Abstract Traditional soil salinity studies, especially over large areas, are expensive and time-consuming. Therefore, it is necessary to employ new methods to examine salinity of large areas to reduce the time and cost of analysis. This study investigates soil salinity trends in the Yazd-Ardakan plain of Iran using remote sensing with emphasis on historic and projected land use and groundwater change between 1986 and 2030. A random forest model was used to estimate soil salinity. To predict the salinity of the Yazd-Ardakan plain in 2030, the relationships between soil and auxiliary data from 2016 were used. Land use parameters and groundwater quality parameters that are projected to change by 2030 were selected. A sensitivity analysis of a forage management model was conducted in conjunction with soil salinity modeling and the most important auxiliary data were found to be groundwater parameters and digital elevation derivatives of vegetation indices. Based on 10-fold cross-validation, random forest model predicted soil salinity with R2 value of 0.73. Comparison of soil salinity trends from 1986 to 2016 shows that during this period the size of the area with salinities in the range of 4–8 dS/m and >32 dS/m were increased from 1.6 to 3.1% (~1.5%↑) and from 13.1 to 18.3% (~5.1%↑), respectively. However, the size of the fairly high (8–12 dS/m), high (12–16 dS/m) and very high (16–32 dS/m) classes were decreased from 13.6 to 11.9% (~1.7%↓), from 20.2 to 16.5% (~3.8%↓), and from 50.2 to 49% (~1.1%↓) , respectively. In other words, it can be said that during this 30-years, we see an increase in salinity levels and a decrease in soil quality. The results of the changes in soil salinity show that between 2016 and 2030, the area of the class with >32 (dS/m) (43159.2 ha, 8.83%↑) increased, while the class with

中文翻译：

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利用随机森林研究伊朗中部沙漠土壤盐分时空变化

摘要 传统的土壤盐分研究，尤其是大面积研究，既昂贵又费时。因此，有必要采用新的方法来检测大面积的盐度，以减少分析的时间和成本。本研究使用遥感调查了伊朗亚兹德-阿尔达坎平原的土壤盐度趋势，重点是 1986 年至 2030 年间历史和预测的土地利用和地下水变化。随机森林模型用于估计土壤盐度。为了预测 2030 年亚兹德-阿尔达坎平原的盐度，使用了 2016 年的土壤和辅助数据之间的关系。选择了预计到 2030 年会发生变化的土地利用参数和地下水质量参数。结合土壤盐度模型对牧草管理模型进行了敏感性分析，发现最重要的辅助数据是地下水参数和植被指数的数字高程导数。基于 10 倍交叉验证，随机森林模型预测土壤盐度，R2 值为 0.73。1986-2016年土壤盐分变化趋势对比表明，在此期间，盐度在4-8 dS/m和>32 dS/m范围内的区域面积从1.6%增加到3.1%（~1.5%↑）分别从 13.1% 到 18.3% (~5.1%↑)。然而，相当高 (8–12 dS/m)、高 (12–16 dS/m) 和非常高 (16–32 dS/m) 等级的大小从 13.6% 减少到 11.9% (~1.7%↓ )，分别从 20.2 到 16.5% (~3.8%↓) 和从 50.2 到 49% (~1.1%↓)。换句话说，可以说，在这30年里，我们看到盐分增加，土壤质量下降。土壤盐分变化结果表明，2016-2030年间，>32(dS/m)(43159.2 ha, 8.83%↑)类的面积增加，而