A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance
| Type | Article | ||||||||
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| Date | 2021-09 | ||||||||
| Language | English | ||||||||
| Author(s) | Jorge Daniel S.F.1, Loisel Hubert1, Jamet Cédric1, Dessailly David2, Demaria Julien3, Bricaud Annick4, Maritorena Stéphane5, Zhang Xiaodong6, Antoine David4, 7, Kutser Tiit8, Bélanger Simon9, Brando Vittorio O.10, Werdell Jeremy11, Kwiatkowska Ewa2, Mangin Antoine3, Fanton D'Andon Odile3 | ||||||||
| Affiliation(s) | 1 : Univ. Littoral Côte d'Opale, CNRS, Univ. Lille, UMR 8187 - LOG - Laboratoire d'Océanologie et de Géosciences, F-62930 Wimereux, France 2 : EUMETSAT, Darmstadt, Germany 3 : ACRI-ST, Sophia Antipolis Cedex 06904, France 4 : Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, Villefranche-sur-Mer, France 5 : Earth Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA 6 : Division of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA 7 : Remote Sensing and Satellite Research Group, School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia 8 : Estonian Marine Institute, University of Tartu, Estonia 9 : Département de Biologie, Chimie et Géographie and BORÉAS, Université du Québec à Rimouski, Canada 10 : Institute of Marine Sciences, National Research Council of Italy (CNR-ISMAR), Rome, Italy 11 : NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA |
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| Source | Remote Sensing Of Environment (0034-4257) (Elsevier BV), 2021-09 , Vol. 263 , P. 112537 (17p.) | ||||||||
| DOI | 10.1016/j.rse.2021.112537 | ||||||||
| WOS© Times Cited | 10 | ||||||||
| Abstract | We present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(λ). The derived IOPs include the total (a(λ)), phytoplankton (aphy(λ)), and colored detrital matter (acdm(λ)), absorption coefficients, and the total (bb(λ)) and particulate (bbp(λ)) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a(λ) and bbp(λ) are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a(λ) and bb(λ) at any wavelength at which Rrs(λ) is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy(λ) and acdm(λ) from anw(λ).The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters. |
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