Soil moisture content (SMC) is one of the most important indicators influencing the exchange of energy and water among vegetation, soil, and the atmosphere. Accurate detection of soil moisture content is beneficial to improving the precision of crop yield evaluating and field management measures. In this paper, a novel method ADI (Angle Dryness Index) based on NIR-RED spectral feature space used for calculating SMC was proposed, which improved the accuracy of calculating SMC with red and near infrared band reflectance. It was found that an intermediate parameter θ in NIR-RED feature space was significantly related to SMC, and independent of vegetation coverage according to the linear decomposition of mixed pixel and the empirical correlation between SMC and red/NIR band reflectance which were achieved by previous researches. Then, ADI was proposed with the feature discovered in the paper. The mathematical expression on SMC is nonlinear, and the newton iterative method is applied to ADI for calculation SMC. Then, the newly proposed method was validated with two kinds of remote sensing imagery data (Thematic Mapper (TM) and moderate resolution imaging spectrometer (MODIS)) and the synchronous observed data in the field. Validation results revealed that the ADI-derived SMC was highly accordant with the in-situ results with high correlation (R2=0.74 with TM and R2=0.64 with MODIS data). We also calculated MPDI (Modified Perpendicular Drought Index) developed by Ghulam, which is also proposed with the red and near infrared reflectance. The result showed that the accuracy of MPDI was lower than that of ADI. The most likely reason was that ADI was insensitive to fv, but the calculation errors of fv would reduce the accuracy of SMC estimation. MODIS had a low spatial resolution, thus there may be more than two end members in a mixed pixel. In this case, the linear decomposition of mixed pixel was not applicable and the errors would finally be enlarged. ADI achieved good results in monitoring SMC in vegetated area because it was less influenced by vegetation coverage than other similar approaches. ADI only requires the satellite image data including the red and near infrared band which are available from most of the optical sensors. Therefore, it is an effective and promising method for monitoring SMC in vegetated area, and would be widely used in agriculture, meteorology, and hydrology.