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 S tatistical, inherent O ptical property (IOP) -based, and mu L ti-conditional I nversion proce D ure (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

中文翻译：

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估算内陆和近岸沿海水域总悬浮固体 (TSS) 的稳健算法

摘要 现代水生遥感中的一项具有挑战性的任务是总悬浮固体 (TSS) 近地表浓度的反演。本研究旨在提出一种基于统计的、固有的光学特性 (IOP) 和多条件反演过程 (SOLID)，用于在广泛的水下生物条件下增强卫星衍生 TSS 的反演。 - 河流、湖泊、河口和沿海水域的光学条件。在本研究中，使用大型原位数据库 (N > 3500)，使用三步程序设计 SOLID 模型：(a) 输入遥感反射率 (Rrs) 的水类型分类，(b) 检索使用半分析、机器学习和经验模型在红色或近红外 (NIR) 区域进行颗粒反向散射 (bbp)，(c) 通过特定于水类型的经验模型从 bbp 估计 TSS。使用我们原位数据的独立子集（N = 2729），TSS 范围从 0.1 到 2626.8 [g/m3]，SOLID 模型经过彻底检查，并与几种最先进的算法（Miller 和 McKee， 2004 年；Nechad 等人，2010 年；Novoa 等人，2017 年；Ondrusek 等人，2012 年；Petus 等人，2010 年）。我们表明 SOLID 在不同程度上优于所有其他模型，即从 10% 到 >100%，这取决于统计属性（例如，全局与特定于水类型的指标）。出于演示目的，该模型针对 Sentinel-2A/B 上的多光谱成像仪在切萨皮克湾、旧金山湾-三角洲河口、奥基乔比湖和太湖上获取的图像实施。为了能够生成一致的多任务 TSS 产品，其性能进一步扩展到其他任务并对其进行评估，例如海洋和陆地颜色仪器 (OLCI)、中分辨率成像光谱仪 (MODIS)、可见光红外成像辐射计套件 (VIIRS) 和操作陆地成像仪 (OLI) . 对大气改正引起的不确定性的敏感性分析表明，Rrs 中 10% 的不确定性导致