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 MOD_T model variant uses the 1-day×250 m MODIS public domain datasets, and the FUS_T model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUS_T model integrates the Frank Copula with the FUS_T 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 CFUS_T 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 CFUS_T and CFUS_TU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

沿城市延伸的河流生态系统管理主要依赖于以更精细的空间分辨率对水质进行高频（每日）监测。然而，由于维护成本高，现场监测站数量减少，因此需要开发下一代遥感（RS）工具作为替代方法，以更好地覆盖河流水质评估. 本研究提倡使用公共领域的 MODIS 和 Landsat 卫星数据集来估计每日范围内的总悬浮固体 (TSS) 浓度的三个新模型变体。MOD _T模型变体使用 1 天×250 m MODIS 公共域数据集，以及 FUS _T模型基于 1 天×30 m MODIS-Landsat 融合数据集，而 CFUS _T模型集成了 Frank Copula 和 FUS _T模型。使用2016 年至 2019 年在多个监测站测量的原位TSS 数据集，在印度东部以城市垃圾为主的恒河下游（即胡格利河和婆罗门河）对这些分层模型变体进行了评估。结果表明， CFUS _T是最佳 TSS 估计模型变体，其平均确定系数为 0.88-0.93，平均绝对误差为 0.17-0.19，正态均方根误差为 0.05-0.09。最后，拟议的 CFUS _T和 CFUS _TU随机模型可分别用作沿动态河流系统进行 TSS 和浊度评估的潜在工具。