Urmia Lake is a shallow terminal Lake located in northwest Iran and it is one of the largest permanent Lakes in the Middle East. In this study, the changes in soil salinity at Urmia Lake were investigated using satellite images and the oldest salinity map of the area over a period of 45 years from 1973 to 2018. The distribution of salinity in 2018 was estimated using the supervised classification by the nonlinear hybrid model of artificial multi-layered neural network-genetic algorithm model (ANN-GA) while the salinity map for the years of 1985, 1995, 2005 and 2015 was estimated by the unsupervised method. Further, the salinity data of surface soil in the region for the year 1973 was also digitized and utilized. For this purpose, 291 surface samples (258 samples for modeling and 33 samples for the re-evaluation of the model) of the studied region were collected and analyzed in 2018. The input neurons were selected by analyzing the satellite imagery bands, salinity indices, salinity ratio index and normalized difference vegetation index. The correlation coefficient and root-mean-square error of the training network model were equal to 0.94 and 0.04, respectively. The salinity map of the studied region was estimated using this model and classified into six classes (S0 to S5). The produced map of 2018 was used to re-evaluate the results. It showed that lower estimation accuracy was in classes S1 and S2. The obtained results in this study indicated that roughness, moisture, the density of halophyte plants and sodium slickspot were some of the sources for estimation of errors in lower salinity classes. The time-series changes in the salinity class of estimated maps showed that S3, S4 and S5 classes have expanded between 1973 and 2018. These are in agreement with the field observation and with the other scientific reports about the studied area.

乌尔米亚湖是位于伊朗西北部的浅水终端湖，是中东地区最大的永久性湖泊之一。在这项研究中，利用卫星图像和该地区最古老的盐度地图（1973年至2018年）调查了Urmia湖土壤盐分的变化.2018年盐分的分布是通过监督分类法估算的。人工多层神经网络-遗传算法模型（ANN-GA）的非线性混合模型，同时采用无监督方法估算了1985、1995、2005和2015年的盐度图。此外，该地区1973年表层土壤的盐度数据也被数字化和利用。以此目的，在2018年收集并分析了291个表面样本（258个用于建模的样本和33个用于模型重新评估的样本）并进行了分析。通过分析卫星图像波段，盐度指数，盐度比指数和盐度来选择输入神经元归一化植被指数 训练网络模型的相关系数和均方根误差分别等于0.94和0.04。使用该模型估算了研究区域的盐度图，并将其分为六类（S0至S5）。制作的2018年地图用于重新评估结果。结果表明，S1和S2类的估计准确性较低。在这项研究中获得的结果表明，粗糙度，湿度，盐生植物的密度和浮油酸钠是估计低盐度等级误差的一些来源。估计图的盐度类别的时间序列变化表明，S3，S4和S5类别在1973年至2018年之间有所扩展。这与实地观察以及有关研究区域的其他科学报告相一致。