Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad plain, northeastern Iran

• Published in: Environmental earth sciences Vol. 82; no. 9; p. 234
• Main Authors: Mousavi, Amin, Karimi, Alireza, Maleki, Sedigheh, Safari, Tayebeh, Taghizadeh-Mehrjardi, Ruhollah
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023; Springer Nature B.V
• Subjects: Agricultural management; Agriculture; Biogeosciences; Calcium; Calcium carbonate; Calcium carbonates; Carbonate rocks; Carbonates; Digital mapping; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geology; Geostatistics; Gypsum; Hydrology/Water Resources; Machine learning; Mapping; Modelling; Organic carbon; Organic soils; Original Article; Overtraining; Remote sensing; Soil management; Soil properties; Spatial data; Spatial variations; Statistical methods; Sustainability management; Terrain; Terrestrial Pollution; Topography; Vegetation; Vegetation index; Wetness index; Spatial prediction; Regression kriging (RK); Digital soil mapping (DSM); Random forest-ordinary kriging (RF-OK)
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-023-10919-x

Understanding the spatial variation of soil properties is essential for monitoring land capabilities as well as the sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables, i.e., terrain attributes and remote sensing (RS) indices, across 1500 km 2 of Mashhad plain lands. To achieve this purpose, a total of 180 soil samples (0–10 cm) were taken. The random forest (RF) model combined with ordinary kriging (OK), as well as regression kriging (RK), were applied to relate environmental variables and the studied soil properties. The results revealed that RF-OK was the best model with R 2 and RMSE for silt (0.89% and 0.10%), followed by calcium carbonate equivalent (0.88% and 3.30%), clay (0.87% and 2.26%), soil organic carbon (0.86% and 0.24%), sand (0.84% and 4.21%), and pH (0.82% and 5.42%). The RS covariates, including band 5 (B5), modified soil-adjusted vegetation index (MSAVI), difference vegetation index (DVI), band 2 (B2), carbonate rock index (CRI2), gypsum index (GI), and enhanced vegetation index (EVI), and terrain attributes, including topographic wetness index (TWI) and elevation (EL), and topographic position index (TPI), were the most important variables in modeling different soil properties. RF-OK showed the prediction and uncertainty maps related to high precision and low standard deviation in most study areas, which indicate low overfitting and overtraining in modeling processes. In general, the RF-OK model, with low cost and high accuracy, can be applicable to use for predicting different soil properties, as well as spatial information acquired from an effort to maps to managing agriculture in areas at different conditions. Finally, this method can be applied to other regions of similar properties and for similar purposes.