{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:36:06Z","timestamp":1771608966106,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T00:00:00Z","timestamp":1566000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["755617"],"award-info":[{"award-number":["755617"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s computational burden and GSA\u2019s 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\u20132500 nm spectral range at 15 cm      \u2212 1      (between 0.3\u20139 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.<\/jats:p>","DOI":"10.3390\/rs11161923","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"1923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"first","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"given":"Jorge","family":"Vicent","sequence":"additional","affiliation":[{"name":"Magellium, 31520 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1448","authenticated-orcid":false,"given":"Juan Pablo","family":"Rivera-Caicedo","sequence":"additional","affiliation":[{"name":"CONACyT-UAN, Secretar\u00eda de Investigaci\u00f3n y Posgrado, Universidad Aut\u00f3noma de Nayarit, Ciudad de la Cultura Amado Nervo, CP. Tepic 63155, Mexico"}]},{"given":"Maria","family":"Lumbierres","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"given":"Pablo","family":"Morcillo-Pallar\u00e9s","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"given":"Jos\u00e9","family":"Moreno","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. 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