MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach

Abstract

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88–0.93, mean absolute error of 0.17–0.19, and normal root mean square error of 0.05–0.09. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.