSG:4326, WGS 84). (b) One of the blocks, the southern one, containing 25 genotypes (G1–G25). The green dots indicate the planting grid.</figcaption></figure>

best predictive performance, exhibiting the lowest AIC, RMSE, and R² among the tested equations. The resulting calibrated (3) was applied to estimate chlorophyll content from SPAD measurements in the current study. Although new genotypes were introduced in the current rotation, the model is expected to perform reliably for most cases

$$Cab = 0.772 + (0.204 * SPAD_i) + (0.0150 * SPAD_i^2) \quad (3)$$

where Cab stands for chlorophyll a+b content (µg/cm²), and SPAD stands for a proxy measurement of leaf chlorophyll content obtained via a SPAD meter.

Additionally, every leaf from each tree was removed and weighed in the field. Using the LMA and EWT calculated in (3) and (1), the leaf fresh weight of each crown section was converted to leaf area (m²). The total leaf area (LA, m²) per tree was calculated by summing the leaf area of each crown section. LAI was calculated using age-genotype-specific allometric equations between the tree diameter and/or tree height and the leaf area of the felt trees as specified in Le Maire et al. [33]. We discarded observations from plots with tree mortality higher than 30%. The total number of observations was 570 for LAI and 230 for Cab, LMA, and EWT. More LAI values were available because they were estimated for all Genotype test blocks using allometric relationships, whereas the other variables were only obtained from the measured block. Fig. 2 illustrates the biophysical variables LAI, Cab, LMA, and EWT measured in the field.

The ITATINGA dataset was used to estimate other leaf biochemical and physical characteristics, such as leaf carotenoid content (Cxc), leaf anthocyanin content (Canth), leaf brown pigment content (Cbp), and the leaf structure parameter N, all obtained from PROSPECT model inversion on leaf optical properties (see description in Section II-C). Directional-hemispherical reflectance and transmittance measurements were acquired using an ASD FieldSpec Pro, which provided a 1 nm spectral sampling range from 400 to 2500 nm [18]. To obtain

<figure><img src="image_3.png" /><figcaption>Fig. 2. Biophysical variables measured from 2019 to 2023. The colors indicate each of the 25 Eucalyptus genotypes (G1–G25). LAI stands for leaf area index, Cab for chlorophyll content, LMA for leaf mass per area, and EWT for equivalent water thickness.</figcaption></figure>

Cxc, Canth, Cbc, and N, PROSPECT-D inversion was performed using iterative optimization [20], [38].

## B. Remote Sensing Data

The remotely sensed data consists of Sentinel-2 (S2A and S2B, hereafter referred to as S2) observations obtained from March 2019 to May 2023. A total of 24 Sentinel-2 images were acquired between March 2019 and May 2023, distributed across the Brazilian seasons as follows: 1 in summer, 10 in autumn, 6 in winter, and 7 in spring. The S2 bands used in this study were B3 to B8 at 10 m spatial resolutions, and B8A, B11, and B12 at 20 m resolution. In addition to the spectral bands, data on the
<figure><img src="image_4.png" /><figcaption>Fig. 3. Study's framework, including the preprocessing of remote sensing data and the experimental design. The connecting orange lines represent the 12 configurations tested in this study, as detailed in the text. BV: biophysical variables, LaSRC: Land Surface Reflectance Code, S2: Sentinel-2, SVR: Support Vector Regression.</figcaption></figure>

viewing and illumination geometry (sun zenith, sun azimuth, view zenith, and view azimuth angles) were also acquired from the S2 metadata. Image preprocessing included atmospheric corrections and cloud filtering. Following the download of Level-1C tiles (MSIL2A), the atmospheric correction was done using the land surface reflectance code (LaSRC) [39]. To detect clouds and generate cloud and cloud shadow masks, we used the FMask algorithm [40] and the S2cloudless algorithm [41]. A restrictive cloud mask thresholding was adopted to minimize the possibility of classifying thin haze as a noncloudy pixel. We selected the closest noncloudy images acquired within a window of ±20 days from the date of the field measurement. Notably, 97.8% of the images were captured within 5 days of the field campaigns, while only 2.2% fell within the 10–20 day range. Pixel selection was performed as follows: we considered an internal polygon covering only the 100 central trees of each plot (i.e., 600 m²). Then, the Sentinel-2 pixel providing the maximum plot coverage was selected. In cases where more than one pixel achieved full (100%) coverage, the average value of the two pixels was used. Both 10 and 20 m pixels were extracted at their native resolution using that same rule. Fig. 3 summarizes the preprocessing steps.

### C. Experimental Design

The following sections describe the organization of the experiment. First, we present the PROSAIL model, followed by the sampling strategy used to generate the simulated datasets, the soil background reflectance datasets, the PROSAIL inversion procedure and the band selection strategy. We then outline how the different configurations were tested, and how model performance was evaluated. Fig. 3 summarizes each of the experimental design steps thoroughly detailed in the following sections.

1) PROSAIL Model: The coupling of the leaf optical properties model, PROSPECT, with the canopy bidirectional reflectance model, SAIL, is popularly referred to as PROSAIL [8]. PROSPECT simulates leaf directional-hemispherical reflectance and transmittance from 400 to 2500 nm, at 1 nm intervals, as a function of a limited number of input variables [20]. The model assumes a relatively simple representation of

leaves as proposed in the generalized plate model by Allen et al. [42]: leaves are represented as a pile of N homogeneous plates separated by N-1 air spaces, N being a real number. The generalized plate model simulates leaf directional-hemispherical reflectance and transmittance as a function of the leaf structure parameter (N); the known specific absorption coefficients, which describe absorption at a particular wavelength from constituents expressed in unit mass per unit area; and the refractive index, which is a measure of how much light refracts at the air-cell interface defined by the N parameter [7], [8], [43]. The N parameter is a theoretical value representing the number of internal cell wall-air interfaces in the mesophyll. It accounts for the internal leaf structure, specifically the density of leaf layers, which affects light scattering within the leaf. PROSPECT model versions have evolved to represent leaf biophysical constituents more precisely. Early versions, such as PROSPECT-5, improved the discrimination of photosynthetic pigments by distinguishing total chlorophylls and total carotenoids, allowing for more accurate pigment quantification in leaves [43]. PROSPECT-D further advanced the model by explicitly including anthocyanins and recalibrating the refractive index of several biochemical constituents, enhancing the representation of pigment diversity and leaf optical properties [44]. The latest version, PROSPECT-PRO, explicitly separates nitrogen-based proteins from carbon-based constituents (cellulose, lignin, hemi-cellulose, starch), which are critical for understanding plant physiology and nutrient cycling [45].

