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Remote Sens. 2016, 8(3), 211; doi:10.3390/rs8030211

Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters

1
National Ocean Technology Center (NOTC), 219 Jieyuanxi Rd., Tianjin 300112, China
2
Université du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), 62930 Wimereux, France
3
School of Marine Sciences, Nanjing University of Information Science and Technology (NUIST), 219 Jingning 6 Rd., Nanjing210044, China
4
Institut de Recherche Pour le Développement (IRD), Université de Toulouse, UPS (OMP), UMR 5566 LEGOS, 14 av. Edouard Belin, 31400 Toulouse, France
5
Space Technology Institute (STI), Vietnam Academy of Science & Technology (VAST), 18 Hoang Quoc Viet, CauGiay, Hanoi, Vietnam
6
CNRS Guyane, USR3456, 97334 Cayenne Cedex, France
7
ACRI-HE, 8 Quai de la douane, 29200 Brest, France
8
Department Water-Environment-Oceanography, University of Science and Technology of Hanoi (USTH), 18 Hoang Quoc Viet, CauGiay, Hanoi, Vietnam
9
Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences, 17 Chunhui Rd, Yantai 264003, China
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Richard W. Gould, Xiaofeng Li and Prasad S. Thenkabail
Received: 26 December 2015 / Revised: 22 February 2016 / Accepted: 29 February 2016 / Published: 5 March 2016
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
View Full-Text   |   Download PDF [6538 KB, uploaded 5 March 2016]   |  

Abstract

Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters. View Full-Text
Keywords: suspended particulate matter; coastal waters; ocean color; specific backscattering coefficient; empirical algorithm; semi-analytic algorithm suspended particulate matter; coastal waters; ocean color; specific backscattering coefficient; empirical algorithm; semi-analytic algorithm
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MDPI and ACS Style

Han, B.; Loisel, H.; Vantrepotte, V.; Mériaux, X.; Bryère, P.; Ouillon, S.; Dessailly, D.; Xing, Q.; Zhu, J. Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters. Remote Sens. 2016, 8, 211.

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