TY - JOUR
T1 - Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters
AU - Balasubramanian, Sundarabalan V.
AU - Pahlevan, Nima
AU - Smith, Brandon
AU - Binding, Caren
AU - Schalles, John
AU - Loisel, Hubert
AU - Gurlin, Daniela
AU - Greb, Steven
AU - Alikas, Krista
AU - Randla, Mirjam
AU - Bunkei, Matsushita
AU - Moses, Wesley
AU - Nguyễn, Hà
AU - Lehmann, Moritz K.
AU - O'Donnell, David
AU - Ondrusek, Michael
AU - Han, Tai Hyun
AU - Fichot, Cédric G.
AU - Moore, Tim
AU - Boss, Emmanuel
N1 - Funding Information:
We gratefully acknowledge the PIs, scientists and contributors to SeaBASS, including Zhongping Lee, Steve Ackleson, Richard Reynolds (ICESCAPE), Colleen Mouw, and are also very thankful to the NASA Ocean Biology Processing Group (OBPG), David Doxaran, and Kevin Ruddick for distributing in situ datasets. Nima Pahlevan is funded under NASA ROSES Awards # 80NSSC18K0077 , # 80HQTR19C0015 , and the USGS Landsat Science Team Award # 140G0118C0011 . Emmanuel Boss was funded under ONR grant N000141612218 . John Schalles was funded under NOAA grant # NA11SEC4810001 and NSF grant # 1832178 . Further, we are grateful to the two anonymous reviewers for their fruitful comments and meticulous reviews, which ultimately led to the enhanced quality of this manuscript.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.rse.2020.111768
DO - 10.1016/j.rse.2020.111768
M3 - Article
AN - SCOPUS:85085340652
VL - 246
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 111768
ER -