Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

Abstract

AbstractTo explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R2 of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes.