气候变化领域集成服务门户(CCPortal): Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

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DOI	10.1016/j.rse.2020.111768
	Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters
	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 T.-H.; Fichot C.G.; Moore T.; Boss E.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	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 (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp 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/m3], 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 Rrs 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. © 2020 The Authors
英文关键词	Aquatic remote sensing; Backscattering; Coastal and inland waters; Inherent optical properties; Inversion models; Remote sensing reflectance; Sentinel-3; Total suspended solids
语种	英语
scopus关键词	Classification (of information); Infrared devices; Lakes; Marine applications; Optical properties; Radiometers; Remote sensing; Sensitivity analysis; Thermography (imaging); Aquatic remote sensing; Atmospheric corrections; Inherent optical properties; Moderate resolution imaging spectroradiometer; Operational land imager; Remote-sensing reflectance; State-of-the-art algorithms; Visible infrared imaging radiometer suites; Uncertainty analysis; algorithm; backscatter; classification; coastal water; concentration (composition); correction; estimation method; machine learning; MODIS; near infrared; optical property; remote sensing; satellite data; sensitivity analysis; surface reflectance; suspended sediment; VIIRS; California; Chesapeake Bay; China; Florida [United States]; Lake Okeechobee; San Francisco Bay; Taihu Lake; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179273
作者单位	NASA Goddard Space Flight Center, Greenbelt, MD, United States; Science Systems and Applications, Inc. (SSAI), LanhamMD, United States; University of Maryland, Department of Geographical Sciences, College ParkMD, United States; Geo-Sensing and Imaging Consultancy, Trivandrum, Kerala, India; Environment and Climate Change Canada, Burlington, ON, Canada; Creighton University, Department of Biology, Omaha, NE, United States; Univ. Littoral Côte d'Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, Lille, France; Wisconsin Department of Natural Resources, Madison, WI, United States; University of Wisconsin-Madison, Space Science and Engineering, Madison, WI, United States; Tartu observatory of University of Tartu, Tartu, Estonia; University of Tsukuba, Ibaraki, Japan; Naval Research Laboratory, Washington, DC, United States; VNU University of Science, Vietnam National University, Hanoi, Viet Nam; Xerra Earth Observation Institute and the University of Waikato, Hamilton, New Z...
推荐引用方式 GB/T 7714	Balasubramanian S.V.,Pahlevan N.,Smith B.,et al. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters[J],2020,246.
APA	Balasubramanian S.V..,Pahlevan N..,Smith B..,Binding C..,Schalles J..,...&Boss E..(2020).Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters.Remote Sensing of Environment,246.
MLA	Balasubramanian S.V.,et al."Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters".Remote Sensing of Environment 246(2020).