The color of a soil may, to a certain extent, reflect degree in development, type and fertility of the soil. Traditionally, soil color is measured with the Munsell colorimetry, which, though quite high in accuracy is time-consuming and low in efficiency. It is, therefore, essential to explore for a quick and accurate method to measure soil colors. Nowadays remote sensing and proximal sensing methods can be used to obtain soil information, and numerous attempts have been made to extract soil color information from soil spectra. For that end, color space conversion (CSC) method is a commonly used one. It uses mathematical formulas to match colors between different coordinates, so as to realize prediction of soil colors. The first step of this method is to extract average reflectance values of the RGB bands from spectral reflectance and then converts them into XYZ values in the CIE XYZ coordinate, and further into HV/C values in the munsell coordinate. In this paper, a novel method was introduced to predict soil colors using partial least squares regression (PLSR) of hyperspectral reflectance of soils, and then comparison was made between PLSR and CSC in prediction accuracy. 【Method】A total of 76 soil samples different in colors were collected in the bordering area of Anhui, Jiangxi and Hubei Province for the study, covering soil types e.g. red soil (Argi-Udic Ferrosols), paddy soil (Fe-accumuli-Stagnic Anthrosols), yellow-brown soil (Ferri-Udic Argosols), fluvo-aquic soil (Ochri-Aquic Cambosols), purple soil (Dystric Purpli-Udic Cambosols) and yellow soil (Ali-Perudic Argosols) in the study area. After being air-dried, the soil samples were determined in color through color matching with the Munsell color system, and their spectral reflectance was acquired simultaneously with the aid of the ASD spectrometer. Then PLSR and CSC was applied separately to predict colors of the soil samples. 【Result】Results show that the PLSR model can be well used to predict soil Hue (H), Value (V), and Chroma (C) with cross validation coefficient R_cv²) being 0.62, 0.61 and 0.75 respectively, and RPD being 1.94, 1.67 and 2.15 respectively, which suggests that it is feasible to use the PLSR method to predict soil colors and that the mean square root error (RMSE) of H, V and C predicted with PLSR was only 1.32, 0.55 and 0.97 units, respectively, and 0.94, 1.24 and 0.95 lower than their respective ones predicted with the CSC method. The former, being 1.91, was 5.16 lower than the latter in mean ∆E, the mean HV/C comprehensive index. Analysis of reasons for that reveals that PLSR uses the spectral reflectance information of all the bands, while CSC makes use of mean reflectance of Red, Green and Blue bands only. Furthermore, certain errors inevitably occur in every step of the conversion of CSC.【Conclusion】Therefore, it could be concluded that the PLSR method is superior to the CSC method in predicting Munsell color of a soil. And compared the conventional soil color measuring methods, this one saves time and labor by a large mirgin. So this method opens up a new way for quick soil color acquisition via soil spectrum.