POSTER Session 2

SPATIAL AND TEMPORAL VARIATIONS IN WATER CLARITY IN THE CHESAPEAKE BAY AS REVEALED BY SATELLITE OCEAN COLOR DATA

Satellite remote sensing data are broadly used to monitor water clarity in inland, coastal, and open ocean waters with high spatial and temporal resolutions. In this study, we focus on the Chesapeake Bay, the largest estuary in the U.S., that is characterized by highly productive waters with a large amount of river freshwaters containing dissolved and particulate materials. A complex mixture of chlorophyll concentration, colored dissolved organic matter (CDOM), and total suspended sediments (TSS) strongly affect both water quality and optical properties. Water clarity is a critical water quality parameter to measure light availability within the water column, particularly in the turbid coastal waters such as the Chesapeake Bay, and used for better understanding phytoplankton primary production and habitat suitability of submerged aquatic vegetation. We evaluate a recently proposed algorithm to retrieve Secchi Disk Depth (Zsd) from satellite ocean color data for the turbid coastal waters in the Chesapeake Bay and compare it to a new local Zsd algorithm for the Chesapeake Bay using a Random Forest Machine Learning method. Both algorithms are applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002-2022) on board Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (SNPP) data (2012-2023) to generate seasonal and interannual variability in the Chesapeake Bay, and the time series are used to characterize spatial and temporal variation of water clarity in the Chesapeake Bay.

Seunghyun Son, University of Maryland, USA

Code of Conduct
Privacy Policy