The SAIL model was developed in the early 1980s by Verhoef [9] and is based on the one-dimensional model by Suits [46]. The SAIL model uses a four-stream radiative modeling theory to describe the interaction of radiation with vegetation canopies. It involves the classification of four fluxes: two direct and two diffuse fluxes [47]. The direct fluxes are the incident solar flux, which corresponds to the direct solar irradiance incident on a horizontal plane, and the radiance in the viewing direction, which corresponds to the radiant energy reflected in the observer's direction [47]. The diffuse fluxes are hemispherical diffuse upward and downward irradiance, which correspond to the diffuse sunlight coming from all directions above the canopy and reaching a specific point within the canopy, as well as the
<table><caption>TABLE I
RANGE AND DISTRIBUTION OF BIOPHYSICAL AND MEASUREMENT VARIABLES USED TO GENERATE THE INPUT PARAMETERS FOR THE PROSAIL MODEL</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Abbrev</th><th rowspan="2">Description</th><th rowspan="2">Unit</th><th rowspan="2">Distribution</th><th colspan="3">ATBD</th><th colspan="3">SIMEUCFLUX</th></tr><tr><th>Range</th><th>Average</th><th>SD</th><th>Distribution</th><th>Range</th><th>Average</th><th>SD</th></tr></thead><tbody><tr><td rowspan="8">PROSPECT-D</td><td>Canth</td><td>Anthocyanins</td><td>µg cm<sup>-2</sup></td><td>-</td><td>0</td><td>0</td><td>0</td><td>Gaussian</td><td>0.37-9.49</td><td>4.36</td><td>1.88</td></tr><tr><td>Cxc</td><td>Carotenoids</td><td>µg cm<sup>-2</sup></td><td>Gaussian</td><td>5.32-22.45</td><td>13.62</td><td>4.22</td><td>Gaussian</td><td>0.2-21.75</td><td>11.37</td><td>4.88</td></tr><tr><td>Cbc</td><td>Brown pigments</td><td>µg cm<sup>-2</sup></td><td>Gaussian</td><td>0-0.97</td><td>0.19</td><td>0.15</td><td>Gaussian</td><td>0.01-0.5</td><td>0.25</td><td>0.14</td></tr><tr><td>Cab</td><td>Chlorophyll a and b</td><td>µg cm<sup>-2</sup></td><td>Gaussian</td><td>21.29-89.81</td><td>54.51</td><td>16.92</td><td>Gaussian</td><td>25.11-67.93</td><td>45.29</td><td>11.4</td></tr><tr><td>EWT*</td><td>Equivalent Water Thickness</td><td>g m<sup>-2</sup></td><td>Gaussian</td><td>58.02-555.11</td><td>205.94</td><td>85.61</td><td>Gaussian</td><td>96.25-230.77</td><td>163.72</td><td>36.95</td></tr><tr><td>LMA</td><td>Leaf Mass per Area</td><td>g m<sup>-2</sup></td><td>Gaussian</td><td>30.64-109.95</td><td>67.76</td><td>20.64</td><td>Gaussian</td><td>54.25-182.92</td><td>115.86</td><td>33.40</td></tr><tr><td>N</td><td>Leaf structure parameter</td><td>-</td><td>Gaussian</td><td>1.2-1.8</td><td>1.51</td><td>0.51</td><td>Gaussian</td><td>1.05-1.8</td><td>1.41</td><td>0.21</td></tr><tr><td>ALA</td><td>Average Leaf Angle</td><td>Deg</td><td>Gaussian</td><td>30.67-79.85</td><td>56.88</td><td>11.41</td><td>Gaussian</td><td>25.32-84.66</td><td>53.03</td><td>15.23</td></tr><tr><td rowspan="6">4SAIL</td><td>LAI</td><td>Leaf Area Index</td><td>-</td><td>Gaussian</td><td>0-12.75</td><td>3.23</td><td>2.18</td><td>Gaussian</td><td>0.05-7.48</td><td>2.74</td><td>1.24</td></tr><tr><td>H</td><td>Hot Spot parameter</td><td>-</td><td>Gaussian</td><td>0.1-0.5</td><td>0.29</td><td>0.11</td><td>Uniform</td><td>0.1-0.5</td><td>-</td><td>-</td></tr><tr><td>psoil</td><td>Soil brightness</td><td>%</td><td>-</td><td>0-1</td><td>-</td><td>-</td><td>-</td><td>0-1</td><td>-</td><td>-</td></tr><tr><td>SZA</td><td>Sun Zenith Angle</td><td>Deg</td><td>Uniform</td><td>21.46-53.29</td><td>-</td><td>-</td><td>Uniform</td><td>21.46-53.29</td><td>-</td><td>-</td></tr><tr><td>VZA</td><td>View Zenith Angle</td><td>Deg</td><td>Uniform</td><td>2.66-2.74</td><td>-</td><td>-</td><td>Uniform</td><td>2.66-2.74</td><td>-</td><td>-</td></tr><tr><td>RAA</td><td>Relative Azimuth Angle</td><td>Deg</td><td>Uniform</td><td>62.67-116.94</td><td>-</td><td>-</td><td>Uniform</td><td>62.67-116.94</td><td>-</td><td>-</td></tr></tbody><tfoot><tr><td colspan="12">*Note: *In the ATBD [50], water content is referred as Cw rel (canopy water content). Within the prosail R package model [49], Cw rel is converted into equivalent water thickness (EWT) based on the leaf's dry mass per unit area (LMA) and its relative water fraction, such that EWT represents the additional mass of water contained in the leaf per unit area beyond its dry mass. ATBD corresponds to a standard proposed by Weiss et al. [50], and SIMEUCFLUX corresponds to measured biophysical variables from Eucalyptus plantations.</td></tr></tfoot></table>

sunlight reflected upward from the canopy, respectively [47]. The interaction of these fluxes within the canopy is modeled by a system of linear differential equations that are solved analytically. This model takes into account multiple factors that influence the reflection and transmission of light through the canopy, including the atmosphere, leaf orientation, soil background, and canopy architecture. Several versions of SAIL have been proposed over the years to better account for additional factors influencing canopy reflectance and transmission. This article uses the 4SAIL model version [28].

The coupling of PROSPECT and SAIL models simulates canopy bidirectional reflectance using inputs from both models: for PROSPECT, the input parameters are leaf pigments (e.g., Cab), LMA, EWT, and N; for SAIL, the input parameters are LAI, ALA, the hot-spot parameter (*h*), which is the ratio of leaf size over canopy height [48]; geometry of acquisition including solar zenith angle (SZA), solar azimuth angle (SAA), viewing zenith angle (VZA), and relative azimuth angle (RAA); soil background reflectance, and a scaling soil factor. PROSAIL outputs the simulated bidirectional reflectance factor (BRF) across the 400–2500 nm spectral range in 1 nm intervals in forward mode. We used the PROSAIL implementation in the *prosail* R package version 2.5.8 [49].

