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Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach
Author(s): Jiang, Dalin
Matsushita, Bunkei
Pahlevan, Nima
Gurlin, Daniela
Fichot, Cédric G
Harringmeyer, Joshua
Sent, Giulia
Brito, Ana C
Brotas, Vanda
Werther, Mortimer
Mascarenhas, Veloisa
Blake, Matthew
Hunter, Peter
Tyler, Andrew
Spyrakos, Evangelos
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Keywords: Total suspended solids
Semi-analytical algorithm
Sentinel-2 MSI
Inland waters
Coastal waters
Issue Date: Oct-2023
Date Deposited: 26-Oct-2023
Citation: Jiang D, Matsushita B, Pahlevan N, Gurlin D, Fichot CG, Harringmeyer J, Sent G, Brito AC, Brotas V, Werther M, Mascarenhas V, Blake M, Hunter P, Tyler A & Spyrakos E (2023) Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach. <i>ISPRS Journal of Photogrammetry and Remote Sensing</i>, 204, pp. 362-377.
Abstract: Inland and coastal waters provide key ecosystem services and are closely linked to human well-being. In this study, we propose a semi-analytical method, which can be applied to Sentinel-2 MultiSpectral Instrument (MSI) images to retrieve high spatial-resolution total suspended solids (TSS) concentration in a broad spectrum of aquatic ecosystems ranging from clear to extremely turbid waters. The presented approach has four main steps. First, the remote sensing reflectance (Rrs) at a band lacking in MSI (620 nm) is estimated through an empirical relationship from Rrs at 665 nm. Second, waters are classified into four types (clear, moderately turbid, highly turbid, and extremely turbid). Third, semi-analytical algorithms are used to estimate the particulate backscattering coefficient (bbp) at a reference band depending on the water types. Last, TSS is estimated from bbp at the reference band. Validation and comparison of the proposed method with three existing methods are performed using a simulated dataset (N = 1000), an in situ dataset collected from global inland and coastal waters (N = 1265) and satellite matchups (N = 40). Results indicate that the proposed method can improve TSS estimation and provide accurate retrievals of TSS from all three datasets, with a median absolute percentage error (MAPE) of 14.88 %, 31.50 % and 41.69 % respectively. We also present comparisons of TSS mapping between the Sentinel-3 Ocean and Land Colour Instrument (OLCI) and MSI in Lake Kasumigaura, Japan and the Tagus Estuary, Portugal. Results clearly demonstrate the advantages of using MSI for TSS monitoring in small water bodies such as rivers, river mouths and other nearshore waters. MSI can provide more detailed and realistic TSS estimates than OLCI in these water bodies. The proposed TSS estimation method was applied to MSI images to produce TSS time-series in Lake Kasumigaura, which showed good agreements with in situ and OLCI-derived TSS time-series.
DOI Link: 10.1016/j.isprsjprs.2023.09.020
Rights: This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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