Abstract: An agricultural plastic covering classification algorithm was proposed based on multi-kernel active learning to achieve accurate classification of agricultural greenhouses and mulch film by introducing multi-source and multi-temporal satellite remote sensing data, and their spectral features and texture features were firstly extracted to construct a multi-dimensional feature space based on the multi-temporal Sentinel-1 radar and Sentinel-2 optical remote sensing data. And then, a multi-kernel learning model was constructed to realize the adaptive fusion of multi-source and multi-temporal features. Finally, a pool-based active learning strategy is constructed to further improve the generalization ability of the classification model by introducing an elimination mechanism for training samples. The test results showed that the overall accuracy of the proposed classification method was 95.6%, the Kappa coefficient was 0.922. Compared with that of the classic SVM, random forest, KNN, decision tree, AdaBoost model, the accuracy of the active learning model was improved by 5.7, 12.1, 11.4, 22.3 and 10.3 percentage points. And under the same classification accuracy, active learning can reduce more than half of the label data than passive learning. The accuracy was improved by 3.7 and 12.7 percentage points, respectively, compared with using only single-phase and single-sensor remote sensing images. The research results showed that multi-kernel active learning can effectively perform multi-sensor and multi-temporal data fusion, and can achieve high classification accuracy under small sample conditions. It can provide model reference for remote sensing monitoring of agricultural plastic cover.