Dynamic Water Quality Changes in the Main Stream of the Yangtze River from Multi-Source Remote Sensing Data

Total nitrogen (TN) and total phosphorus (TP) are important indicators of water quality. Although water quality can be obtained with high accuracy using traditional measurement methods, the cost is high and the area is limited. In the past a single-satellite remote sensing system was normally used to estimate water quality at a large scale, while bands were fewer with limited accuracy. In this paper, inversion models for TN and TP are obtained and validated in the main stream of the Yangtze River using multi-source remote sensing data. The joint inversion models for TN and TP have higher accuracy (R-2=0.81 and 0.86, RMSE=0.51 and 0.10 mg L-1) than the single-satellite inversion models (R-2=0.61-0.62 and 0.59-0.75, RMSE=0.41-0.61 and 0.07-0.12 mg L-1). Using these models, water quality changes in the Yangtze River are obtained from 2019 to 2021. It is found that TN and TP in the upstream and downstream are high. In spring and autumn, the water quality is poor. The water quality in the Yangtze River is mostly Class III with improvement. Furthermore, it is found that TN and TP are negatively correlated with the water level, temperature and flow in Jiujiang. The p value between water quality and the water level is higher than for other factors, with -0.76 and -0.64 for TN and TP, respectively.

Automated summary

In this study, inversion models for total nitrogen (TN) and total phosphorus (TP) in the main stream of the Yangtze River were obtained and validated using multi-source remote sensing data. The joint inversion models showed higher accuracy than the single-satellite inversion models. The water quality changes from 2019 to 2021 in the Yangtze River were analyzed, revealing high TN and TP levels in the upstream and downstream, poor water quality in spring and autumn, and overall improvement in water quality with most of it classified as Class III. Additionally, a negative correlation was found between TN and TP concentrations and water level, temperature, and flow in the Jiujiang area, with water level showing the highest correlation.