The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy (NIRS) as a rapid method for the accurate estimation of nutrient components in agricultural soils. Focusing on the soil of the Sanjiang Plain, NIRS was used to predict soil organic matter (SOM), the total nitrogen (TN) and the total phosphorus (TP). A total of 540 samples were collected from the three different depths (180 samples from each depth: 0-10, 10-20 and 20-30 cm), from 2015 to 2017, from the Sanjiang Plain in Heilongjiang Province, China. From every depth, 120 samples were used to construct the calibration set. Other 60 samples were used to check the efficiency of the model. Combining the first-order differentiation with the partial least square (PLS) method, a prediction model was obtained to measure SOM, TN and TP. The correlation coefficient of SOM from 0 to 10 cm was R2=0.9567, from 10 to 20 cm was R2=0.9416, and from 20 to 30 cm was R2=0.9402. The corresponding ratio (standard deviation [SD]/root mean square error of prediction [RMSEP]) was >2.96. R2 of TN with the three depths was 0.9154, 0.9028 and 0.9024, respectively, all with SD/RMSEP>2.89. Meanwhile, R2 of TP with the three depths was 0.8974, 0.8624 and 0.7804, respectively, all with SD/RMSEP>2.50. These results demonstrated that NIRS based on the first-order differentiation and PLS could efficiently predict SOM, TN and TP from different soil depths.