Establishment of Remote Sensing Inversion Model and Its Application in Pollution Source Identification: A Case Study of East Lake in Wuhan

Abstract

In remote watersheds or large water bodies, monitoring water quality parameters is often impractical due to high costs and time-consuming processes. To address this issue, a cost-effective methodology based on remote sensing was developed to predict water quality parameters over a large and operationally challenging area, especially focusing on East Lake. Sentinel-2 satellite image data were used as a proxy, and a multiple linear regression model was developed to quantify water quality parameters, namely chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. This model was then applied to East Lake to obtain the temporal and spatial distribution of these water quality parameters. By identifying the locations with the highest concentrations along the boundaries of East Lake, potential pollution sources could be inferred. The results demonstrate that the developed multiple linear regression model provided a satisfactory relationship between the measured and simulated water quality parameters. The coefficients of determination R2 of the multiple linear regression models for chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand were 0.943, 0.781, 0.470, 0.624 and 0.777, respectively. The potential pollution source locations closely matched the officially published information on East Lake pollutant discharges. Therefore, using remote sensing imagery to establish a multiple linear regression model is a feasible approach for understanding the exceedance and distribution of various water quality parameters in East Lake.