22 Apr 2022
 | 22 Apr 2022
Status: this preprint is currently under review for the journal HESS.

Remote Quantification of the Trophic Status of Chinese Lakes

Sijia Li, Shiqi Xu, Kaishan Song, Tiit Kutser, Zhidan Wen, Ge Liu, Yingxin Shang, Lili Lyu, Hui Tao, Xiang Wang, Lele Zhang, and Fangfang Chen

Abstract. 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 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 blue/red, green/red, and red/red, 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 (R2 = 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 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 the XGBoost to map TSI in 2019–2020 with good quality Sentinel-2 Level-1C images embedded in ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10-m TSI products from investigated 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate, and anthropogenic activities. The methodological framework proposed herein could serve as a useful resource toward a continuous, long-term, and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.

Sijia Li et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2022-91', Yumei Li, 05 May 2022 reply
  • RC1: 'Comment on hess-2022-91', Anonymous Referee #1, 22 Jun 2022 reply

Sijia Li et al.


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Short summary
1. Blue/Red and Green/Red Rrs(λ) are sensitive to lake TSI. 2. Machine learning algorithms reveals optimum performance of TSI retrieval. 3. Accurate TSI model was achieved by MSI imagery data and XGBoost. 4. Trophic status in five limnetic regions was qualified. 5. The 10-m TSI products were first produced in typical 555 lakes in China.