Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters
|Author(s)||Han Bing1, 2, 3, Loisel Hubert2, 4, 5, Vantrepotte Vincent2, 6, Meriaux Xavier2, Bryere Philippe7, Ouillon Sylvain4, 8, Dessailly David2, Xing Qianguo9, Zhu Jianhua1|
|Affiliation(s)||1 : NOTC, 219 Jieyuanxi Rd, Tianjin 300112, Peoples R China.
2 : ULCO, LOG, F-62930 Wimereux, France.
3 : NUIST, Sch Marine Sci, 219 Jingning 6 Rd, Nanjing 210044, Jiangsu, Peoples R China.
4 : Univ Toulouse, IRD, UPS OMP, UMR LEGOS 5566, 14 Av Edouard Belin, F-31400 Toulouse, France.
5 : VAST, STI, 18 Hoang Quoc Viet, Hanoi, Vietnam.
6 : CNRS Guyane, USR3456, F-97334 Cayenne, France.
7 : ACRI HE, 8 Quai Douane, F-29200 Brest, France.
8 : USTH, Dept Water Environm Oceanog, 18 Hoang Quoc Viet, Hanoi, Vietnam.
9 : Chinese Acad Sci, Yantai Inst Coastal Zone Res YIC, 17 Chunhui Rd, Yantai 264003, Peoples R China.
|Source||Remote Sensing (2072-4292) (Mdpi), 2016-03 , Vol. 8 , N. 3 , P. 211 (23p.)|
|WOS© Times Cited||82|
|Note||This article belongs to the Special Issue Remote Sensing in Coastal Environments|
|Keyword(s)||specific backscattering coefficient, empirical algorithm, semi-analytic algorithm, coastal waters, suspended particulate matter, ocean color|
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, R-rs(lambda), 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 center dot m(-3). Different empirical and semi-analytical approaches developed to assess SPM from R-rs(lambda) 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.