2) *Sampling Strategy:* In this study, we have tested two sampling strategies to generate the simulated datasets. These simulated spectra, referred to as the “BRF LUT” (BRF Lookup Table), are created by randomly sampling each PROSAIL input parameter from predefined distributions (see Table I). This section outlines the two distribution configurations tested in this study.

The default input PROSAIL parameter distribution adopted in the R package *prosail* reproduces the sampling strategy described in the algorithm theoretical based document (ATBD) for the biophysical processor of the Level 2 Sentinel-2 products implemented in the Sentinel-2 toolbox (S2ToolBox) [49], [50]. The ATBD parameter distributions were defined to create a BRF LUT that accurately represents the global distribution

of vegetation types across the Earth's surface [50]. The ATBD's range and distributions (mostly truncated Gaussian) of PROSAIL input parameters are derived from a compilation of experimental data described in the literature. In addition, it includes possible codistribution between variables by restricting the range of variation of some parameters as a function of LAI (e.g., high LAI values are never associated with low Cab and planophiles leaf inclination). The parameter combinations are randomly generated within the distributions and rules reported in Table I. The ATBD sampling strategy was generated using an older PROSPECT version, which did not include Anthocyanins (Canth). Consequently, this parameter was set to zero (see Table I).

The SIMEUCFLUX input parameter distributions for PROSAIL were derived from the EUCFLUX field dataset and the inversion of PROSPECT using the local ITATINGA dataset (see description in Section II-A). The field measurements include all measurements of LAI, Cab, LMA, EWT, and ALA, while the inverted values are Carotenoids (Cxc), Canth, Brown pigments (Cbc), and N. The upper and lower bounds of each parameter distribution were defined as the measured maximum and minimum values, respectively, extended by one or two standard deviations, with non-negative constraints, to account for natural variability not fully captured by the sample (see Table I). The SIMEUCFLUX sampling strategy was based on PROSPECT-D, a later version of PROSPECT that includes Canth, which were set according to values derived from PROSPECT leaf model inversion applied to the ITATINGA leaf dataset.

3) *Background Reflectance:* The BRF LUT generated with PROSAIL accounts for the variability of soil background reflectance as a weighted average of two extreme reflectances: one corresponding to a dark soil for the minimum reflectance and the other to a bright soil for the maximum reflectance. The PROSAIL psoil input parameter determines the weighting, which ranges from 0 to 1. This section describes the three types of background reflectance datasets (i.e., three pairs of minimum and maximum reflectance) tested in this study.
The DEFAULT background reflectance is provided in the default implementation in the R package prosail [49]. The prosail package uses soil reflectance at two configurations, corresponding to a bright and dry soil (upper soil reflectance bound) and a dark, wet soil (lower bound), to represent the variability of soil reflectances under different soil type properties. The soil background reflectance was resampled to match the S2 sensor spectral response function, as defined by study [51].

The FIELDSPEC background reflectance was acquired using a field spectroradiometer (ASD FieldSpec Pro) spanning from 350 to 2500 nm at 1 nm intervals. Forest litter and soil reflectance were measured in situ in 2011 at 110 locations across different genotypes (blocks illustrated in Fig. 1). These measurements included the natural variability of illumination, the proportion of litter (dry leaves and branches), and the soil. We then calculated the average and standard deviation for each wavelength of the 110 spectra. Since we did not directly measure drier and wetter soils, we account for a higher soil reflectance variability in experimental conditions by adding or subtracting 1.5 times the standard deviation to the mean soil reflectance at each wavelength. These values defined an upper and lower bound for each wavelength. In addition, the soil background reflectance was resampled to match the S2 sensor spectral response function, as defined by study [51] (see Supplementary Material I).

The third background reflectance dataset, Sentinel-2-based soil reflectance dataset (S2SOIL), was derived from Sentinel-2 imagery. It represents the forest floor reflectance (i.e., litter and soil reflectance) acquired at the study site between the harvest of the previous rotation (July 2018) and the planting of the current rotation (November 2018), a period when no trees were present, allowing the forest floor (soil and litter) to be visible in the satellite images. Eight cloud-free Sentinel-2 images were selected for the study period, acquired on 13, 18, and 28 August 2018; 7, 12, and 22 September 2018; and 17 and 22 October 2018. The variability observed in reflectance was induced by the spatial variability among the different genotypes and the temporal variability, which includes variations in soil moisture content. Therefore, the upper bounds were determined by taking the maximum value measured by S2 at each wavelength, while the lower bounds were determined by taking the minimum value. Fig. 4 displays the upper and lower bounds of the three soil background datasets tested in the present study.

4) Model Inversion: Multiple strategies that leverage physical modeling can be applied to estimate vegetation properties from remote sensing data [52]. RTM inversion using machine learning is often preferred for applications involving large datasets, such as imagery, due to the computational efficiency of machine learning regression models. This family of methods typically runs an RTM to build a BRF LUT representing a broad range of canopy biophysical, biochemical, and structural variable combinations (as described in Section II-D), along with their corresponding simulated spectra. In the sequence, the BRF LUT is used as input to train a machine-learning algorithm [53]. Several reasons justify the popularity of machine learning inversion approaches, including the potential to combine the generalization capabilities of RTMs with the flexibility, robustness, and computational efficiency of machine learning methods and their suitability for handling high-dimensional data [54].

<figure><img src="image_5.png" /><figcaption>Fig. 4. Three soil background datasets' Upper and lower reflectance bounds. The wavelength axis spans 400–2500 nm to show the position of Sentinel-2A bands within the broader spectral domain, providing context. For visualization, reflectances are plotted only at the central wavelengths of Sentinel-2A bands, with vertical bars indicating the bandwidth and central wavelength of each band.</figcaption></figure>

Once the input PROSAIL parameters were generated, we simulated bidirectional reflectance factors (BRFs) corresponding to each parameter combination, randomly selected from their respective distributions (see Table I). The simulated BRF spans from 400 to 2500 nm in 1 nm increments. Next, it was resampled to match the S2 sensor spectral response function, as defined by study [51]. Band 2 (centered at 494.4 nm) was discarded due to its higher uncertainty in the satellite images, primarily attributed to atmospheric interference. Next, we applied multiplicative Gaussian noise (mean of 0 and a standard deviation of 2% reflectance) to the simulated sensor BRF. The noise is applied because training machine learning models on non-noisy simulated data can easily lead to overfitting on simulated data and poor retrieval accuracy on experimental data, as the model may learn patterns that do not generalize well to real-world scenarios [55].

