Assessing water clarity in the Chesapeake Bay using satellite-derived Secchi disk depth

• Proposed new Secchi Disk Depth algorithm performs well in the Chesapeake Bay. • Satellite-derived maps revealed spatial and temporal patterns in water clarity. • Long-term variations in water clarity has strong correlation with river discharge. Water clarity is an important characteristic of water quality and ecosystem health in inland and coastal waters. Secchi disk depth (SDD) is a conventional in situ optical method of measuring this parameter. Assessments of SDD from remotely-sensed ocean color data benefit from wide spatial and temporal coverage but are subject to low accuracy resulting from unstable relationship between water transparency and the color of water surface. A semi-analytical SDD algorithm (hereafter called CB-SDD algorithm) proposed for turbid coastal waters was extensively evaluated in the Chesapeake Bay. The results demonstrate the CB-SDD algorithm performs well for data collected by satellite ocean color sensors over the Chesapeake Bay with lower bias and higher accuracy. Satellite SDD maps from MODIS-Aqua and VIIRS-SNPP were generated using CB-SDD algorithm to analyze spatiotemporal variations of water clarity in the Chesapeake Bay. Spatial distribution of the satellite-derived SDD products illustrates that SDD are shallower in the northern Bay and upstream in its tributaries, while SDD are deeper along the main stem in the middle and lower parts of the Bay. Expected seasonal and interannual variation in SDD in the entire Bay are evident in the satellite results. The assessments of satellite-derived SDD reveal river discharge patterns of seasonal and interannual variations similar to the patterns resulting from in situ SDD measurements. Disagreements between the assessments are insignificant and can be explained by the differences in sampling locations. Our results show that satellite data can be fit-for-purpose for water quality management across the Chesapeake Bay and this satellite method can be considered to extend water quality observations where and when in situ observations are lacking in the Bay. Furthermore, this study contributes to the increasing confidence that with similar analysis of other turbid coastal and inland water bodies, ocean color remote sensing can be a robust tool to understand and manage water quality, globally, including retrospective studies covering the satellite era.