Soil salinity is a widespread natural hazard that negatively influences soil fertility and crop productivity. Using the potential of earth observation data and remote sensing technologies provides an opportunity to address this environmental issue and makes it possible to identify salt-affected regions accurately. While most of the utilized methods and model development techniques for monitoring soil salinity to date have been globally considered and tried to detect salinity and create predictive maps with a single regression algorithm, fewer studies have investigated the potential of local models and weighted regression techniques for estimating soil salinity. Accordingly, this research deals with monitoring surface soil salinity by the potential of Sentinel-2 multispectral imagery using the geographic weighted regression (GWR) technique. The field study was conducted in an area that has suffered from salinization, and the salinity of several soil samples was measured to be used as a source of ground truth data. The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis. The GWR algorithm was then implemented with a fixed Gaussian kernel, and the optimized bandwidth was calculated in a calibration process using the cross-validation score (CV score). The results of the analysis proved that the GWR method has a great capability to predict soil salinity with an accuracy of two decimal places. The visual interpretation of the local estimates of coefficients and local t-values for each predictor variable has also been provided, which highlights the local variations in the study site. Finally, the achieved results were compared with the outcomes obtained from implementing two global regression techniques, Support Vector Machines (SVM), and Multiple Linear Regression (MLR), which confirmed the higher performance of the GWR algorithm.

土壤盐分是一种广泛的自然灾害，会对土壤肥力和农作物生产力产生负面影响。利用地球观测数据和遥感技术的潜力为解决这一环境问题提供了机会，并使准确识别受盐影响的地区成为可能。迄今为止，虽然已广泛考虑了大多数用于监测土壤盐分的方法和模型开发技术，并试图通过单一回归算法检测盐分并创建预测图，但很少有研究调查了局部模型和加权回归技术在估算中的潜力。土壤盐分。因此，本研究利用地理加权回归（GWR）技术通过Sentinel-2多光谱图像的潜力来监测地表土壤盐分。在盐碱化地区进行了实地研究，并测量了一些土壤样品的盐度，以用作地面真实数据的来源。最有效的卫星特征是通过Sentinel-2数据得出的，该特征可以通过较高的光谱反射率准确地预测表层土壤的盐度，并用作分析中的解释变量。然后，使用固定的高斯核实现GWR算法，并使用交叉验证得分（CV得分）在校准过程中计算出优化的带宽。分析结果证明，GWR方法具有很好的预测土壤盐分的能力，精确度为两位小数。还提供了每个预测变量的系数局部估计值和局部t值的视觉解释，突出显示了研究地点的局部差异。最后，将获得的结果与通过实施两种全局回归技术（支持向量机（SVM）和多重线性回归（MLR））获得的结果进行比较，这证实了GWR算法的更高性能。