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Open AccessArticle

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

1
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain
2
Magellium, 31520 Toulouse, France
3
CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, CP. Tepic 63155, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1923; https://doi.org/10.3390/rs11161923
Received: 26 June 2019 / Revised: 9 August 2019 / Accepted: 15 August 2019 / Published: 17 August 2019
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. View Full-Text
Keywords: radiative transfer models; global sensitivity analysis; emulation; machine learning; top-of-atmosphere radiance data; PROSAIL; MODTRAN; retrieval; Sentinel-2 radiative transfer models; global sensitivity analysis; emulation; machine learning; top-of-atmosphere radiance data; PROSAIL; MODTRAN; retrieval; Sentinel-2
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MDPI and ACS Style

Verrelst, J.; Vicent, J.; Rivera-Caicedo, J.P.; Lumbierres, M.; Morcillo-Pallarés, P.; Moreno, J. Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data. Remote Sens. 2019, 11, 1923. https://doi.org/10.3390/rs11161923

AMA Style

Verrelst J, Vicent J, Rivera-Caicedo JP, Lumbierres M, Morcillo-Pallarés P, Moreno J. Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data. Remote Sensing. 2019; 11(16):1923. https://doi.org/10.3390/rs11161923

Chicago/Turabian Style

Verrelst, Jochem; Vicent, Jorge; Rivera-Caicedo, Juan P.; Lumbierres, Maria; Morcillo-Pallarés, Pablo; Moreno, José. 2019. "Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data" Remote Sens. 11, no. 16: 1923. https://doi.org/10.3390/rs11161923

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