Remote quantification of the trophic status of Chinese lakes

Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as inline-formula $M1inlinescrollmathml\mathrm{normal\; blue}/\mathrm{normal\; red}$ 44pt14ptsvg-formulamathimg9fec3a3ee81679f5b8317ce2b1de0660 hess-27-3581-2023-ie00001.svg44pt14pthess-27-3581-2023-ie00001.png , inline-formula $M2inlinescrollmathml\mathrm{normal\; green}/\mathrm{normal\; red}$ 52pt14ptsvg-formulamathimg96cd85bbd34db5e253b9419a83cc490d hess-27-3581-2023-ie00002.svg52pt14pthess-27-3581-2023-ie00002.png and inline-formula $M3inlinescrollmathml\mathrm{normal\; red}/\mathrm{normal\; red}$ 40pt14ptsvg-formulamathimg35fa873a6dd9ead1a77828c10cb848cf hess-27-3581-2023-ie00003.svg40pt14pthess-27-3581-2023-ie00003.png , which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (inline-formulaR²= 0.87, slope inline-formula= 0.85) and fewer errors (MAE inline-formula= 3.15 and RMSE inline-formula= 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.