Abstract: As one of the economic forest species in Jiangxi Province, Camellia oleifera is also a characteristic advantageous industry in Jiangxi Province, and it is of great significance to accurately obtain its spatial distribution in terms of yield estimation, production management and policy formulation. In response to the lack of optical images due to the cloudy and rainy climate in the south, as well as the problem of fragmented terrain in hilly and mountainous areas, Yuanzhou District, Yichun City, Jiangxi Province, was taken as the study area. Using time-series Sentinel satellite imagery and SRTM DEM data as data sources, a total of 125 feature variables were constructed and selected, including spectral features, vegetation-water indices, red edge indices, radar features, terrain features and texture features. Among them, the texture features were calculated by comparing 15 different scale windows by using the cumulative difference method to calculate the best texture features for Sentinel-1 and Sentinel-2 images. Based on ReliefF feature preference algorithm and random forest classification algorithm, eight feature combination schemes were designed to carry out experiments to explore the impact of different feature types on the extraction accuracy of Camellia oleifera. The results showed that the optimal texture feature window for both Sentinel-1 and Sentinel-2 calculated experimentally by using the cumulative difference method was 35×35, and the optimal texture feature combinations were mean, variance and contrast. Building upon spectral features and vegetation-water indices, the incorporation of different features for Camellia oleifera classification demonstrated varying degrees of effectiveness. The favorability ranking of different feature types for Camellia oleifera extraction from large to small was as follows: S2 texture features, S1 texture features, terrain features, radar features and red edge index. Compared with single-spectrum and index features, the inclusion of texture features significantly enhanced classification accuracy. The synergistic classification results of multiple features surpass those of single-feature classification, with the highest precision achieved through Camellia oleifera extraction based on feature selection. The ReliefF algorithm feature optimized scheme had the highest accuracy with overall accuracy of 88.29% and Kappa coefficient of 0.81. This study utilized time-series Sentinel satellite imagery and DEM terrain data to develop a large-scale remote sensing extraction method for Camellia oleifera in the cloudy and rainy southern hilly mountainous region. This method can serve as a reference for the investigation and monitoring of Camellia oleifera resources in the hilly areas of southern China.