An improved algorithm for estimating the Secchi disk depth of inland waters across China based on Sentinel-2 MSI data

Accurate remote sensing of the Secchi disk depth (Z(SD)) in waters is beneficial for large-scale monitoring of the aquatic ecology of inland lakes. Herein, an improved algorithm (termed as Z(SD20) in this work) for retrieving Z(SD) was developed from field measured remote sensing data and is available for various waters including clear waters, slightly turbid waters, and highly turbid waters. The results show that Z(SD20) is robust in estimating Z(SD) in various inland waters. After further validation with an independent in situ dataset from 12 inland waters (0.1 m < Z(SD) < 18 m), the developed algorithm outperformed the native algorithm, with the mean absolute square percentage error (MAPE) reduced from 32.8 to 19.4%, and root mean square error (RMSE) from 0.87 to 0.67 m. At the same time, the new algorithm demonstrates its generality in various mainstreaming image data, including Ocean and Land Color Instrument (OLCI), Geostationary Ocean Color Imager (GOCI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Finally, the algorithm's application was implemented in 410 waters of China based on Sentinel-2 MSI imagery to elucidate the spatiotemporal variation of water clarity during 2015 and 2021. The new algorithm reveals great potential for estimating water clarity in various inland waters, offering important support for protection and restoration of aquatic environments.