Traditional methods for acquiring soil moisture can only provide discrete point data, which arenot so appropriate for finely and continuously mapping soil moisture distribution in soil profile. In this paper, the feasibility of predicting and mapping ofsoil moisture content (SMC) in soil profile was studied using near-infrared hyperspectral imaging in the spectral range of 882～1 709 nm. Two soil profiles, located in Dongtai City of Jiangsu Province, were continuously observed in situ for 5 days by the near-infrared hyperspectral imaging system. A total of 280 soil samples were obtained for later SMC measurement by oven-drying method. After a series of preproces on the acquired raw hyperspectral images, including digital number(DN) correction, reflectance correction, mosaicking, geometric correction, image clipping and masking, the average spectral reflectance of each sampling point in the corrected hyperspectral images was extracted for further analysis. Then the extracted spectra (Raw) were preprocessed by LOG10(1/R), Savitzky-Golay (SG), first derivative (FD), second derivative (SD), multiplicative scatter correction (MSC) and standard normal variate (SNV), and partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models were developed and comparedfor a selection of optimum prediction model. Results showed that the soil spectral reflectance gradually decreased with the increase of SMC, and different spectral preprocessing methods had different prediction accuracy. Except for the MSC preprocessing method, the prediction accuracy of the LS-SVM model was higher than the PLSR model with the same spectral preprocessing method. The prediction accuracy of the LS-SVM model with LOG10(1/R) preprocessed spectra was highest with R2c of 096 and RMSEc of 065% for calibration, and R2p of 088, RMSEp of 105% and RPDp of 288 for prediction. The optimum model was then applied to produce high spatial resolution maps of SMC in profiles. The prediction accuracy was high (R2: 085～095, RMSE: 094%～102%) by comparing the extracted SMC values from prediction maps with the measured values, and both SMC had the samedistribution tendency in profiles, demonstrating that the SMC prediction mapscould well displaynot only the SMC distribution in profiles in the millimeter scale, but also the changes of SMC at different locations in the profile between different days. Thus, the near-infrared hyperspectral imaging technology combined with optimized prediction model could provide a new approach to quantitatively predict and map high spatial resolution images of SMC in soil profiles in situ, which could help to rapidlyand effectively monitorsoil moisture in profiles in the field.