Enhanced Understanding of Key Soil Properties in Northern Xinjiang Using Water-Heat-Spectral Datasets Based on Bioclimatic Guidelines

Abstract

Current digital soil mapping of soil properties (soil organic carbon, SOC; electrical conductivity, EC; and pH) is mainly based on transfer learning, which is inadequate in terms of accuracy for the northern plain area of Xinjiang. To address this issue, establishing a new model is urgently required that can improve our understanding of the soil properties in this region. To this end, based on the global bioclimatic variables and surface dry–wet and wet–dry transitions, The study developed a spectral–water–heat database (SWHD). The study then incorporated this database and background data into machine learning algorithms (XGBoost, LightGBM, and random forest) to establish models applicable to the study area and draw spatial changes in the key soil properties. Our findings revealed that the organic carbon content was the highest in grasslands, whereas shrublands had high soil salinity. The pH value indicated overall alkalinity in the study area. Additionally, the SWHD-based predictions outperformed the mean or maximum value datasets, with LightGBM showing superior performance among all models. Furthermore, the validation accuracy obtained through our optimal algorithm was significantly higher than that obtained by other products, such as Harmonized World Soil Database (HWSD) and SoilGrid250, likely because of the limitations of these datasets, which may represent historical soil properties rather than current variations in the soil properties in the region. The study also observed that the mean SOC and EC values significantly decreased compared to the historical data, while the decrease in pH was smaller but not significant. Structural equation modeling and variable importance analysis revealed that the variables with the greatest influence on modeling SOC, EC, and pH were BIO10, DTW2021_406-426_B3 (Surface reflectance acquired in spring), and land use type. Our improved model developed based on the SWHD dataset offers important scientific evidence and decision support for land use management and provides a solid foundation for future research in this field.