Abstract: Abstract: Dynamic monitoring of soil moisture by remote sensing can play a significant role in agricultural production, weather research, and ecological environment evaluation. In recent years, continuous attention has been focused on the thought that soil moisture information can be extracted from multi-dimensional spectral feature space. A variety of soil moisture monitoring indices have been put forward in succession based on the spectral feature space, and some of them have gotten widespread attention and application such as PDI (perpendicular drought index), an simple, effective, and feasible index which has been developed based on red and near infrared spectral feature space and applied in many areas. However, the majority of these indices are devised without considering the influence of mixed pixels, which makes the effect of soil moisture estimation worse in vegetation-covered areas than in bare areas. Aiming at the problem of accuracy degradation for PDI monitoring in vegetation-covered areas, this study thoroughly analyzed the distribution of vegetation-soil pixels in Nir-Red spectral feature space, and figured out the distribution characteristics of PDI monitoring error under vegetation coverage. By taking PVI (perpendicular vegetable index) as vegetation coverage characterization, we tried to improve the PDI model in the PVI-PDI two-dimensional feature space and developed a new index (vegetation adjusted perpendicular drought index, VAPDI) theoretically suitable for vegetation coverage. Then, we used the measured soil moisture data in the study area in the Mingan Town, Bayinnaoer city of the Inner Mongolia autonomous region, China to compare and validate the PDI and VAPDI. Results showed that the coefficient of determination between the PDI and measured soil moisture were 0.630, 0.504, 0.571 and 0.543 respectively in 4 vegetation cover types (bare soil, wheat stubble, potato, and cowpea), and the coefficient of determination between the VAPDI and measured data were 0.599, 0.523, 0.602, and 0.585, respectively. The accuracy of the VAPDI was higher than the PDI in vegetation areas, and the advantage of VAPDI on monitoring accuracy increased gradually with the increase of vegetation coverage degree. Therefore, the overall effect of the VAPDI was better than that of the PDI in farmland vegetation areas. The comparison of space distribution of the PDI and VAPDI also indicated that the VAPDI does a better job in distinguishing the differences of soil moisture. The VAPDI index with clear physical meaning is simple and practical, which implies a good application prospect in the aspect of the mesoscale soil moisture inversion by remote sensing. The research can provide valuable information for method selection and error analysis in farmland soil moisture monitoring by remote sensing.