Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters SCIE SCOPUS

Cited 52 time in WEB OF SCIENCE Cited 57 time in Scopus
Title
Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters
Author(s)
Balasubramanian S.V.; Pahlevan N.; Smith B.; Binding C.; Schalles J.; Loisel H.; Gurlin D.; Greb S.; Alikas K.; Randla M.; Bunkei M.; Moses W.; Nguyễn H.; Lehmann M.K.; O'Donnell D.; Ondrusek M.; Han, Tai Hyun; Fichot C.G.; Moore T.; Boss E.
KIOST Author(s)
Han, Tai Hyun(한태현)
Alternative Author(s)
한태현
Publication Year
2020-09
Abstract
One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP)-based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R-rs), (b) retrieval of particulate backscattering (b(bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m(3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to> 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in R-rs leads to< 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.
ISSN
0034-4257
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/38588
DOI
10.1016/j.rse.2020.111768
Bibliographic Citation
Remote Sensing of Environment, 2020
Publisher
Elsevier Inc.
Keywords
Total suspended solids; Remote sensing reflectance; Backscattering; Coastal and inland waters; Inversion models; Inherent optical properties; Aquatic remote sensing; Sentinel-3
Type
Article
Language
English
Document Type
Article
Publisher
Elsevier Inc.
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