To distinguish and precisely extract the information of different types of facility agriculture, this study takes the solar greenhouses and polyhouses in Weifang as the research objects using the Google Earth Engine (GEE) platform. By integrating Sentinel-1 radar data, Sentinel-2 optical data and SRTM DEM data, 41 feature variables are extracted, including the features of spectrum, index, radar, texture and topography, and the optimal selection of features is conducted. By comparing 14 window scales with the cumulative difference method, the optimal texture features for Sentinel-2 imagery are identified. The experiments with 6 combinations of features are designed using the random forest classification algorithm to generate a distribution map of facility agriculture in Weifang with the resolution of 10 m in 2023, and the impacts of different combinations on the accuracy of extraction are explored. The results are outlined below. (1) The optimal window scale of the texture feature for extracting facility agriculture using Sentinel-2 is 27×27, and the best combination of texture features includes Mean, Entropy, Variance, Dissimilarity and Contrast. (2) Besides the features of spectrum and index, the features of texture, radar and topography are used to classify facility agriculture. The order of different features beneficial to extract facility agriculture from high to low is texture, topography and radar, and compared to the classification using only spectrum and index, the accuracy increases by 4.08%, 1.40% and 0.80%, respectively. (3) The optimal selection of features yields the highest classification accuracy, with an overall accuracy of 91.03% and a Kappa coefficient of 0.86. The producer’s accuracy and user’s accuracy for polyhouses are 92.21% and 91.83%, respectively, while those for solar greenhouses are 88.54% and 89.47%, respectively. (4) The remote sensing extraction method for various facility agriculture types in Weifang using Sentinel imagery and SRTM DEM topography data offers decision-making support for disaster risk management of facility agriculture.