Remote Sensing of Water Quality Parameters over Western Mississippi Sound by Using Sentinel-3 OLCI and Machine Learning

Remote sensing has emerged as a crucial tool for monitoring water quality, offering a cost-effective and spatially comprehensive approach for monitoring water quality and assessing aquatic ecosystem health. The remote sensing sensor, Ocean and Land Color Instrument (OLCI), onboard the Sentinel 3A satellite provides significant opportunities for monitoring coastal waters. With a spatial resolution of 300 m across 21 spectral bands, it enables extraction of detailed information about water quality parameters. In this study, we investigated the feasibility of using OLCI products to monitor an optically complex coastal water body in the western Mississippi Sound (WMS), building on the potential of remote sensing to address water quality issues. We employed machine learning (ML) and deep learning models for developing algorithms for chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), turbidity, and surface dissolved oxygen using OLCI imagery and in situ measurements by an autonomous surface vessel in 2021, 2022, and 2023 from WMS. Our approach involved applying automatic model selection algorithms to determine the optimal combination and number of spectral bands for training models. Notably, for Chl-a estimation, the random forest (RF) model yielded the highest adjusted (Adj) R² of 0.96 with a root mean squared error (RMSE) of 0.31 μg/L. For CDOM, the RF, extreme gradient boosting (XGBoost), and decision tree models produced high Adj R² values of 0.99, 0.98, and 0.98, with RMSEs of 1.18, 1.16, and 1.18 μg/L, respectively. Similarly, for turbidity, RF, XGBoost, and K-Nearest Neighbors models emerged as top performers, demonstrating high accuracy. For dissolved oxygen estimation, RF and XGBoost models exhibited robust performance across various metrics, both achieving an Adj R² of 0.89, indicating an excellent fit between the estimated and actual values. This study demonstrates the efficacy of utilizing OLCI products coupled with ML techniques for robust monitoring of water quality parameters in optically complex coastal environments, significantly contributing to enhanced environmental monitoring, management, and conservation efforts.

Presentation Preference: Oral

Primary Presenter: hafez ahmad, Mississippi State University (hafezahmad100@gmail.com)

Authors:

Padmanava Dash, Mississippi State University  (pd175@msstate.edu)

Gray Turnage, Geosystems Research Institute, Mississippi State University  (lgt4@msstate.edu)

Robert J. Moorhead, Geosystems Research Institute, Mississippi State University  (rjm9@msstate.edu)