Strengthening Remote Sensing-Based Estimation of Riverine Total Phosphorus Concentrations by Incorporating Land Surface Temperature

Direct retrieval of Total Phosphorus (TP) from remote sensing is not possible because TP is not optically active. Unlike optically active parameters, TP does not exhibit spectral signals and relies on indirect correlations with Optically Active Constituents (OACs) such as Chl-a and suspended solids. Existing approaches often rely solely on spectral reflectance while neglecting the environmental variables, such as temperature, that can affect the correlations between OACs such as Chl-a and temperature. To address this, this study integrates satellite-derived Land Surface Temperature (LST) with Landsat 8/9 spectral features, utilizing LST as a spatial proxy for the aquatic thermodynamic environment. Focusing on the Dongjiang River, a subtropical river in China, a machine learning framework was constructed based on in situ measurements collected from 2020 to 2023. Feature selection using Pearson’s correlation and Random Forest importance identified the optimal combination of spectral bands and thermal inputs. The results from the model revealed the following: (1) annual mean TP concentrations in the delta were higher than in the main channel, with more pronounced seasonal fluctuations; (2) statistical verification (Wilcoxon signed-rank test, p < 0.01) confirmed that incorporating LST yielded a certain reduction in retrieval error compared to the spectral-only model; (3) the most influential predictors for TP estimation were a combination of the blue, green, and red spectral bands along with LST; (4) models incorporating LST achieved significantly higher accuracy than those based solely on spectral reflectance, with improved R2 and RMSE values across most TP concentration ranges (except for 0.04–0.06 mg/L). These findings demonstrate that integrating LST with spectral features enhances the accuracy of remote sensing-based TP retrieval in rivers, offering new opportunities for improved large-scale water quality monitoring.