LAUSR

Abstract Prediction of soil chemical properties has significant implications for land management in Thailand but it is especially challenging in vast areas with limited soil data. In this study we identified important spectral and terrain indices for predicting various soil properties, evaluated the suitability of the digital soil mapping (DSM) technique for creating digital soil maps, and assessed the soil nutrients levels in the Thung Kula Ronghai (TKR) region of Thailand. A total of 186 soil samples were collected at 0–30 cm depth and analyzed for nutrients. A digital elevation model with 5 m resolution was used to derive the terrain variables of the study area. Landsat-8 images collected at bare soil conditions with 30 m resolution were used to determine the soil and vegetation indices. Models developed to predict soil properties using multiple linear regression (MLR) were evaluated in terms of the coefficient of determination, root mean square error and normalized root mean square error. We found that indices such as brightness, saturation, coloration, normalized difference water, and moisture stress are the most important predictor variables, significantly correlated with various soil properties. The accuracy of the MLR models developed for predicting soil properties in this study suggests that the DSM technique could be useful to predict soil nutrient status in the TKR region.