To get the soil moisture of the large scale rapidly and the best monitoring depth in bare soil by UAV multispectral remote sensing technology, the clay loam soil was prepared into two different depths (5cm and 10cm) and the soil moisture ranged from 3% to 30% of the different samples. The UAV was equipped with a Micro-MCA multispectral camera to monitor the soil samples at 3 p.m. for three consecutive days. The soil spectral reflectance values of six bands (490nm, 550nm, 680nm, 720nm, 800nm and 900nm) were collected. The surface moisture content (about 1cm) and overall moisture content of soil samples of two different depths were also measured. The regression models between soil moisture and the reflectance of different bands were established by the regression methods of partial least squares regression, stepwise regression and ridge regression. Quantitative relationship was analyzed of the regression modes and the methods. The results showed that the three regression models had statistical significance (P<0.001) for predicting soil moisture content. The accuracy evaluation of the model through the validation set showed that the stepwise regression model had good prediction ability (R2 were 0.775, 0.764, 0.798 and 0.694, RMSE were 0.028, 0.042, 0.037 and 0.038 and RPD were 2.22, 2.04, 2.20 and 1.75), followed by ridge regression method and partial least squares method. The regression models of the surface soil had good inversion effect in monitoring depth. The inversion effect was decreased as the increase of monitoring depth. The relationship between the soil moisture and the wavelength of 720nm, 680nm and 550nm band was better among the six bands. The results showed that the best regression method was stepwise regression method, and the best monitoring depth was the surface layer (about 1cm) of the soil samples. The research result can provide reference for the rapid monitoring of soil moisture in the area by using multispectral remote sensing of UAVs, and promote the further development of precision agriculture.