Abstract: In order to invert the surface soil moisture in the underground mining disturbance area, taking the 52501 working face of Daliuta Coal Mine as an example. An unmanned aerial vehicle (UAV) equipped with an imaging spectrometer was used to acquire hyperspectral images. The obtained spectral data underwent logarithmic, reciprocal logarithmic, first-order, and envelope removal transformations. Combining these data with 128 ground-collected soil moisture samples, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were constructed to predict soil moisture content and validate their predictive accuracy. The results indicate that the PLSR model and LSSVM model based on the first-order transformation exhibit relatively good predictive accuracy. For the first-order transformation PLSR model, the coefficient of determination for the modeling set R_c² is 0.702 1, and for the prediction set R_p² is 0.640 5, with root mean square errors of calibration (RMSE_c) and prediction (RMSE_p) being 1.638 4% and 1.103 4%, respectively, and relative prediction error (RPD_p) of 1.726 3.For the first-order transformation LSSVM model, the modeling set R_c² is 0.812 5, and the prediction set R_p² is 0.597 9, with RMSE_c and RMSE_p being 1.275 5% and 1.345 9%, respectively, and RPD_p of 1.632 3. Ultimately, based on the PLSR and LSSVM models, soil moisture mapping was completed, achieving spatial prediction of soil moisture. This provides spatial data support for the precise enhancement of soil moisture in vegetation-guided restoration efforts in the study area.