Prediction of spatial distribution of soil nutrients using terrain attributes and remote sensing data

The distribution of the soil organic matter and total nitrogen can provide reliable and useful information for sustainable land management and land use planning. In this study, regression Kriging with environmental predictors was used to predict the spatial distribution of soil nutrients (organic matter and total nitrogen) in Nong’an County, Jilin Province, Northeast China, considering the disadvantages of conventional geo-statistic methods. Terrain and vegetation indicators were chosen for regression Kriging including ten terrain attributes and one vegetation index. The results indicated that relative elevation (Hr), gradient (β), roughness of terrain (QFD), rate of gradient (SOS) and NDVI had significant correlations with soil organic matter and total nitrogen. M and Ψ had higher significant correlation with soil organic matter than those with total nitrogen. Relative elevation (Hr), gradient (β), surface roughness (M), river dynamic index (Ω) and NDVI were the best predictors for describing soil nutrients in the study area for they described the regression equations most. In Nong’an County, the soil organic matter and total nitrogen distributed regularly from southeast to northwest, and the values were higher in the part of southeast. This distribution pattern was affected by terrain and vegetation factors synthetically, and it had a significant relationship with soil type. Precision assessment results showed that regression Kriging improved the accuracy significantly and it could be an effective method for evaluating the spatial distribution of soil properties.