This study provided a systematic review of the ecological issues arising from the development of saline-alkali land in China. These included secondary salinization, the formation of groundwater depression cones, wetland shrinkage and functional degradation, and reduction in natural vegetation, as well as high remediation costs and pollution risks. In addition, it clarified the technological development pathways for the comprehensive utilization of saline-alkali land. These pathways encompassed four major directions: targeted strategies under a systematic management approach, cost-effective remediation under new ecological requirements, dual-force development through land-crop synergy, and specialized agriculture aligned with the broader concept of food. Furthermore, the study proposed an integrated strategy to strengthen the comprehensive management of saline-alkali lands, including emphasizing zonal rehabilitation of saline-alkali farmland, establishing a collaborative innovation system, and advancing fundamental theories and key technologies for sustainable utilization. It also recommended developing a tiered land-use model to support pilot programs for reserve resources and cultivated land, promoting specialized agriculture, enhancing productive capacity, advancing water-adapted planting, fostering innovation in water-saving agricultural technology, and strengthening ecological monitoring and impact assessment. This study provided the theoretical foundation and strategic support for ecological protection in the comprehensive utilization of saline-alkali land in China.
【Objective】Soil salinization is a key environmental problem threatening the sustainable development of agriculture in arid areas, leading to the deterioration of soil structure, crop yield reduction and ecosystem degradation. The purpose of this study is to use spectral transformation, band selection and a variety of machine learning methods to build a soil salinity prediction model, which can quickly and accurately estimate soil salinity, and provide technical support for the scientific management of salinized farmland.【Method】Taking farmland soil in Dalate Banner as the research object, soil samples were collected systematically and their electrical conductivity (EC) and spectral reflectance data were measured. Firstly, Savitzky-Golay (S-G) filter was used to smooth the original spectrum (R). On this basis, 12 kinds of spectral transformation processing including reciprocal, logarithmic, first-order differential and second-order differential were carried out to mine the hidden spectral features. Then, the correlation analysis (CA) and least angle regression (LAR) methods were used to reduce the feature dimension, and the competitive adaptive reweighted sampling (CARS) algorithm was combined to further screen the sensitive feature bands. Finally, partial least squares regression (PLSR), support vector machine (SVM), back propagation neural network (BPNN) and random forest (RF) models were constructed based on the optimal features. The performance of the model was comprehensively evaluated by determination coefficient (R2) and root mean square error (RMSE), and the modeling effects of feature sets in different algorithms were compared.【Result】After spectral transformation, the correlation coefficients of the original spectrum were improved in varying degrees, indicating that spectral transformation could significantly enhance the correlation between soil salinity and spectral characteristics; When CARS was used for feature band optimization, LAR had better feature dimension reduction effect than CA; The reciprocal logarithmic first-order differential (ATFD) combined with PLSR model performed best, and its validation set accuracy was R2=0.81, RMSE=2.04 dS·m-1; The comparison of different modeling methods showed that the performance of PLSR prediction model was better than the other three models (BPNN/RF/SVM), indicating that PLSR model was more suitable for the prediction of soil salinity in this region.【Conclusion】The hyperspectral prediction model of soil salinity based on ATFD-LAR-CARS-PLSR has high accuracy and optimal prediction ability, which proves that hyperspectral technology combined with multi-dimensional feature optimization can effectively realize the prediction of soil salinity in arid areas.
【Background】Soil salinization severely constrains crop growth and ecological balance, and its accurate monitoring is essential for saline-alkali land reclamation, yield forecasting, and precision farmland management. Driven by natural and anthropogenic factors, salinization is governed by the redistribution of water and salt within the soil profile, exhibiting pronounced vertical migration and strong spatial heterogeneity. Although unmanned aerial vehicle (UAV) remote sensing is now widely used for field-scale salinity mapping, it essentially captures surface information and fails to characterize salt gradients in deeper layers. 【Objective】To develop a UAV-image-based, layer-specific modeling framework that integrates machine learning with Kriging interpolation for high-resolution 3-D mapping of subsurface soil salinity.【Method】Firstly, the UAV was equipped with multispectral sensors to obtain high-resolution images of the test field, and the soil salinity data at different depths were measured synchronously, supplemented by real-time dynamic differential positioning technology to ensure spatial accuracy. Then, a spectral feature set including the red-edge band was constructed, and the feature optimization was carried out based on the random forest algorithm. On this basis, machine learning and Kriging interpolation method were combined to establish a stratified soil salinity prediction model and generate a high-resolution salinity distribution map. Finally, the advantages of the proposed method in the spatial representation of deep salinization were verified by comparing it with the cubic fitting depth function prediction method.【Result】The prediction accuracy R2 of each depth of deep soil salinization spatial prediction by the mixed model hierarchical modeling was 0.68 (0-10 cm), 0.51 (10-20 cm), 0.58 (20-40 cm), 0.56 (40-60 cm) and 0.52 (60-80 cm), respectively, and the prediction effect of 0-10 cm surface layer was the best. The red-edged salinity index was an important predictor at all depths, which verified the applicability and effectiveness of the constructed red-edged index. By comparing the prediction results of the mixed model with the cubic fitting depth function, the spatial prediction accuracy of deep soil salinization in the layered model of the mixed model was higher, and it could more truly reflect the salinization degree at different depths in the experimental area.【Conclusion】UAV remote sensing technology is the best in shallow (0-10 cm) soil salinity prediction, and the prediction accuracy of soil properties decreases with the increase of depth, and the depth accuracy still needs to be improved. From the prediction results, the average soil salinity gradually increases with the increase of depth, indicating that there is an accumulation phenomenon of salt in the soil profile. Compared with the cubic fitting depth function method, the hybrid model based on random forest stratification modeling and Kriging residual correction shows higher spatial prediction accuracy in each soil layer, which is more reasonable and practical, and provides a scientific basis for dynamic monitoring of regional soil salinization and accurate layered soil salinity mapping.
