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

Water clarity in the Chesapeake Bay is an important environmental variable to monitor due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. The Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include a volumetric indicator of water clarity - total suspended solids (TSS). Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only a single red or near-infrared band due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various machine learning and statistical models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing machine learning model, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. The results of this study have implications for future sensor design and monitoring of surface water quality parameters.