High spatial resolution inversion of chromophoric dissolved organic matter (CDOM) concentrations in Ebinur Lake of arid Xinjiang, China: Implications for surface water quality monitoring

Abstract

Utilizing satellite remote sensing for the assessment and temporal-spatial analysis of Chromophoric Dissolved Organic Matter (CDOM) is vital for overseeing lake water health and devising management plans. This study focused on the saline, turbid and arid Ebinur Lake, located in China’s northwestern region. The Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) model algorithms were compared to select the one with the highest accuracy. It combined Sentinel-2 remote sensing data and in situ measurement data for the quantitative inversion of CDOM. Monthly CDOM distribution maps were generated with a 10 m resolution for the non-frozen months of May to October from 2018 to 2022, followed by a comprehensive analysis of temporal trends. The primary conclusions are: (1) The XGBoost model yielded highly accurate CDOM estimates, with a training set coefficient of determination (R2) of 0.94, a Root Mean Square Error (RMSE) of 0.06 mg/L, Mean Absolute Percentage Error (MAPE) of 6.05 %, Relative Percent Difference (RPD) of 4.07; the test set demonstrated an R2 of 0.41 with an RMSE of 0.22 mg/L, MAPE of 22.74 %, RPD of 1.35; (2) Throughout the study period, the main lake portion displayed variable CDOM spatial patterns and trends. The inversion indicated higher CDOM concentrations in the central part than nearshore areas and decreasing CDOM in tandem with seasonable water-surface shrinkage. The findings offer hints for an accurate evaluation of water color parameters of Ebinur Lake and practical references for monitoring arid-region lake water quality via remote sensing.

Keywords

• Ebinur lake
• Chromophoric dissolved organic matter (CDOM)
• Water quality monitoring
• Machine learning
• Remote sensing
• Spatiotemporal analysis