Evaluation of Different Approaches for Assessing Water Quality Using Sentinel-2/MSI: A Case Study in Coastal Ningde
2026
Binbin Jiang | Daidu Fan | Qinghui Huang | Xueding Li | Nguyen Dac Ve | Fahui Ren | Junyu Yu | Emmanuel Boss
Water quality observations are vital for effectively managing coastal resources and influencing decisions from emergency beach closures to aquaculture leasing agreements. This study focuses on deriving two water quality parameters—Chlorophyll <i>a</i> (Chl-a) and suspended particulate matter (SPM)—through the high-resolution multispectral imager (MSI) onboard the Sentinel 2A&B satellites, specifically for the Ningde coastal region, which is a crucial aquaculture hub in China. Since more than 90% of the signals captured by satellites are affected by atmospheric interference, it is crucial to apply a process called “atmospheric correction” (AC) to isolate the water contribution, known as water leaving reflectance, from the radiance measured at the top of the atmosphere. Our research assesses five published AC models and various algorithms designed to accurately estimate Chl-a and SPM from water leaving reflectance. We determine the most effective combination by comparing these findings against in situ data gathered from eleven locations in the Ningde coastal region (POLYMER-SOLID with lowest metric RMSLE (0.29), and MAE (1.68) and POLYMER-MDN with the lowest metric RMSLE (0.59), and MAE (0.56)). Our study underscores the importance of selecting locally validated AC models and algorithms for generating water quality products, as this enhances the utility of remote sensing data in monitoring water quality. Moreover, we conduct a spatiotemporal analysis of the water quality parameters from 2016 to 2021, revealing significant interannual variability that underlines the need for continuous monitoring and robust data analysis in coastal management efforts.
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