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• The MLR model was effectively applied to predict soil fertility with high accuracy. • Soil fertility was extracted from major soil chemical properties by PCA. • BI, CI, NDWI, and MSI indices are important predictors to express soil fertility. • Half of the TKR region was occupied by "very low" to "low" soil fertility. • The soil fertility map indicates well the potential of rice productivity. Digital soil assessment (DSA) is an application for the interpretation of digital soil mapping (DSM) outputs for evaluating soil and related environmental factors. However, DSA has not been widely applied to assess soil fertility in the humid tropics due to the complexity of integrating numerous soil chemical properties into one map. In this study, principal component analysis was applied to extract the most appropriate soil indicator for crop production. A digital map of soil fertility for jasmine rice was created using a multiple linear regression (MLR) model. Finally, the digital map was confirmed by comparing it with actual rice yield in the Thung Kula Ronghai (TKR) region of Thailand. The calculated soil fertility was significantly expressed by soil pH, electrical conductivity, organic matter, and contents of macronutrients (P, K, Ca, and Mg). The obtained MLR model for this region's soil fertility consisted of spectral indices (brightness, coloration, normalized difference water, and moisture stress) and topographic indices (slope and topographic wetness). The digital map indicated that more than half of the TKR region contained "very low" to "low" soil fertility. The soil fertility map fits well to actual rice production in the TKR region. [ABSTRACT FROM AUTHOR]

• DSM techniques produced digital soil maps of soil properties in the TKR region. • The MLR model well identified the significant predictor variables. • BI, SI, CI, NDWI, and MSI were significant variables for predicting soil property. • The DSM showed inter-relationships among pH, macro and micronutrients. 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. [ABSTRACT FROM AUTHOR]