FN Archimer Export Format
PT J
TI A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance
BT 
AF Jorge, Daniel S.F.
   Loisel, Hubert
   Jamet, Cédric
   Dessailly, David
   Demaria, Julien
   Bricaud, Annick
   Maritorena, Stéphane
   Zhang, Xiaodong
   Antoine, David
   Kutser, Tiit
   Bélanger, Simon
   Brando, Vittorio O.
   Werdell, Jeremy
   Kwiatkowska, Ewa
   Mangin, Antoine
   Fanton d'Andon, Odile
AS 1:1;2:1;3:1;4:2;5:3;6:4;7:5;8:6;9:4,7;10:8;11:9;12:10;13:11;14:2;15:3;16:3;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;11:;12:;13:;14:;15:;16:;
C1 Univ. Littoral Côte d'Opale, CNRS, Univ. Lille, UMR 8187 - LOG - Laboratoire d'Océanologie et de Géosciences, F-62930 Wimereux, France
   EUMETSAT, Darmstadt, Germany
   ACRI-ST, Sophia Antipolis Cedex 06904, France
   Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, Villefranche-sur-Mer, France
   Earth Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
   Division of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA
   Remote Sensing and Satellite Research Group, School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia
   Estonian Marine Institute, University of Tartu, Estonia
   Département de Biologie, Chimie et Géographie and BORÉAS, Université du Québec à Rimouski, Canada
   Institute of Marine Sciences, National Research Council of Italy (CNR-ISMAR), Rome, Italy
   NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
C2 UNIV LITTORAL COTE D'OPALE, FRANCE
   EUMETSAT, GERMANY
   ACRI-ST, FRANCE
   UNIV SORBONNE, FRANCE
   UNIV CALIF SANTA BARBARA, USA
   UNIV SOUTHERN MISSISSIPPI, USA
   UNIV CURTIN, AUSTRALIA
   UNIV TARTU, ESTONIA
   UNIV QUEBEC RIMOUSKI, CANADA
   CNR ISMAR, ITALY
   NASA, USA
IF 13.85
TC 14
UR https://archimer.ifremer.fr/doc/00700/81225/85517.pdf
LA English
DT Article
CR BOUSSOLE
   PEACETIME
BO Pourquoi pas ?
AB 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. 
PY 2021
PD SEP
SO Remote Sensing Of Environment
SN 0034-4257
PU Elsevier BV
VL 263
UT 000702872900004
DI 10.1016/j.rse.2021.112537
ID 81225
ER

EF