Xinjiang’s traditional cotton field spider mite monitoring method is time-consuming and inefficient. The paper proposes combining ground hyperspectral, UAV multi-spectrum, environmental data and field survey for dynamic monitoring of large-scale spider mite damage. Firstly, cotton canopy hyperspectral data and low-altitude-scale UAV multi-spectral data in different cotton periods are collected separately by analyzing the original hyperspectral spectrum and first-order differential spectral characteristics, four sensitive bands of spider mite damage are extracted as below: green light band near 553 nm, red light band near 680 nm, red side band of 680~750 nm, and near infrared band of 760~1 350 nm, which are also included in the multi-spectrum carried by UAV. Secondly, the correlation analysis among 23 vegetation indices, 13 field environmental data, and the occurrence of spider mites surveyed on the ground is done. SAVI, OSAVI, TVI, NDGI, average humidity, temperature-humidity coefficient and average soil temperature of 10 cm are all significantly correlated with spider mite occurrence (sig≤0.01); RDVI, RVI, MSR, maximum temperature, average temperature, accumulated temperature, the highest temperature of 10 cm soil and the average humidity of 10 cm soil all reach a significant correlation level with the occurrence of spider mite damage (sig≤0.05). 15 characteristic values with sig values below 0.05 were selected; cotton field mite monitoring models based on single environmental data, single vegetation indices, and a combination of environmental data and vegetation indices are established respectively using support vector machine (SVM). Finally, from the optimum model, we can draw the spatial distribution map of spider mite damage in different periods and calculate the proportion of spider mite damage are based on the number of spider mite damage and healthy pixels in the statistical distribution map. Then the field environmental data is analyzed for correlation, the environmental factors most closely related to the spider mite area value are determined by multiple stepwise regression analysis, and the cotton field spider mite area prediction model is established. The results show that the accuracy rate of the cotton field spider mite monitoring model based on a single environmental data is 62.22%, while the accuracy rate of the cotton field mite monitoring model based on a single vegetation index is 75.56%. Moreover, the most effective model is based on the combination of environmental data and vegetation indices with an accuracy rate of 80%. The coefficient of determination of the spider mite area prediction model is R²=0.848. In this study, based on multi-source data, the cotton field spider mite occurrence monitoring model and spider mite area prediction model can provide a reference for the large-scale monitoring and trend warning of cotton field mite damage in Xinjiang.