The machine learning strategy implemented in the R package prosail [49] consists of a parsimonious ensemble method based on a bootstrap aggregating (bagging) prediction of biophysical properties from multiple radial basis function (RBF) support vector regression (SVR) models. Each SVR model was trained with limited samples (100), ensuring a fast training stage. The final predicted values for each target biophysical variable are obtained by averaging the predictions across the ensemble of SVR models. The default parameterization employs an ensemble of 20 SVR models, corresponding to 2000 training samples, substantially fewer than the 41 472 cases used for training the artificial neural networks in the Sentinel-2 toolbox [50]. Despite this smaller sample size, the ensemble design ensures stability and generalization through the random resampling of training subsets and the internal k-fold cross-validation performed during hyperparameter tuning by the liquidSVM R package [56]. Additional tests were conducted using different training sample sizes, and the results (not shown) demonstrated consistent retrieval performance and agreement with the SNAP processor outputs, confirming the robustness of this parsimonious ensemble approach. Consequently, this lightweight inversion framework,
designed for research purposes, achieves comparable retrieval accuracy to the Sentinel-2 toolbox while substantially reducing computational demand and enabling rapid experimentation with alternative sampling and inversion strategies.

The nine simulated S2 bands of the BRF LUT were used as input features, and the corresponding sampled LAI, Cab, LMA, and EWT were used separately as target variables during the training stage. The SVR models trained on simulated data were then applied to experimental S2 reflectance data to predict the values of biophysical variables (see Fig. 3).

5) Band Selection: The backward stepwise band selection is a feature reduction method that starts with all available features and iteratively removes the least important one. The PROSAIL inversion was executed with all bands, and then one of the bands was iteratively removed from the inversion. The selection was based on the concordance correlation coefficient (CCC) (4) [57], which measures the agreement between observed and predicted values by combining both their correlation and accuracy with respect to the 1:1 line. At each iteration, the PROSAIL inversion was performed while sequentially removing one spectral band. The CCC was computed for each configuration to assess the impact of removing each band on model performance. The band whose removal resulted in the highest CCC was considered the least informative and therefore eliminated. This procedure was repeated iteratively, removing one band at a time, until the optimal subset of bands was identified. The final band combination yielding the maximum CCC was retained for further analysis. In contrast, subsets with fewer than three bands were excluded to ensure that the retained spectral information represented canopy reflectance characteristics.

Both forward and backward stepwise selection methods were tested during preliminary analyses. As the results were globally similar and the objective of this study was not to benchmark selection algorithms but rather to assess the impact of band selection on PROSAIL inversion performance, only the backward selection results are presented here for simplicity

$$ \mathrm{CCC} = \frac{2r\sigma_{\hat{y}}\sigma_y}{\sigma_{y^2} + \sigma_{\hat{y}}^2 + (\bar{y} - \bar{\hat{y}})^2} \quad (4) $$

where r is the Pearson correlation coefficient between observed and predicted values, $y_i$ are the observed values, $\hat{y}_i$ are the predicted values, $\sigma_{y^2}$ and $\sigma_{\hat{y}}^2$ are the variances of observed and predicted variables, and $\bar{y}$ and $\bar{\hat{y}}$ are their respective means.

The CCC simultaneously accounts for precision (through the Pearson correlation) and accuracy (through the deviation of the mean and variance of predictions from those of the observations). A CCC value of 1 indicates perfect concordance between observed and predicted values, while 0 indicates no agreement.

6) Testing Different Configurations: The PROSAIL inversion was tested iteratively in a loop for all combinations of the following configurations: Input PROSAIL parameter distribution with ATBD or SIMEUCFLUX datasets; Background with DEFAULT, S2SOIL, or FIELDSPEC upper and lower reflectances; and with or without band selection. In total, 12 configurations were tested (2 inputs × 3 soils × 2 band selection configurations). Models 1–3 use all bands and ATBD sampling strategy with soil backgrounds: DEFAULT, S2SOIL,

and FIELDSPEC, respectively. Models 4–6 use all bands and SIMEUCFLUX with those same three backgrounds. Models 7–9 use selected bands and ATBD, and Models 10–12 use selected bands and SIMEUCFLUX, combined with DEFAULT, S2SOIL, and FIELDSPEC (see Fig. 4).

To simplify references to specific models in the text, we use abbreviated labels: ALL for all bands, SEL for band selection, AT for ATBD, EU for SIMEUCFLUX, DEF for DEFAULT, S2 for S2SOIL, and SPEC for FIELDSPEC. Model configurations are named hereafter by combining these abbreviations. For example, a setup using all bands, ATBD, and DEFAULT is referred to as “all-at-def.”

7) Model Evaluation: Multiple validation statistics are used to evaluate model performance in addition to the already introduced CCC, including RMSE (5), which quantifies the average magnitude of prediction error in the original units; percent bias [PBIAS (6)], which indicates the average tendency of the estimates to over or under predict relative to observations; Pearson correlation coefficient [r (7)], which measures the strength and direction of the linear relationship between observed and predicted values. Lower RMSE and PBIAS (%) values and higher r and CCC values indicate better model performance and greater reliability

$$ \mathrm{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i)^2} \quad (5) $$

$$ \mathrm{PBias} = 100 \frac{\sum_{i=1}^{n} (\hat{y}_i - y_i)}{\sum_{i=1}^{n} y_i} \quad (6) $$

$$ r = \frac{\sum_{i=1}^{n} (y_i - \bar{y}_i) (\hat{y}_i - \bar{\hat{y}}_i)}{\sqrt{\sum_{i=1}^{n} (y_i - \bar{y}_i)^2} \sqrt{\sum_{i=1}^{n} (\hat{y}_i - \bar{\hat{y}}_i)^2}} \quad (7) $$

where n is the number of observations, $y_i$ are the observed values, $\hat{y}_i$ are the predicted values, and $\bar{y}$ and $\bar{\hat{y}}$ are the means of observed and predicted values, respectively.

### III. RESULTS

Table II summarizes the performance of the 12 model configurations. Fig. 5 graphically compares their RMSE values, while Fig. 6 highlights the optimal configuration for each biophysical variable. Supplementary Material II details the selected bands per variable for models 7 to 12.

