Abstract: Accurate distribution information of crop types is vital for monitoring crop growth, guiding agricultural production, and making effective management measurements. Time series remote sensing data can reflect phenological characteristics of crops, which have more advantages than single temporal data in identifying crop types or planting patterns. MODIS and Landsat data can be fused to obtain time series data with medium spatial resolution and high temporal resolution, which can be used for classifying different crops based on phenology characteristics. In this study, in order to test the accuracy of combining long short-term memory (LSTM) algorithm with time series remote sensing data in crop classification, the Linfen basin was chosen as the study area for obtaining crop distribution map. At first, the Savitzky-Golay filter was used to denoise and reconstruct time series MODIS NDVI data. Then the filtered MODIS NDVI and Landsat NDVI were merged by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to generate time series NDVI with a spatial resolution of 30 m pixel by pixel. Based on field investigation, GlobeLand30 data, Google Earth images and agro-meteorological stations data, we obtained the coordinate information of several sampling sites representing different land cover and crop types. The phenological characteristics of time series NDVI of the pixels covering the sampling sites were analyzed, and the types of randomly selected pixels were determined based on the phenological characteristics for increasing the number of sampling sites. Three methods were used for crop classification in this study: 1) the Landsat NDVI of training samples were used to train the LSTM model, and the trained LSTM model was adopted to determine the crop type pixel by pixel (called the Landsat NDVI+LSTM method); 2) the fused NDVI of training samples were used to train the LSTM model for crop classification (called the fused NDVI+LSTM method); and 3) the fused NDVI of training samples were used to train the neural network (NN) model for crop classification (called the fused NDVI+NN method). In order to compare the accuracies of the three methods, the classification accuracies were evaluated with the validation samples. The evaluation indexes included overall accuracy (OA) and Kappa coefficient. Also, the planting area of winter wheat for each county of the study area was estimated according to the crop classification map, and the relative error (RE) and root mean square error (RMSE) between estimated and statistical wheat areas were calculated for further validating the accuracies of the three methods. Results showed that the Savitzky-Golay filter can remove the influence of factors such as cloud and atmosphere, thus the reconstructed time series MODIS NDVI curves could reflect the phenological characteristics of crops effectively. Positive correlation between the fused NDVI and the Landsat NDVI indicated the fused NDVI can reflect the information of Landsat NDVI effectively. The classification accuracies based on the fused NDVI, either using the fused NDVI+LSTM (OA=90.00%, Kappa=0.88) or fused NDVI+NN (OA=88.10%, Kappa=0.86) methods, were significantly higher than the accuracy of the Landsat NDVI+LSTM method (OA=82.86%, Kappa=0.80). The RE and RMSE of the formers were lower than those of the latter. These results indicated that the fused time series NDVI could highlight the phenological information of different crop types, thus the classification accuracy can be improved significantly. In addition, the classification accuracy of the fused NDVI+LSTM method was slightly higher than that of the fused NDVI+NN method, and the RE and RMSE of the former were lower than those of the latter. These indicated that the classification accuracy of LSTM algorithm was higher than that of the NN algorithm. This study can provide an important reference for accurately extracting distribution information of different crops in the study area.