Journal of Range and Watershed Managment

Abstract Accurate spatial data on soil property distribution is crucial for monitoring of land resources, informed management practices, and robust environmental modeling, especially in arid and semi-arid regions. This study aimed to develop a spatial prediction model for soil salinity in the Meymeh Plain, Dehloran Province. The Random Forest (RF) algorithm was employed to investigate spatial variations in soil salinity within the surface (0–30 cm) and subsurface (30–60 cm) soil layers. Soil samples were collected from 100 sites, analyzed for electrical conductivity (EC), and the spatial variability of soil salinity was modeled using random forest (RF) analysis. Seven environmental variables of Greenery, Diffuse Radiation, Valley Bottom Flatness Index, Normalized Difference Vegetation Index, Salinity Index, Wind Direction Index, and Brightness were selected based on the Variance Inflation Factor, including parameters from a digital elevation model and Sentinel-2 satellite reflectance data. The model used 80% of the data for calibration and 20% for validation, with performance assessed through root mean square error (RMSE), coefficient of determination (R²), and concordance correlation coefficient (CCC). The RF model showed high prediction accuracy for surface EC and relatively acceptable results for subsurface layers. The R² for the surface layer was 0.92, and for the subsurface layer was 0.37; the RMSE for the surface and subsurface layers was 0.22; and the CCC for the surface layer was 0.82 and for the subsurface layer was 0.97. Overall, topographic derivatives demonstrated a greater influence on predicting soil salinity in both surface and subsurface layers compared to remote sensing data. The multi-resolution valley bottom flatness index with high spatial resolution was identified as the most important predictor of soil salinity, highlighting the impact of topographic factors in the study area.

Abstract Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally model Ks using machine learning approaches in the Kilanah watershed, located in Kurdistan Province, covering an area of 12,000 hectares. Three machine learning algorithms, including Gradient Boosted Decision Tree (XGBoost), Random Forest (RF), and k-Nearest Neighbors (k-NN), were utilized, incorporating various environmental variables derived from the digital elevation model and Sentinel-2 satellite imagery. These variables included distance from the drainage channel, valley depth, relative slope position, channel base level, brightness index, wind effect index, Normalized Difference Vegetation Index (NDVI), Band 12, greenness index, and surface curvature. Additionally, soil parameters such as organic matter, lime content, bulk density, geometric mean particle diameter, soil texture, and near-soil spectroscopic data (Latent Variable) within the wavelength range of 400–2450 nm were used as proxies for pedogenic factors to model saturated hydraulic conductivity. The results indicated that the XGBoost model exhibited the highest accuracy for predicting Ks, with an R² value of 0.65 and an nRMSE of 0.25, outperforming the other models. Spectral data, topographic variables, and soil parameters, as model inputs, played a significant role in predicting the spatial variability of Ks. The XGBoost model was able to provide highly accurate predictions. The results demonstrated that topographic, physical, and spectral variables influence Ks; organic matter, soil texture, and topographic indices such as slope and relative position had the most substantial impact. The generated maps can be utilized for water and soil resource management and hydrological models.

Abstract Mechanical properties of soil, such as shear strength and penetration resistance, play a crucial role in optimizing crop productivity and proper soil management. The objective of the research was to produce digital map of soil shear strength and penetration resistance in Kielaneh watershed, located in Kurdistan Province, covering an area of 12,000 hectares using Gradient Boosted Decision Trees (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). Soil penetration resistance and shear strength were measured using handheld penetrometers and vane shear devices at 150 observation points from the surface soil layer (0 to 10 centimeters). Spectral data and auxiliary variables derived from the Digital Elevation Model and Sentinel-2 satellite images were used to predict soil shear strength and penetration resistance. These variables include CHND, VD, RSP, CHNBL, Brightness, WE, NDVI, Band12, Greenness, PLC, as well as soil parameters such as organic matter, calcium carbonate, bulk density, geometric mean particle size, soil texture (percentages of clay, sand, silt), and visible near-infrared spectral data as latent variable (LT), representing soil formation factors. The results showed that the XGBoost had higher accuracy compared to other models for predicting shear strength in surface soil layer with an (R2) of 0.61 and an nRMSE of 0.16, as well as for predicting penetration resistance in the surface soil layer with an (R2) of 0.60 and an nRMSE of 0.11. In conclusion, the XGBoost model, using spectral data along with topographic variables and soil parameters, was able to estimate the spatial variability of soil mechanical properties with acceptable accuracy in the study area. The generated maps can be used to make necessary management decisions regarding of the region.