Regarding LAI retrieval, overall, all tested configurations achieve relatively similar performances, with improvements observed mainly in the RMSE and in the percentage of bias with respect to the default model. Band selection models (sel-at) outperformed their counterparts (all-at). The rate of improvement when performing band selection was higher in the configuration using the fieldspec soil background (all-at-spec versus sel-at-spec), with a reduction of 7% in RMSE and 59% in PBIAS. When considering the differences between the sampling strategies (ATBD or SIMEUCFLUX), the rate of change increases to –12% in RMSE when band selection is not applied (all-at versus all-at). Among band selection models, depending on the soil background used, changing from ATBD to SIMEUCFLUX resulted in minor improvements, if any, particularly when using
<table><caption>TABLE II
RESULTS OF THE PROSAIL INVERSION IN THE 12 TESTED INVERSION CONFIGURATIONS FOR LEAF AREA INDEX (LAI), CHLOROPHYLL A AND B CONTENT (CAB), LEAF MASS PER AREA (LMA), AND EQUIVALENT WATER THICKNESS (EWT)</caption><thead><tr><th rowspan="2">Bands</th><th rowspan="2">Input PROSAIL</th><th rowspan="2">Background</th><th colspan="4">LAI</th><th colspan="4">Cab</th><th colspan="4">LMA</th><th colspan="4">EWT</th></tr><tr><th>RMSE</th><th>PBIAS %</th><th>r</th><th>CCC</th><th>RMSE</th><th>PBIAS %</th><th>R</th><th>CCC</th><th>RMSE</th><th>PBIAS %</th><th>r</th><th>CCC</th><th>RMSE</th><th>PBIAS %</th><th>R</th><th>CCC</th></tr></thead><tbody><tr><td>All</td><td>ATBD</td><td>DEFAU LT</td><td>0.97</td><td>-1.21</td><td>0.77</td><td>0.77</td><td>23.46</td><td>55.34</td><td>0.29</td><td>0.02</td><td>32.52</td><td>-25.88</td><td>0.59</td><td>0.2</td><td>106.56</td><td>55.95</td><td>0.53</td><td>0.1</td></tr><tr><td>All</td><td>ATBD</td><td>S2SOIL</td><td>0.94</td><td>-3.32</td><td>0.76</td><td>0.76</td><td>18.02</td><td>41.46</td><td>0.34</td><td>0.05</td><td>31.14</td><td>-24.9</td><td>0.65</td><td>0.23</td><td>151.17</td><td>87.71</td><td>0.48</td><td>0.04</td></tr><tr><td>All</td><td>ATBD</td><td>FIELD SPEC</td><td>1.11</td><td>-9.92</td><td>0.77</td><td>0.74</td><td>23.82</td><td>55.43</td><td>0.18</td><td>0.02</td><td>25.5</td><td>-17.09</td><td>0.5</td><td>0.15</td><td>196.89</td><td>117.17</td><td>0.05</td><td>0.01</td></tr><tr><td>All</td><td>SIM EUCFLU X</td><td>DEFAU LT</td><td>0.94</td><td>13.52</td><td>0.78</td><td>0.75</td><td>11.33</td><td>24.01</td><td>0.09</td><td>0.02</td><td>21.8</td><td>10.36</td><td>0.54</td><td>0.46</td><td>44.99</td><td>10.87</td><td>0.46</td><td>0.28</td></tr><tr><td>All</td><td>SIM EUCFLU X</td><td>S2SOIL</td><td>0.93</td><td>4.85</td><td>0.77</td><td>0.77</td><td>9.31</td><td>18.21</td><td>0.19</td><td>0.07</td><td>16.17</td><td>-5.68</td><td>0.7</td><td>0.66</td><td>70.34</td><td>40.92</td><td>0.4</td><td>0.05</td></tr><tr><td>All</td><td>SIM EUCFLU X</td><td>FIELD SPEC</td><td>0.98</td><td>5.28</td><td>0.77</td><td>0.77</td><td>10.19</td><td>21.06</td><td>0.22</td><td>0.07</td><td>24.62</td><td>6.94</td><td>0.31</td><td>0.29</td><td>77.92</td><td>46.12</td><td>0.15</td><td>0.01</td></tr><tr><td>Band Selection</td><td>ATBD</td><td>DEFAU LT</td><td>0.89</td><td>3.78</td><td>0.78</td><td>0.77</td><td>10.49</td><td>20.79</td><td>0.25</td><td>0.10</td><td>31.78</td><td>-24.91</td><td>0.56</td><td>0.2</td><td>53.57</td><td>27.28</td><td>0.6</td><td>0.22</td></tr><tr><td>Band Selection</td><td>ATBD</td><td>S2SOIL</td><td>0.88</td><td>5.25</td><td>0.78</td><td>0.78</td><td>8.40</td><td>14.42</td><td>0.36</td><td>0.22</td><td>30.58</td><td>-24.16</td><td>0.63</td><td>0.23</td><td>84.61</td><td>46.92</td><td>0.6</td><td>0.12</td></tr><tr><td>Band Selection</td><td>ATBD</td><td>FIELD SPEC</td><td>1.03</td><td>-4.06</td><td>0.76</td><td>0.75</td><td>10.02</td><td>20.50</td><td>0.40</td><td>0.16</td><td>24.07</td><td>-15.37</td><td>0.49</td><td>0.18</td><td>60.3</td><td>32.06</td><td>0.54</td><td>0.16</td></tr><tr><td>Band Selection</td><td>SIM EUCFLU X</td><td>DEFAU LT</td><td>0.90</td><td>8.97</td><td>0.79</td><td>0.77</td><td>9.58</td><td>13.38</td><td>0.18</td><td>0.12</td><td>14.67</td><td>1.68</td><td>0.7</td><td>0.54</td><td>16.53</td><td>-0.86</td><td>0.52</td><td>0.51</td></tr><tr><td>Band Selection</td><td>SIM EUCFLU X</td><td>S2SOIL</td><td>0.94</td><td>2.20</td><td>0.77</td><td>0.76</td><td>6.28</td><td>3.46</td><td>0.28</td><td>0.27</td><td>14.78</td><td>-4.4</td><td>0.73</td><td>0.7</td><td>36.19</td><td>16</td><td>0.48</td><td>0.27</td></tr><tr><td>Band Selection</td><td>SIM EUCFLU X</td><td>FIELD SPEC</td><td>0.98</td><td>-0.92</td><td>0.77</td><td>0.77</td><td>6.35</td><td>6.04</td><td>0.28</td><td>0.24</td><td>16.7</td><td>-5.25</td><td>0.59</td><td>0.48</td><td>45.71</td><td>23.86</td><td>0.32</td><td>0.11</td></tr></tbody></table>

Note: Bold blue values indicate the best-performing metrics.

