We have recently developed an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. During this process, we have also designed a new approach to systematically assess the effects of varying levels of phenological changes on the fusion performance. The paper can be found in the following link: https://doi.org/10.3390/rs13245005
