Using an epitomic representation approach to downscale the goes-16' s land surface temperature product

This work presents an application of the epitome model to downscale low-resolution satellite GOES-16 LST products. The goal of the research work is to generate a high temporal high spatial resolution LST by fusing high temporal resolution GOES-16's 2km LST product with high spatial resolution, 30m, auxiliary image products obtained from Landsat 8 imagery. The approach used seeks to extend an epitomic-based representation approach used for classification label super- resolution to downscaling a continuous variable like LST. Experiments were conducted using GOES-16 and Landsat 8 imagery from El Paso, Texas. The qualitative and quantitative results of the presented experiments paint an overall favorable picture for the proposed approach with some instances where the performance is lower than expected primarily due to the limitation of using sun synchronous Landsat 8 data for algorithm training. Furthermore, land cover and seasonal dependency were studied. The algorithm performed better for grassland & shrubland than for other land covers and performed better for winter and fall than for summer and spring.