Remote Estimation of the Particulate Phosphorus Concentrations in Inland Water Bodies: A Case Study in Hongze Lake

Abstract

Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. In this study, we take Hongze Lake as the research area and establish a semianalytical model to estimate PP concentrations based on the total particle absorption coefficient (ap); the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values, which indicate the model accuracy, were 14.90% and 0.009 mg/L, respectively. In addition, the construction process and parameter selection criteria of the remote sensing-based PP concentration estimation model were derived using remote sensing data obtained at different spectral resolutions. Sentinel 3 Ocean and Land Color Instrument (OLCI) and Landsat 9 Operational Land Imager version 2 (OLI-2) data were selected as representatives to verify the accuracy of the model; compared to these two datasets, the MAPE values of the models were 16.32% and 26.84%, respectively, while the RMSE values were 0.010 mg/L and 0.014 mg/L, respectively. Finally, the models were applied to Sentinel 3 OLCI and Landsat 9 OLI-2 images obtained on 16 January 2022. The results show that the spatiotemporal distributions of PP concentrations in Hongze Lake estimated from these two images were relatively consistent, but the OLI data reflected overestimations and underestimations in some areas. These research results provide a new methodology for estimating PP concentrations through remote sensing methods and help to further improve the accuracy of remotely sensed PP concentration estimations in inland water bodies.