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The Songnen Plain of Northeast China is one of the three largest soda saline–alkaline regions in the world. To better understand soil alkalinization in this important agricultural region of China, it is vital to explore the driving factors of soil alkalinity. Combined with prior research on the Wuyu’er–Shuangyang River Basin, this study examined the driving factors of soil alkalinity using the Geodetector method. First, the analysis results of the risk detector, the factor detector, and the ecological detector revealed the primary driving factors of soil alkalinity in the study area. Next, the analysis results of the interaction detector determined how combinations of driving factors impacted soil alkalinity in the study area. In general, the natural driving factors of altitude and spring temperature, especially altitude, played a key role in soil alkalinization. These results indicated that the closed terrain and warming trends were the main causes of soil alkalinization in the study area. In addition, there were significant enhance-nonlinear and enhance-bivariate relationships among the driving factors, which indicated that combined driving factors contributed more to soil alkalinization than individual driving factors. Full article

The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na₂CO₃ and NaHCO₃ in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R²_Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area. Full article