Land degradability mapping using remote sensing data and soil chemical properties

Selection of the most influential soil properties to land degradability (LDy) mapping and their association is unclear. Accordingly, this study developed a geo-environmental modelling approach using multi-source datasets including remote sensing imagery, meteorological data, and soil properties (electrical conductivity (EC), soil organic matter (SOM), and calcium carbonate (CaCO3)) to LDy mapping in Jajrud and Karaj basin of Iran. LDy reference map obtained from soil properties data and Hedged Product overlay method was used to train and validate the random forest model. A sensitivity analysis was performed, which showed that normalized difference vegetation index (NDVI) is the most effective variable in LDy mapping. Under data constraints, the combination of biological soil crust index, leaf area index, and maximum wind speed is the best dataset for LDy mapping. Results revealed that the study area's most degraded lands were primarily barelands, followed by farmlands, grasslands, and fallow lands, respectively. Besides, considering their lower EC, the highlands and fallowlands did not witness land degradation.