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The efficient management of agricultural resources can be significantly improved through the accurate extraction of apple cultivation areas. In order to solve the problems of poor classification accuracy and time lag in apple planting area extraction, a CNN?LSTM temporal classification model was proposed based on Sentinel-2 and MODIS fusion images. The ESTARFM spatio-temporal fusion algorithm was firstly used to construct the fusion image, which complemented the strengths and weaknesses of different satellite images in spatial and temporal monitoring capabilities, and obtained image data with high spatial and temporal resolution. The random forest model was utilized to select the most optimal feature combinations from the initial 25 features, narrowed down to 15 key variables using backward feature elimination. In terms of classification models, convolutional neural networks（CNN）can well extract effective features in the spatial and spectral domains. As an improvement of recurrent neural network, long short-term memory network （LSTM） can handle unequal input sequences. The combination of the two networks proposed can extract effective features in the spatial, temporal and spectral domains to achieve more accurate image classification and remote sensing data analysis. Taking Guanshui Town, Muping District, Yantai City as the study area, the spatio-temporal fusion algorithm was utilized to compensate for the lack of images from a single Sentinel-2, and the CNN?LSTM model was used for apple tree planting area extraction. The CNN?LSTM model achieved an overall accuracy of 97.98% and a Kappa coefficient of 0.9586, outperforming the other four machine learning algorithms by 15.43 percentage points,，5.25 percentage points,，4.00 percentage points, and 3.31 percentage points，respectively. The overall accuracy and Kappa coefficient of the CNN?LSTM model were improved by 2.11 percentage points and 0.0148, respectively, compared with that of the LSTM model. The precise remote sensing extraction method for apple tree planting areas proposed can provide strong support for the development of scientific and rational agricultural management.