UAV-based remote sensing technique provides a new perspective and platform for precision agriculture and agricultural information monitoring. The hyperspectral sensor has centimeter-level spatial and fine spectral resolution, allowing for the acquisition of high-quality hyperspectral data. However, hyperspectral data often bring question on noise, data redundancy and inefficient use of hyperspectral information, whereas conventional preprocessing is difficult to estimate withhigh-precision. Therefore, data mining for UAV-based hyperspectral images is essential to solve the above problems. Here we used fractional order differential (FOD) to process UAV-based hyperspectral data (with a step length of 0.1). The optimal FOD order is explored at the spectral level by comparing the ability of the FOD technique with the integer order technique to improve the hyperspectral data. Soil moisture content (SMC) estimation models were constructed under the Gradient-Boosted Regression Tree (GBRT) algorithm, and the spatial distribution of SMC was finally evaluated under the best model. The results found that the correlation between the spectrum and SMC alsowas increased (absolute maximum correlation coefficient, r_max=0.768). Compared with the original image and processed images via first and second order derivatives, r_max increased by 0.168, 0.157 and 0.158, respectively. The main reason for the FOD technique to enhance the accuracy of model estimation is to highlight the role of effective spectral information, especially chlorophyll, plant structure and water response bands closely sensitive to drought stress. (430, 460, 640, 660 and 970 nm). By comparison, the low-order FOD (order<1) is more effective in the image quality, correlation and model accuracy than high-order FOD (order>1). The higher order FOD adds a certain amount of noise to the image, though the FOD technology achieves the desired result. Estimated model achieved the best results in the 0.4-order model (R_p²=0.874, RMSEP=1.458, RPIQ=3.029). In addition, the SMC estimation models of 0.1—0.9 order and 1.6—1.9 order outperformed the integer-order models (R_p² improvement of 0.8%~13.8%), but the lower-order FOD models were found to be stronger in terms of model predictive power based on the RPIQ of the models. The spatial distribution of inverse farmland soil moisture under the 0.4 order model indicated significant spatial heterogeneity of farmland SMC in the arid regions. In conclusion, the low-order FOD technique effectively enables the mining of hyperspectral data to accurately estimate agricultural SMC. This study proposes a new approach to airborne hyperspectral image processing that provides a new strategy for precision agriculture implementation and management in arid regions.