<figure><img src="image_6.png" /><figcaption>Fig. 5. Graphical representation of PROSAIL inversion RMSE for each biophysical variable in their respective unit. The x-axis indicates if band selection was performed; facets show the sampling strategy; colors represent background reflectance datasets. (a) LAI. (b) Cab. (c) LMA. (d) EWT.</figcaption></figure>

fieldspec (–4.85% in RMSE). No model consistently achieved top performance for all validation metrics. Nevertheless, models sel-at-def and sel-at-S2 obtained the best tradeoff between the validation metrics, especially regarding RMSE and PBIAS, with sel-at-def performing slightly better (lower PBIAS). Fig. 6 illustrates the model sel-at-def (RMSE = 0.89, PBIAS = 3.78%, r = 0.78, CCC = 0.77). This model used the following S2 bands: B3, B4, B5, B6, B7, B8, B8A, B11 (only band 12 was removed).

For Cab, the inversion of PROSAIL performed poorly across all configurations. This outcome is primarily due to the low

variability in the observed values, amplifying the impact of prediction errors. Nevertheless, we observe substantial differences across model configurations in all other metrics. Configurations using all bands and ATBD sampling strategy (all-at) considerably overestimated the results, as indicated by the large positive PBIAS. Changing from ATBD to SIMEUCFLUX sampling strategy when using all bands contributed to a decrease in RMSE of up to 57% and 62% in PBIAS, depending on the soil background. Once again, band selection models (sel) outperformed their counterparts using all bands (all). We observed a reduction in RMSE up to 58% and a consistent decrease in PBIAS.
<figure><img src="image_7.png" /><figcaption>Fig. 6. Comparison of measured and estimated LAI, Cab, LMA, and EWT, from PROSAIL inversion using the best configuration identified in Table II. The color represents the stand age in years. (a) LAI. (b) Cab. (c) LMA. (d) EWT.</figcaption></figure>

Models sel-eu-S2 and sel-eu-spec exhibited comparable performance across evaluation metrics. However, sel-eu-S2 achieved a slightly lower RMSE and higher *r* and CCC while showing a substantially lower PBIAS than sel-eu-spec. For this reason, sel-eu-spec is demonstrated in Fig. 6 as the optimal configuration, which selected the following S2 bands: B6, B7, and B8A.

For LMA, all inversion configurations based on ATBD performed poorly, resulting in a substantial underestimation of most observations, as indicated by a strong negative PBIAS (models all-at, and sel-at). Replacing the ATBD dataset with the SIMEUCFLUX dataset reduced the RMSE by up to 48% without band selection and up to 52% with band selection. Sel-eu models achieved the best tradeoff among the validation metrics. Notably, sel-eu-def and sel-eu-S2 models performed similarly overall, with the second model showing a slight improvement in *r* and CCC, despite comparable RMSE and PBIAS values. Although both models performed similarly, the one with the higher CCC was preferred due to its slightly stronger agreement with observations. For this reason, sel-eu-S2 is further illustrated in Fig. 6. This model selected the following S2 bands: B3, B4, B5, B6, B7, B11, B12 (bands B8 and B8A were removed).

For EWT, we observed that all models without band selection performed poorly, leading to the overestimation of most EWT observations, as indicated by the strong positive PBIAS in configurations all-at and all-eu. Depending on the configuration, RMSE was reduced by up to 69% when performing band selection. Replacing the ATBD input parameter distribution with SIMEUCFLUX decreased the RMSE by up to 60% without band selection and up to 63% with band selection. The model sel-eu-def achieved the best validation metrics among all models (RMSE = 16.53 g/m², PBIAS = -0.86%, *r* = 0.52, CCC = 0.51).

The retained Sentinel-2 simulated bands within the sel-eu-def were B3, B5, B6, B7, B8, B12 (removed bands B4, B8A, and B11).

Fig. 5 represents the RMSE of all 12 configurations, with the colored bars representing the soil background datasets. For LAI, the DEFAULT and S2 soil backgrounds showed similar performances, regardless of band selection or the sampling strategy. On the other hand, models using FIELDSPEC to retrieve LAI achieved the highest errors when using ATBD with or without band selection. This error in the FIELDSPEC models tended to decrease when SIMEUCFLUX replaced ATBD. Regarding Cab, S2 soil backgrounds systematically achieve the lowest errors among the configurations. However, when band selection is performed and SIMEUCFLUX replaces ATBD, both FIELDSPEC and S2 soil backgrounds achieve similar performances. For LMA, when using ATBD, with or without band selection, FIELDSPEC models reached the lowest errors. Nevertheless, when SIMEUCFLUX replaces ATBD, the error associated with FIELDSPEC is higher than that of DEFAULT and S2 soil backgrounds. For EWT, the DEFAULT soil background consistently showed the lowest errors among the other soil datasets, regardless of the configuration.

Fig. 6 shows that LAI values below 1, typically associated with younger plots, were precisely estimated by PROSAIL inversion, whereas LAI values above 4, commonly linked to older plots (age > 3), tended to be underestimated. No clear link was observed between age and retrieval accuracy for Cab and EWT. For LMA, the estimation error remained consistent regardless of plot age.

## IV. DISCUSSION

### A. Impact of Band Selection on Model Performance

Band selection was crucial when performing PROSAIL inversion for LAI, Cab, LMA, and EWT. These results corroborate studies showing that using limited spectral subdomains, irrespective of the band selection method, has improved the estimations of LAI [22], [24], Cab [24], and EWT [20], [24]. The performance gain is mainly attributed to reducing band redundancy and noise, since poorly simulated PROSAIL bands are the primary sources of error in the machine learning inversion. Consequently, eliminating irrelevant bands is expected to improve model accuracy. In addition, it allows for an enhanced focus on spectral regions where the signal is most sensitive to variations in specific biophysical properties [20].

Regarding LAI, all configurations preserved the following bands: B3 (green), B6 (red edge), B8 (NIR), and B11 (SWIR) (see Supplementary Material II). The red-edge and NIR bands are particularly informative for LAI because they capture both pigment absorption and structural effects of the canopy. In the red-edge region, reflectance increases sharply as chlorophyll absorption in the red declines, making it highly sensitive to changes in leaf area and chlorophyll content. The NIR region, by contrast, is dominated by leaf internal structure and canopy scattering, where light is strongly reflected rather than absorbed. This combination allows red-edge and NIR bands to provide high correlation and predictive accuracy for LAI across different
crops and sensors, as highlighted in the literature [24], [58]. Through global sensitivity analysis, Verrelst et al. [24], identified that LAI is primarily sensitive across the full spectral range from 400 to 2500 nm. This broad sensitivity arises because LAI, representing the total leaf area per unit ground area, affects both light absorption and scattering throughout the canopy. Pigments like chlorophyll dominate absorption in the visible range (400–700 nm), while leaf structure and water content influence NIR and SWIR reflectance.

