The Pingluo area, as an experimental study area in Yinchuan, has been subjected to major environmental degradation due to soil salinization problems. Soil salinization is one of the main problems of land degradation in arid and semiarid regions. In the present study, remote sensing was integrated with mathematical modeling to evaluate soil salinization adequately. To detect soil salinization, soil water content and electrical conductivity of soil samples were analyzed. The reflectance of soil samples was measured using a spectrometer (SR-3500) with 1024 bands. Indices of soil salinity, vegetation and drought were analyzed using Landsat images over the study area. Based on Landsat images, physicochemical analysis, reflectance of sensitive bands for soil salinization and environmental indices, canopy response salinity index (CRSI), perpendicular drought index (PDI) and enhanced normalized difference vegetation index (ENDVI), a new model was established for simulation and prediction of soil salinization in the study area. Correlation analyses and multiple regression methods were used to construct an accurate model. The results showed that green, blue and near-infrared light was significantly correlated with soil salinity and that the spectral parameters improved this correlation significantly. Therefore, the model was more effective when combining spectral parameters with sensitive bands with modeling. After mathematical transformation of soil reflectance, the correlations of bands sensitive to soil salinization were 0.739 and 0.7 for electrical conductivity and water content, respectively. After transformation of vegetation reflectance, the correlation coefficient of soil salinity became 0.577. After inversion of the model based on soil hyperspectral and water content, the significance became 0.871 and 0.726, respectively, which can be used to predict soil salinity and water content. The spectral soil salinity model had a coefficient of 0.739 for soil salinity prediction. Among the salinity indices, the CRSI was selected as the most significant, with R² of 0.571, whereas the R² for PDI reached only 0.484. Among the vegetation indices, the ENDVI had the highest response to soil salinity, with R² of 0.577. After scale conversion, the correlation percentages between CRSI and measured soil salinity and between ENDVI and measured soil salinity increased to 16.2% and 8.5%, respectively. Following the correlation between PDI and soil water content, the percentage of correlation increased to 11.6%. The integration of hyperspectral remote sensing, ground methods and an inversion method for salinity is a very important and effective technique for rapid and nondestructive monitoring of soil salinization.

平罗地区作为银川的试验研究区，由于土壤盐渍化问题，环境恶化严重。土壤盐渍化是干旱和半干旱地区土地退化的主要问题之一。在本研究中，遥感与数学建模相结合，以充分评估土壤盐渍化。为了检测土壤盐渍化，分析了土壤样品的土壤含水量和电导率。使用具有 1024 个波段的光谱仪 (SR-3500) 测量土壤样品的反射率。使用研究区域的 Landsat 图像分析了土壤盐度、植被和干旱指数。基于 Landsat 图像、理化分析、土壤盐渍化敏感波段的反射率和环境指数、冠层响应盐度指数 (CRSI)、垂直干旱指数（PDI）和增强归一化差异植被指数（ENDVI），建立了研究区土壤盐渍化模拟和预测的新模型。相关分析和多元回归方法用于构建准确的模型。结果表明，绿光、蓝光和近红外光与土壤盐度显着相关，光谱参数显着改善了这种相关性。因此，当将光谱参数与敏感波段结合起来进行建模时，该模型更有效。对土壤反射率进行数学变换后，土壤盐渍化敏感波段的电导率和含水量相关系数分别为0.739和0.7。植被反射率变换后，土壤盐分的相关系数变为0.577。基于土壤高光谱和含水量的模型反演后，显着性分别变为0.871和0.726，可用于预测土壤盐分和含水量。光谱土壤盐度模型的土壤盐度预测系数为 0.739。在盐度指数中，CRSI 被选为最显着的，其中 R²的 0.571，而PDI的 R ²仅达到 0.484。在植被指数中，ENDVI对土壤盐分的响应最高，R ²为0.577。尺度转换后，CRSI与实测土壤盐分的相关百分比、ENDVI与实测土壤盐分的相关百分比分别增加到16.2%和8.5%。根据 PDI 与土壤含水量之间的相关性，相关性百分比增加到 11.6%。高光谱遥感、地面方法和盐度反演方法的结合是快速无损监测土壤盐渍化的一项非常重要和有效的技术。