Spatial intersection of soil texture classes and landscape features: An XGBoost-enhanced digital soil mapping approach

Soil texture classes (STCs) can be digitally mapped by first estimating particle-size fractions (PSFs). This study focuses on developing an extreme gradient boosting (XGBoost) model to estimate the spatial distribution of PSFs for a soil depth of 0–50 cm. A set of 30 predictors categorized into main terrain-related attributes, RS data derived from Landsat 9 OLI, climate, and decoded surficial soil color from hue-value-chroma to red-green-blue was used (hereafter referred to as the total dataset). Principal component analysis was performed to minimize the size of the predictors (hereafter referred to as the minimum dataset). In addition to the spatial distribution map of PSFs, the 95% confidence intervals were mapped. Subsequently, basic and sub-categorized STCs were mapped for the entire study area. The spatial intersection of soil texture classes with three landscape features was examined: land use/land cover (LULC), terrain morphological units (TMUs), and slope aspects (north-ward, south-ward, east-ward, and west-ward). Based on 5-fold cross-validation, sand and clay were estimated more accurately using the minimum set of predictor proxies. Allocating original STCs as a reference to four classification systems revealed that the Canadian and USDA systems have the highest agreement (kappa = 0.51 and 0.49, OA = 64% and 63%), followed by the UK and ISSS systems. Nevertheless, the USDA system, widely adopted internationally, was used for mapping. According to the results, the XGBoost model produced more accurate estimates for the sandy clay and sandy clay loam texture classes. Additionally, a higher clay content was observed on the northern slopes compared with the southern slope; grassland soils indicated a 2.5-fold greater area of fine texture and a 2.45-fold lower area of coarse texture than cropland soils; and a 2.27-fold high level of fine texture was found in the north-ward (NW: 0°- 45° and 315°-360°) compared to the south-ward (SW: 135°-225°) over the 0–50 cm depth. It was ultimately concluded that including soil color as a low-cost proxy predictor not only improves PSF estimation accuracy but also, through feature selection, enhances the accuracy of soil texture mapping across the study area located in northwestern Iran.