The tailings dams, as a high-potential manufactured debris flow danger source, has the risk of dam failure when the moisture content is too high, and the generation of dust in the state of low moisture content will endanger the surrounding environment. Monitoring tailings’ moisture content is of great significance to the safety of tailings dams and the environmental protection of mining areas. Compared with traditional sampling and testing methods, hyperspectral remote sensing has the characteristics of a large observation area, easy acquisition of real-time data, and rich spectral information, which provides a means for rapid and high-precision monitoring of tailings moisture. The high-silicon type iron tailing in the Anshan-Benxi iron ore group was selected as the research area, and 77 tailings samples were collected on the spot.The spectral data was obtained by Vis-NIR (350~2 500 nm) spectrometer, and the competitive adaptive reweighted resampling method (CARS) was introduced to screen out the optimal bands and establish three-band spectral indices (TBI), combining random forest (RF), particle swarm optimization extreme learning machine algorithm (PSO-ELM) and convolutional neural network (CNN) model, a tailings moisture inversion model was established to obtain the spatiotemporal distribution characteristics of surface moisture in the tailings dam. Using the domestic Gaofen-5 hyperspectral satellite as the data source, the model was applied to obtain the temporal and spatial distribution characteristics of the surface moisturecontent in the tailings dam. The results showed that: (1) The spectral reflectance of tailings decreased significantly with the increase of moisture content, the spectra characteristic appeared in the O—H absorption bands at 1 455 and 1 930 nm, and the absorption depth gradually decreased with the decrease of moisture content; (2) Based on the CARS method, 18 moisture sensitive bands were screened out, and further use the sensitive bands to construct different forms of three-dimensional tailings moisture content characteristic spectral indices. It is proposed that TBI5=(R1 097.47-R1 990.67)-(R1 990.67-R437.39), which has the highest correlation with moisture content, reaching 0.844 4; (3) Based on the three-dimensional spectral indices combined reflectance data set and the CNN method, the measured spectral model achieves the verification accuracy R2=0.92, the residual predictive deviation (RPD) =3.43. Based on this model, the spatial distribution results of tailings moisture content in the study area were obtained by inversion using the Gaofen-5 satellite data. The moisture content field verification model prediction result in R2 reached 0.79.The result is relatively effective. This study can provide a reference for large-scale real-time, and rapid monitoring of iron tailings moisture content based on hyperspectral technology.