Soil moisture content (SMC) is a key factor in biogeochemical and atmospheric coupling processes. It plays an important role in areas such as agriculture, ecology and environment in arid region. Compared to the spaceborne remote sensing system, UAV platform with hyperspectral sensors possess higher spatial resolution and maneuverability. With UAV (Unmanned Aerial Vehicle) being increasingly popular, it offers brand new platform of remote sensing. This platform realizes the goal that quickly and quantificationally monitor object in the area. Moreover, hyperspectral sensors contribute to remote sensing when they enrich high dimensional and nanoscale data source. However, there still lacks a standardized research scheme for estimation of UAV by hyperspectral Remote Sensing. In this study, we obtained UAV hyperspectral image from a typically dry-farming region lying in Xinjiang Uygur Autonomous Region. Hyperspectral image was pretreated using six methods of pretreatment, including first-derivative (FDR), second-derivative (SDR), continuum removal (CR), absorbance (A), first-derivative absorbance (FDA) and second-derivative absorbance (SDA). From pretreatment foundation, four types spectral indices were proposed containing the Difference Index (DI), the Ratio Index (RI), the Normalization Index (NDI) and the Perpendicular Index (PI). And the rationality of the spectral index was discussed from the spectral mechanism. Considering the superiority of ensemble learning algorithm rising in recent years, the SMC estimation model was constructed via Gradient Boosted Regression Tree (GBRT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). In these models, 28 appropriate spectral indices were used as independent variables and 70 SMC measured values as response variables. Spectral indices were ranked via importance based on ensemble learning model analyzed and compared to make a more comprehensive evaluation. The result indicated that: (1) atmospheric disturbance and soil background were eliminated effectively throughvarious pretreatment schemes and spectral indices. Pretreatment scheme A highlighted more spectral information and PI correlation was significant. (2) Optimum spectral index was A_PI (|r|=0.773) that the ranking of importance ranks first, and the correlation coefficient |r| is the highest, and it had excellent performance in both linear and nonlinear relationships. (2) XGBoost prediction model was outstanding in three ensemble learning models, and it yielded the highest R2val, the lowest RMSP and the best RPD (R2val=0.926, RMSEP=1.943 and RPD=2.556). The ranking of the predictive performance was XGBoost>RF>GBRT. This proved that this scheme was effective in digital mapping in arid region. In conclusion, there is potential high accuracy for UAV imagery based on hyperspectral imagery. This study afforded an effective method for predicting SMC in arid regions, and it provided a new perspective for quickly and easily monitoring object attributes and it proposed an alternative solution for predicting soil moisture. Ultimately, our program is supporting better management and conservation strategies for precision agriculture and ecosystems in arid regions.