Machine Learning Models for Prediction of Soil Properties in the Riparian Forests

Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhile, riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, 103 soil samples were taken using the Latin hypercubic method in the Maroon riparian forest of Behbahan and agricultural lands in the vicinity of the forest to evaluate the spatial variability of soil nitrogen, potassium, organic carbon, C:N ratio, pH, calcium carbonate, sand, silt, clay, and bulk density. Different machine learning models, including artificial neural networks, random forest, cubist regression tree, and k-nearest neighbor were used to compare the estimation of soil properties. Moreover, three main sources of spatial information including remote sensing images, digital elevation model, and climate parameters were used as ancillary data. Our results indicated that the random forest model has the best results in estimating soil pH, nitrogen, potassium, and bulk density. In contrast, the cubist regression tree indicated the best estimation for organic carbon, C:N ratio, phosphorous, and clay. Further, artificial neural networks showed the best estimation for calcium carbonate, sand, and silt contents. Our results revealed that geospatial information such as terrain parameters, climate parameters, and satellite images could be well used as ancillary data for the spatial mapping of soil physiochemical properties in riparian forests and agricultural lands. In conclusion, a specific machine learning model needs to be used for each soil property to provide highly accurate maps with less error.