Traditional remote sensing spatio-temporal data fusion algorithms generally use up-sampled low-resolution images (MODIS) to be fused with high-resolution images (Landsat), this makes both images less spatially consistent and many hybrid image elements in low-resolution images, so uncertainty errors propagate into the fusion results. To address this issue, we propose a framework for combining deep learning-based super-resolution techniques with traditional spatio-temporal fusion methods. By reconstructing low-resolution images using super-resolution image reconstruction techniques, we obtain low-resolution images with more spatial details and better spatial consistency with high-resolution images. These reconstructed images are then fused using spatio-temporal fusion methods. In this study, we selected Flexible Spatio-Temporal Data Fusion (FSDAF) and Residual Channel Attention Network (RCAN) to carry out a detailed study to prove the effectiveness of this kind of framework. That is, a new RCAN-FSDAF model is developed. After testing, RCAN-FSDAF has the following advantages::(1) The band reflectance predicted by RCAN-FSDAF is closer to base reflectance than FSDAF, DMNet, and GAN-STFM, as shown by greater correlation and smaller error. (2) RCAN-FSDAF better decomposes image elements among heterogeneous features and more accurately identifies boundaries between different features and changes in land cover type. (3) High spatial and temporal resolution NDVI data obtained by the inversion of the prediction results of RCAN-FSDAF are more accurate. The framework developed in this study can be extended to other spatial and temporal data fusion applications.

In recent years, with the implementation of the South-to-North Water Diversion Project, the land use problem and its ecological effects on the Danjiang River Basin (DRB), which is a water source in the project, have become some of the focal points of current research in ecology and environmental science. Selecting the DRB (Henan section) as the study area, an ecological vulnerability evaluation model based on the fuzzy comprehensive evaluation/catastrophe progression method was constructed to evaluate the ecological vulnerability of the study area. The spatiotemporal evolution patterns of ecological vulnerability in the study area were quantitatively analyzed, and the main evolutionary drivers were identified by using GeoDetector. The results showed that: (1) the ecological vulnerability of the DRB (Henan section) was mainly moderate and mild, with areas of 2535.26 km² and 2717.33 km², respectively, by 2020, accounting for 30.14% and 32.30%, respectively, of the total area of the basin, with an overall vulnerability distribution characteristic of “low in the north and high in the south”; (2) the ecological vulnerability indices of the DRB (Henan section) in 2000, 2010, and 2020 were 0.56, 0.61, and 0.58, indicating that the ecological quality first decreased and then increased; and (3) the influence of vegetation factors on ecological vulnerability was large, with explanatory power above 4%. The influence of economic pressures and surface factors on ecological vulnerability gradually increased. This study can provide a reference for ecological environmental protection in the water source of the middle route of the South-to-North Water Diversion Project.