Demand for accurate soil information is increasing for various applications. This paper investigates the history of soil survey in Iran, particularly more recent developments in the use of digital soil mapping (DSM) approaches rather than conventional soil mapping (CSM) methods. A 2000–2019 literature search of articles on DSM of areas of Iran in international journals found 40 studies. These showed an increase in frequency over time, and most were completed in the arid and semi-arid regions of central Iran. Artificial Neural Networks (ANN), Random Forests (RF), and Multinomial Logistic Regression (MnLR) were the most commonly applied models for predicting soil classes and properties and ANN performed best in most comparative studies. Given the scale of inquiry of most studies (local or regional), quantitative environmental variables such as terrain attributes and remote sensing data were frequently used whereas qualitative variables such as geomorphology, geology, land use, and legacy soil maps were rarely used. The literature review of CSM showed that this method is incapable of defining the spatial distribution of soils and also provides a lower accuracy than DSM method. This review has identified research gaps that need filling. In Iran, coherent national scale DSM with consistent methodology is needed to update legacy soil maps, and to apply DSM in forestlands, hillslopes, deserts, and mountainous regions which have largely been ignored in recent DSM studies. This review should also be useful for producing more local and regional digital soil maps more rapidly as it helps show which covariates and mathematical methods have been best suited to this scale of DSM in Iran.

对于各种应用，对准确的土壤信息的需求正在增长。本文调查了伊朗土壤调查的历史，特别是使用数字土壤测绘（DSM）方法而非常规土壤测绘（CSM）方法的最新进展。在2000-2019年的国际期刊中，有关伊朗DSM文章的文献检索发现40项研究。随着时间的推移，这些活动的频率在增加，并且大多数活动在伊朗中部的干旱和半干旱地区完成。人工神经网络（ANN），随机森林（RF）和多项式Lo​​gistic回归（MnLR）是预测土壤类型和性质的最常用模型，而ANN在大多数比较研究中表现最佳。鉴于大多数研究（本地或区域）的研究规模，经常使用定量环境变量（例如地形属性和遥感数据），而很少使用定性变量（例如地貌，地质，土地利用和遗留土壤图）。CSM的文献综述表明，该方法无法定义土壤的空间分布，并且比DSM方法精度低。这项审查确定了需要填补的研究空白。在伊朗，需要采用一致的方法对全国范围内的DSM进行更新，以更新遗留的土壤图，并将DSM应用于林地，山坡，沙漠和山区，这在最近的DSM研究中已被忽略。