1. Department of Resources and Environment, Tibet Agricultural and Animal Husbandry College, Linzhi 860114, China
2. Institute of Agricultural Remote Sensing & Information Technology Application, Environmental and Resource Sciences College, Zhejiang University, Hangzhou 310005, China
Abstract：Soil erosion reduces the productivity of the soil, leading to the deterioration of soil environment. Water erosion is one of the most important forms of soil erosion. Soil erodibility K value is an important indicator to evaluate soil susceptibility to erosion, the aim of this paper is to evaluate whether Vis-NIR can be used in predicting K value as a rapid method. Soil samples were sampled from Henan, Fujian and Zhejiang provinces, after air-drying and grinding, visible-near infrared (Vis-NIR) diffuse reflectance spectra were measured. Then, soil reflectance spectra were transformed to absorbance spectra and Savitzky-Golay (SG) algorithm was used to eliminate noise. Data mining methods were used to predict soil organic matter (SOM) and soil texture with Vis-NIR spectra, then K values were estimated with EPIC and RUSLE2 models based on predicted SOM and soil texture. The results were as follows: (1) The prediction models with the highest performance were obtained about the SOM and soil texture (sand, silt and clay), the best model for soil texture prediction gained from support vector machine (SVM) model and the best SOM result was performed using locally weighted regression (LWR) model, of which the ratio of performance to inter-quartile distance (RPIQ) was 2.27, 3.17, 2.18 and 3.44 for sand. Silt, clay and SOM. (2) Based on predicted soil texture, the classification accuracy for grade of soil permeability was good (Kappa coefficient was 0.62), and the spatial distribution between predicted values and measured values was similar in soil texture triangle, of which the main types were silty clay, sandy clay loam, loam, loamy sand and sandy loam. (3) The EPIC and RUSLE2 models both had the accurate prediction ability. EPIC model performed better than RUSLE2 model, of which root mean square error of prediction (RMSEP) was 0.006 6 (t·ha·h)/(ha·MJ·mm) and RPIQ reached 1.58, while the accuracy of RUSLE2 model was lower (RPIQ is 1.43). Therefore EPIC model was recommended to estimate K values in combination with Vis-NIR spectroscopic technique. This study presents the potential for estimating soil erodibility K values using Vis-NIR spectroscopy, which provides supplementary method for monitoring soil erosion in large area.
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