Land cover maps are significant in the agricultural analysis. However, the existing workflow of producing maps takes too long. This work builds long short-term memory (LSTM) recurrent neural network (RNN) model to improve the update frequency. An end-to-end framework is proposed to train the model. Landsat scenes are used as Earth observations. Field-measured data and CDL (Cropland Data Layer) are used as ground truth. The network is deeply trained using state-of-the-art techniques. Finally, we tested the network on multiple Landsat images to produce five-class land cover maps with timestamps. The results are visualized and compared with CDL and ground truth. The experiment shows a satisfactory overall accuracy (>97%) and proves the feasibility of the model. This study paves a path to efficiently using LSTM RNN in remote sensing image classification.