Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneity. Such exercise can be achieved with coupled leaf and canopy radiative transfer models (RTMs), whereby input variables can virtually simulate any vegetation scenario. With the intention of evaluating multiple VIs in an efficient way, this led us to the development of a global sensitivity analysis (GSA) toolbox dedicated to the analysis of VIs on their sensitivity towards RTM input variables. We identified VIs that are designed to be sensitive towards leaf chlorophyll content (LCC), leaf water content (LWC) and leaf area index (LAI) for common sensors of terrestrial Earth observation satellites: Landsat 8, MODIS, Sentinel-2, Sentinel-3 and the upcoming imaging spectrometer mission EnMAP. The coupled RTMs PROSAIL and PROINFORM were used for simulations of homogeneous and forest canopies respectively. GSA total sensitivity results suggest that LCC-sensitive indices respond most robust: for the great majority of scenarios, chlorophyll a + b content (Cab) drives between 75% and 82% of the indices’ variability. LWC-sensitive indices were most affected by confounding variables such as Cab and LAI, although the equivalent water thickness (Cw) can drive between 25% and 50% of the indices’ variability. Conversely, the majority of LAI-sensitive indices are not only sensitive to LAI but rather to a mixture of structural and biochemical variables.

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Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2018.02.010 ◽

2018 ◽

Vol 253-254 ◽

pp. 247-260 ◽

Cited By ~ 25

Author(s):

Zhihui Wang ◽

Andrew K. Skidmore ◽

Roshanak Darvishzadeh ◽

Tiejun Wang

Keyword(s):

Radiative Transfer ◽

Nitrogen Content ◽

Forest Canopy ◽

Hyperspectral Imagery ◽

Radiative Transfer Models ◽

Canopy Nitrogen ◽

Leaf Canopy ◽

Transfer Models

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Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

Remote Sensing ◽

10.3390/rs11161923 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1923 ◽

Cited By ~ 6

Author(s):

Jochem Verrelst ◽

Jorge Vicent ◽

Juan Pablo Rivera-Caicedo ◽

Maria Lumbierres ◽

Pablo Morcillo-Pallarés ◽

...

Keyword(s):

Sensitivity Analysis ◽

Concept Maps ◽

Global Sensitivity Analysis ◽

Dominant Role ◽

Atmospheric Correction ◽

Total Contribution ◽

Area Index ◽

Global Sensitivity ◽

Radiance Data ◽

Leaf Canopy

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm − 1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

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