Spatial Modeling for Soil Properties Prediction in Mountainous Areas using Partial Least Squares Regression (original) (raw)

Soil properties are one of the most important categories of information for land management and environmental modeling. Unfortunately, soil properties in mountainous areas with slopes of more than 35% are rarely investigated in Thailand due to the complexity of their landscapes and the cost and time requirements. The main objective was to predict soil properties in mountainous areas relating to soil forming factors using partial least squares regression (PLSR). The combination of topographic position index values from two different scales and criteria sets was fi rstly used to classify landform for in situ soil survey. Then, analyzed soil properties of the topsoil and subsoil (sand, silt, clay, pH, organic matter, total N, available P, exchangeable K, cation exchange capacity (CEC) and base saturation) and soil forming factors (rainfall, normalized difference vegetation index, elevation, slope, aspect, plan curvature, profi le curvature, curvature, topographic wetness index and Al/S...