Remote Sensing Observations of Phosphorus in Eutrophic Lakes: From Concentration to Storage

Hydrological processes drive the transport of phosphorus (P) from soil to surface water. This study is the first to use space-based observations to examine P storage in watershed lakes. Based on the vertical distribution characteristics of the total P (TP) concentration in multiple eutrophic lakes, a remote sensing estimation method for water column integrated P storage in eutrophic lakes was proposed using machine learning. The results showed that the TP profile followed a quadratic distribution that was primarily influenced by chlorophyll-a in shallow water and suspended particulate matter (SPM) in deep water. Based on this observation, a water column TP mass estimation algorithm was developed using extreme gradient boosting (XGBoost) to estimate surface TP, combined with an adjusted floating algae index (FAI) and near-infrared band. The algorithm achieved $R^{\mathbf {2}}\ge 0.6$ , with the error increasing with depth. Then, water depth and lake spatial information were added to the algorithm, the average P storage of 35 large lakes in the Jianghuai region was calculated as 5347 t, and the lake area explains 85% of the P storage. The modeled P storage in Lake Taihu and Chaohu exhibited increasing trends that were mainly driven by the water level. This study is the first to observe lake P storage from space and to help elicidate the P cycle in shallow eutrophic lakes. At present, the Yangtze River Basin exports large amounts of P, lakes reduce P loss in the basin and enrich, and there is still great potential for the recycling and utilization of P resources.