Regarding the retrieval of Cab, the two best-performing models retained B6 (red-edge), which was also preserved in almost all other configurations for Cab, B7 (red-edge), and B8A (NIR) (see Supplementary Material II). Red-edge bands were extensively reported to be correlated with Cab [59]. Interestingly, only two configurations retained B4 (red), corresponding to a region of strong absorption for Cab. The red-edge (B6 and B7) bands are centered in a spectral region where vegetation reflectance increases sharply between the low reflectance in the red and the high reflectance in the NIR, due to a decline in chlorophyll absorption [59]. The red-edge region has been reported by Spafford et al. [20] as optimal for retrieving Cab at the leaf scale, notably the subdomain spanning from 700 to 720 nm. Verrelst et al. [24] has also reported the sensitivity of leaf chlorophyll content, which can be related to the covariation between variables.

Féret et al. [18] reported dramatic improvements using optimal subdomains to retrieve EWT and LMA at the leaf scale. The authors report that the optimal spectral region to estimate EWT and LMA was in the SWIR (from 1700 to 2400 nm), where water absorption features are most pronounced [18]. Likewise, our results have shown that B12 (SWIR) was retained by all configurations of LMA and EWT, converging with the findings of Féret et al. [18] and le Maire et al. [58] (see Supplementary Material II). At the canopy scale, Verrelst et al. [24] identified bands in the red edge (723 nm) and in the NIR (1157–1419 nm) as optimal to estimate CWC (the product of LAI and EWT). Similarly, our study identified the SWIR and highlighted the relevance of red-edge bands (B5, B6, and B7) and NIR bands (B8 and B8A). For LMA and EWT, the SWIR band was essential. Other bands may bring information of reflectance “insensitive” to the biophysical variable (for instance, bands in the visible for EWT) or through the differential effect of the biophysical variables in function of the wavelength (e.g., strong effect of LMA on SWIR bands but lower effect on visible bands).

Such results must be considered in light of the limitations of the adopted band selection method. The stepwise backward band selection method does not guarantee a globally optimal set of bands, as it does not explore all possible band combinations and is constrained by the available field-measured data [22]. Additionally, the use of a single criterion (CCC) to discard the least important bands may introduce bias in the selection of bands. Even though CCC is a criterion that jointly accounts for both correlation and agreement between observed and predicted values, different selection criteria may emphasize distinct model performance aspects, potentially leading to different optimal band combinations [25]. As suggested by Wadoux and Minasny [60], using multiple complementary criteria can provide

a more comprehensive assessment of model behavior. However, integrating several metrics into an automated band selection workflow, where bands are iteratively retained or discarded, can be challenging to implement and interpret. To overcome this limitation, one straightforward option could be to define a custom score that assigns different weights to each validation statistic, depending on which aspects of model performance need to be emphasized. Nevertheless, the selected method remains consistent with our primary objective of assessing whether a subset of bands enhances retrieval accuracy compared to the complete set of S2 spectral bands. It provides valuable insight into the most and least informative spectral regions for each biophysical variable. Further studies are encouraged to compare selection approaches (e.g., stepwise, global optimization, etc.).

## B. Optimizing PROSAIL Input Parameter: Narrow Versus Broad Parameter Ranges

Limiting the range of the PROSAIL input parameters' distribution improved the accuracy of most biophysical variables, particularly for Cab, LMA, and EWT retrievals. Cab, LMA, and EWT presented non-negligible range differences compared to the ATBD dataset (see Supplementary Material III). These differences are attributed to the specificities of *Eucalyptus* genotypes. Since the ATBD is based on a literature review encompassing multiple species from diverse locations, particularly crops, some parameters fall within a broader range than those observed in the *Eucalyptus* plantations [50]. Conversely, for other parameters, the values observed for *Eucalyptus* may fall outside the ATBD range due to species-specific characteristics. The retrieval of Cab, LMA, and EWT tends to be more prone to errors due to their stronger interactions with each other or with other variables exerting similar effects on spectral reflectance [12]. Therefore, constraining the range of variable estimates to a more informative interval can help reduce confusion among variables and enhance retrieval accuracy.

Cab and EWT displayed a narrower range in the *in situ* SIMEUCFLUX dataset than in the ATBD, especially regarding the upper limit (see Supplementary Material III). The lower variability in Cab and EWT may partly explain the moderate to poor inversion results. When the range of biophysical variables is narrow, variations in reflectance spectra show only slight differences. As a result, the model has limited ability to detect or quantify variations in these variables. Regarding LMA, the SIMEUCFLUX parameter range shifted toward higher values relative to the upper limit of the ATBD dataset (see Supplementary Material III), indicating that *Eucalyptus* plantations tend to exhibit a greater LMA compared to the distribution defined in the ATBD dataset. The range difference results in the remarkable improvement when switching from ATBD to the SIMEUCFLUX dataset, as models using ATBD tended to underestimate most LMA observations.

For LAI, the best results were obtained using the ATBD dataset. This can be explained by the slight difference between the datasets. High LAI values (LAI > 4) were poorly estimated regardless of the inversion configuration. The higher uncertainty at moderate to high LAI is a common phenomenon caused by
the saturation of the red and NIR bands, which no longer change with the increase in LAI [29].

Narrowing the parameter distribution constrains the output within or close to this range, which can artificially improve statistics such as PBIAS or RMSE. This study is based on distributions acquired during a full rotation of *Eucalyptus* across multiple genotypes. Under these conditions, the typical variability observed in our study area during *Eucalyptus* rotations is well represented. Weiss et al. [50] argue that the differences in biophysical variable variability between vegetation types suggest that training separate algorithms tailored to each vegetation type could improve performance. In this work, we focused on retrieving biophysical variables from *Eucalyptus* only, which justifies the need for an informative and adapted input parameter range. Prior information on PROSAIL input variables for a given vegetation type is crucial for generating an adequate learning database and thus improving estimation accuracy. Nevertheless, such information is limited, especially regarding forest plantations in tropical regions.

### C. Choice of Background Reflectance

The impact of different soil reflectance datasets on model performance was more pronounced when all spectral bands were used. For example, under the configuration without band selection and with the local input parameter ranges (SIMEUCFLUX), the rate of change in RMSE across soil datasets showed considerable variation.

The results suggest that the sensitivity of biophysical variable retrieval to background reflectance depends on the soil background and the specific inversion strategies employed, including band selection and the choice of input parameter range. This is likely due to the varying influence of background reflectance on the simulated spectral bands [26]. Since we compared different inversion configurations, one method or dataset choice may compensate for the shortcomings of another. For example, suboptimal background reflectance can be mitigated by more effective band selection or a narrower parameter range, resulting in improved retrieval performance.

