Digital mapping of soil texture classes using Random Forest classification algorithm

• Published in: Soil use and management Vol. 38; no. 1; pp. 135 - 149
• Main Authors: Dharumarajan, Subramanian, Hegde, Rajendra
• Format: Journal Article
• Language: English
• Published: Bedfordshire Wiley Subscription Services, Inc 01.01.2022
• Subjects: Accuracy; Algorithms; Classification; classification algorithm; Clay; clay fraction; Clay soils; Climate change; Continuity (mathematics); Crop growth; Depth; Digital Elevation Models; Digital mapping; digital soil mapping; Distribution; Hydrology; India; Land use; Landsat; Mapping; Nutrient availability; Physical properties; prediction; random forest model; Regional planning; Remote sensing; Resolution; Sand; Silt; Soil; Soil classification; Soil depth; Soil mapping; Soil maps; Soil physical properties; Soil profiles; Soil properties; Soil texture; Spatial discrimination; Spatial distribution; Spatial resolution; Spline functions; Splines; Texture; USDA; India
• ISSN: 0266-0032; 1475-2743
• DOI: 10.1111/sum.12668

Soil texture is the most important soil physical property that determines water holding capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher spatial resolution at regional and national level is essential for crop planning and management. In the present study, we mapped the soil textural classes over 16.2 M ha area of Andhra Pradesh state, India, at 250 m spatial resolution up to 2 m depth using the digital soil mapping approach. A total of 2,272 profile observations were used for the prediction of soil textural classes using the Random Forest (RF) classification algorithm. To estimate textural classes at six standard soil depth intervals (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm), we used continuous depth function of texture distribution using average sand, silt and clay content of different textural classes. Depth‐wise spline outputs were then transformed into textural classes as per USDA textural classification. Sixteen environmental variables including Landsat‐8 data, digital elevation model attributes and climatic variables were used for modelling. For model building, 75% of data was used and 25% of data was used for validation. Overall classification accuracy index and kappa index were calculated for validation data sets using 100 RF models. We recorded overall accuracy of 50%–65% and kappa index of 35%–47% for various depths. We found that equal‐area quadratic splines of average sand, silt and clay are useful for soil profile depth harmonization of soil textural classes and random forest classification algorithm is a promising tool for spatial prediction of texture classes at the regional level. The present high‐resolution (250 m) maps of soil texture classes are useful for different hydrological studies and preparation of proper land‐use plans.