Soil-environment models can be of great importance to proper understanding of relationships between soil properties and environmental factors, and to predicting and mapping of soil properties, as well. A gully area where the “Grain for Green” policy had been implemented for years was selected in Changwu County, Shaanxi Province, a Loess Plateau region in China. A total of 72 surface soil samples were collected, and 3 fourths of the samples were used as a calibration set of samples and the rest as a validation set. Several easily aquired environmental factors, such as topographic factor, vegetation index and wetness index were used in a PLSR (partial least squares regression)-based soil-environment model established for the study. Quantitative analysis of relationships between environmental factors and soil properties of the samples was done. Results show that soil properties, including available potassium, total potassium, organic matter and total nitrogen, were significantly correlated with environmental factors. The PLSR-based model could well explain 23% to 27% the spatial variability of soil properties. Compared with the stepwise regression model used, the PLSR model was much better at characterizing soil-environment relationships with better fitting and prediction accuracy, suggesting that the PLSR-based model is applicable to prediction of soil properties of similar regions.