Other configurations outperformed the FIELDSPEC background configuration. Jiang et al. [26] found that using a field spectroradiometer source outperformed a generic soil spectral library (ICRAF-ISRIC soil spectral library) to retrieve LAI from PROSAIL inversion in a small experimental area. In our study, the difference between soil datasets under the same inversion configuration was minimal for LAI retrieval. The comparable performance of the DEFAULT background dataset to the FIELDSPEC dataset is encouraging, given that the DEFAULT dataset represents more generic soil types designed for broader applicability across different regions. In contrast, the FIELDSPEC data are specific to our study area. In addition, the influence of the soil background was higher in areas with low vegetation density [26], [29], [61].

Potential causes for FIELDSPEC's suboptimal performance may be related to the fact that this dataset exhibits lower variability in its upper and lower reflectance bounds compared to the S2SOIL and DEFAULT soil background datasets. These bounds

are intended to capture differences in soil conditions, often driven by variations in soil moisture. In the case of FIELDSPEC, these differences were defined as ±1.5 standard deviations from measurements collected on a single date, which inherently limits the range of variability represented in the dataset.

### V. CONCLUSION

The combined effects of spectral band selection, PROSAIL input parameter ranges, and soil background reflectance were evaluated in this research article for retrieving LAI, Cab, LMA, and EWT in *Eucalyptus* plantations. Our results demonstrate that integrating optimized spectral bands for each biophysical variable, incorporating prior knowledge of *Eucalyptus*-specific parameter ranges, and using an appropriate soil reflectance dataset substantially enhances the accuracy of PROSAIL hybrid inversion. This study also benefits from a uniquely comprehensive, season-long dataset that captures the full variability of *Eucalyptus* biophysical properties, an unique dataset.

Overall, we demonstrated that the optimal inversion configuration differs among biophysical variables. Even so, most variables showed clear advantages when performing band selection (LAI, Cab, LMA, and EWT), using customized parameter ranges (Cab, LMA, and EWT), and a S2SOIL (Cab and LMA). These findings highlight the importance of aligning inversion settings with variable-specific requirements to enhance the robustness and accuracy of PROSAIL-based retrievals.

### ACKNOWLEDGMENT

The authors acknowledge CNES and CIRAD for funding the first author. The authors thank the EUCFLUX 2 project led by IPEF. The experimental site is part of SOERE F-ORE-T, with additional support from the IN-SYLVA network. The authors thank the Itatina Experimental Station team, especially Rildo Moreira e Moreira (Esalq, USP) and Eder Araujo da Silva, for their technical assistance.

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<figure><img src="image_8.png" /></figure>

**Vitória Barbosa Ferreira** (Graduate Student Member, IEEE) received the B.Sc. degree in geography from the University of Sao Paulo, Sao Paulo, Brazil, in 2020, and the M.Sc. degree in geodata science from Université Bretagne Sud, Vannes, France, and the University of Salzburg, Salzburg, Austria, in 2022.

Since 2022, she has been conducting her doctoral studies at CIRAD, Institut Agro, and University of Montpellier, Montpellier, France. Her research interests include remote sensing of vegetation, forestry, ecology, and biogeography.

<figure><img src="image_9.png" /></figure>

**Jean-Baptiste Féret** received the diploma in agricultural engineering from Montpellier SupAgro, Montpellier, France, in 2005, and the Ph.D. degree in environmental sciences from Université Pierre et Marie Curie, Paris, France, in 2009.

trometers and 3-D radiative transfer modeling. Since 2014, he has worked at the National Research Institute of Agriculture and Environment (INRAE), Toulouse, in the TETIS Research Unit, Montpellier. His research interests include optical remote sensing and radiative transfer modeling of vegetation, vegetation traits monitoring, and biodiversity mapping.

<figure><img src="image_10.png" /></figure>

From 2010 to 2013, he was affiliated with the Asner Lab at the Carnegie Institution for Science, Department of Global Ecology, Stanford, CA, USA. In 2014, he joined CNES at the CESBIO Laboratory, Toulouse, France, focusing on biodiversity mapping applications using future spaceborne imaging spectrometers.

Since 2014, he has worked at the National Research Institute of Agriculture and Environment (INRAE), Toulouse, in the TETIS Research Unit, Montpellier. His research interests include optical remote sensing and radiative transfer modeling of vegetation, vegetation traits monitoring, and biodiversity mapping.

**Clement Atzberger** received the Ph.D. degree in crop growth modeling and remote sensing data assimilation from Trier University, Trier, Germany, in 1997.

Between 2010 and 2024, he was a Full Professor of Geomatics and Remote Sensing in Vienna. He is currently the Chief Scientific Officer of dClimate Labs, New York, NY, USA.

<figure><img src="image_11.png" /></figure>

**Otávio Camargo Campoe** received the B.Sc. degree in forestry from São Paulo State University, São Paulo, Brazil, in 2005, and the M.Sc. (and Ph.D. degrees in forest resources from the University of São Paulo, São Paulo, in 2008 and 2012, respectively).

He worked from 2012 to 2015 as an ecophysiologist with the Forestry Science and Research Institute. He is currently a Professor of silviculture with the Department of Forestry Sciences, Federal University of Lavras, Lavras, Brazil, where he focuses his research on the physiological ecology of forest plantations for production and restoration.

<figure><img src="image_12.png" /></figure>

**Joannès Guillemot** received the M.Sc. degree in forestry and ecology from AgroParisTech – ENGREF, Palaiseau Cedex, France, in 2012, and the Ph.D. degree in plant ecophysiology from Paris Sud University, Orsay, France, in 2015.

From 2015 to 2016, he was a Postdoctoral Researcher with the Chair of Global Change Ecology, University of Edinburgh. Since 2016, he has been with the CIRAD, Montpellier, France, and has been based in Brazil for several years, where he is a Visiting Professor with the University of São Paulo/ESALQ, São Paulo, Brazil. His research interests span ecophysiology and functional ecology to deepen our understanding of tropical forest functioning, especially in how they respond to drought.

<figure><img src="image_13.png" /></figure>

**Guerric le Maire** received the M.Sc. degree in agronomy, specializing in ecology, from the National Institute of Agronomy, Paris, France, in 2002, and the Ph.D. degree in plant ecophysiology from Paris XI University, Orsay, France, in 2005.

During 2006–2007, he was a Postdoctoral Researcher with the Le Laboratoire des Sciences du Climat et de l’Environnement Laboratory, Saclay, France. Since 2008, he has been with the CIRAD, Montpellier, France, and got the Accreditation to Supervise Research (HDR) in 2018.