Soil salinization can decrease soil productivity and is a significant factor in causing land degradation. Precision mapping of salinization in agricultural fields would improve farmland management. This study focuses on the cropland in the Manas River Basin, located in the arid region of northwest China. It explores the potential of a soil mapping method, the Soil–Land Inference Model (SoLIM), which only requires a small number of soil samples to infer soil salinization of farmlands in arid areas. The model was utilized to create spatial distribution maps of soil salinity for the years 2009 and 2017, and changes in the distribution were analyzed. The research results indicate: (1) Through the analysis of sample point data, it was observed that soil salinity in the study area tends to accumulate in the surface layer (0–30 cm) in spring and in the subsoil layer (60–90 cm) during the crop growing season, with significant spatial variability. Therefore, it is necessary to conduct detailed salinity mapping. (2) Using field measurements as validation data, the simulation results of the SoLIM were compared with spatial interpolation methods and regression models. The SoLIM showed higher inference accuracy, with R² values for the simulation results of the three soil layers all exceeding 0.5. (3) The SoLIM spatial inference showed salt accumulation in the northern part and desalination in the southern part. The findings of this study suggest that the SoLIM has the potential to effectively map soil salinization of croplands in arid areas, offering an efficient solution for monitoring soil salinity in arid oasis croplands.

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R²) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R² of 0.767 and an RMSE of 1.677 g·kg⁻¹. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.