Abstract: In order to explore the ability of field vis-NIR spectroscopy for estimating soil fertility index, 220 soil samples in the depth profile of 20 cm were collected in 2015, 2016 and 2017 in the Huangshui river basin, respectively. Soil reflectance spectroscopy of 350-2500 nm were synchronously measured in field by using ASD Field Spectrometer. Soil nutrients, pH and mechanical composition were analyzed in laboratory. The pretreatments of multiplicative scatter correction (MSC) and SG-1st derivative transform (SG-1st) were used to obtain the spectral reflectance curve, and then stability competitive adaptive reweighted sampling (SCARS) was used to select characteristic wave bands of soil properties. The prediction models for the contents of soil total carbon (TC), organic matter (OM), total nitrogen (TN), alkeline nitrogen (AN), pH, clay, silt and sand were constructed by partial least squares regression(PLSR), then selecting the optimal model for every soil property by comparison analysis. The results showed that SG-1st transformation and MSC correction could effectively enhance the field spectral characteristics of soil nutrient, pH and soil mechanical composition. The characteristic bands selected by SCARS were mainly focused on the near infrared wave bands. PLSR model based on field vis-NIR spectroscopy could roughly estimate the contents of soil TC, OM, TN and AN as well as pH values. For the properties of soil TC, OM, TN and pH, the best estimation model was SCARS-PLSR by SG-1^st processed with the RPD values over 1.70 (RPD_TC = 1.76; RPD_OM = 1.82; RPD_TN = 2.04; RPD_pH = 1.89), the RPIQ values over 1.90 (RPIQ_TC = 1.91; RPIQ_OM = 2.53; RPIQ_TN = 2.98; RPIQ_pH = 2.03). For soil AN contents, the best estimation model was SCARS-PLSR by MSC processed with the RPD values up to 1.91 and the RPIQ values up to 2.39. The contents of soil clay, silt and sand could not be estimated with all the RPD values around 1.00 and the RPIQ values around 1.20.