A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors

Abstract

A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%-481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%-102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types.

Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach

“…Additionally, water depth, or bottom reflectance, impacts observed reflectance independently from IOPs. For best performance, empirical algorithms typically need to be parameterized individually for a given optically complex water body [30,31].…”

Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

“…These algorithms are generally easier to use for conventional applications of remotely sensed TSS estimates, and their physical basis is more straightforward [16]. In a comprehensive quantitative review of published TSS algorithms, Dorji et al 2016 [44] found that optically based algorithms generally perform better than empirical algorithms in waters with unknown composition.…”

Multi-temporal dynamics of suspended particulate matter in a macro-tidal river Plume (the Gironde) as observed by satellite data

“…¼ 511:9 Â R rs 645 r 2 ¼ 0:88; NRMSE ¼ 12:18%; n ¼ 21; n represents the total number of points=match À ups used ðNovoa et al:; 2017Þ (1) Multiple studies have successfully used a similar approach to determine either turbidity or SPM in moderately turbid waters (Dorji and Fearns, 2016), therefore it was considered to be appropriate for our study also. Previous authors used also the green spectral band to retrieve SPM in the Bay of Biscay (Gohin et al, 2005;Gohin, 2011), but such approach might underestimate the higher concentrations close to the coast and river mouth.…”