Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks

Total phosphorus (TP) and total nitrogen (TN) represent the primary water quality parameters indicative of the eutrophication status in the mainstream of the Yangtze River. Nowadays, satellite remote sensing offers an economical and efficient method for monitoring the water environment with a broad geographical scope, while single satellite and traditional methods are still limited. In this paper, inversion models of TN and TP are constructed and evaluated based on the neural networks (NNs) algorithm and random forest (RF) algorithm in the upper, middle, and lower reaches of the Yangtze River, respectively. Subsequently, the monthly variations of TN and TP concentrations are estimated and analyzed in the mainstream of the Yangtze River using Landsat-8 and Sentinel-2 satellites images from January 2016 to December 2022. The results show that the NNs model exhibits better estimation performance than the RF model within the study area. The accuracy of the TN model varies across different sections, with R2 values of 0.70 in the upstream, 0.67 in the midstream, and 0.74 in the downstream, accompanied by respective RMSE values of 0.21 mg/L, 0.21 mg/L, and 0.23 mg/L. Similarly, the TP model exhibits varying accuracy in different sections, with R2 values of 0.71 in the upstream, 0.69 in the midstream, and 0.78 in the downstream, along with corresponding RMSE values of 0.008 mg/L, 0.012 mg/L, and 0.008 mg/L. From 2016 to 2022, the concentrations of TN and TP in the mainstream of the Yangtze River exhibited an overall downward trend, with TN decreasing by 13.7% and TP decreasing by 46.2%. Furthermore, this study also gives the possible causes of water quality changes in the mainstream of the Yangtze River with a specific focus on hydrometeorological factors.

Automated summary

This paper constructs and evaluates the inversion models of total phosphorus (TP) and total nitrogen (TN) in the upper, middle, and lower reaches of the Yangtze River using satellite remote sensing. The monthly variations of TP and TN concentrations in the mainstream of the Yangtze River from 2016 to 2022 are estimated and analyzed. The results show that the neural networks (NNs) model exhibits better estimation performance and the concentrations of TN and TP exhibit an overall downward trend. The possible causes of water quality changes in the mainstream of the Yangtze River are also discussed, with a specific focus on hydrometeorological factors.