Research on spatio-temporal fusion algorithm of remote sensing image based on GF-1 WFV and Sentinel-2 satellite data

The remote sensing data set with high spatial and temporal resolution is of great significance for monitoring surface change. However, the earth observation satellites at domestic and international cannot obtain high spatial resolution and high temporal resolution images at the same time. Remote sensing data spatio-temporal fusion technology is an effective means to solve this problem. In this paper, the multi-period GaoFen-1 WideField-View (GF-1 WFV) satellite images and Sentinel-2 satellite images of Beijing-Tianjin-Hebei region in 2020 are used to perform fusion simulation for the three spatio-temporal fusion algorithms of Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced-STARFM (ESTARFM) and Enhanced Flexible Spatiotemporal Data Fusion Model (EFSDAF) and these simulation degree of the results are quantitatively evaluated. The results show that ESTARFM algorithm is more suitable for the construction of high spatio-temporal fusion data of GF-1 WFV and Sentinel-2 satellites in Beijing-Tianjin-Hebei region.