Abstract: Abstract: The potential of visible-near infrared (vis-NIR, 350~2500nm) laboratory spectroscopy for the estimation of soil properties has been previously demonstrated in the literature. Spectroscopy is rapid, inexpensive, and non-destructive. A single spectrum allows for the simultaneous characterization of various soil properties. The question that always arises when two samples are close in spectral space is whether they are close in terms of soil composition. This paper explores three different approaches to improving prediction accuracy. The first, called the SAM Approach, predicts soil properties via similar soil spectra using a spectral angle mapper (SAM). The second one, called the PLSR Approach, predicts soil properties using partial least-squares regression (PLSR). The third, called the SAM-PLSR Approach, first uses the SAM to choose similar soil spectra, which are then used as calibration samples for the PLSR. These tests were performed on a collection of 400 soil samples from 91 profiles from the Xuancheng region of the Anhui Province. Spectra data include reflectance (R), first derivatives of reflectance (FDR), and the logarithm of the inverse of the reflectance (Log(1/R)). The aims of the work were threefold: (1) to investigate the relationship between soil vis-NIR similarity and soil attribute similarity (soil organic matter (SOM) and cation exchange capacity (CEC)) using a spectral angle mapper (SAM); (2) to predict soil properties by PLSR with different calibration samples, which were independently validated; (3) to compare the accuracy of predictions from the SAM Approach, PLSR Approach, and SAM-PLSR Approach. This study showed that soil vis-NIR similarity reflected the similarity of SOM and CEC content, the SAM Approach can be directly used to predict the content of SOM (R2=0.78, RPD=2.17) and CEC (R2=0.82, RPD=2.41). The PLSR Approach obtained good prediction accuracy of SOM (R2=0.87, RPD=2.77) and CEC (R2=0.87, RPD=2.59). The SAM-PLSR Approach, which was calibrated with FDR, produced more accurate predictions for SOM (R2=0.89, RPD=3.0) and CEC (R2=0.91, RPD=3.06) than the other approaches, and this method can greatly reduce the number of calibration samples. This work demonstrated the potential of diffuse reflectance spectroscopy using the vis-NIR with SAM-PLSR Approach for more efficient soil analysis.