Conventional and digital soil mapping in Iran: Past, present, and future

Abstract

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.