A Novel Multi-platform Spatiotempoal Data Fusion Approach for Remote Sensing Imagery Based on Parameter Selection

Spatiotemporal fusion is an important means to reconstruct the medium spatial resolution remote sensing image series. Presently, many spatiotemporal fusion approaches have been developed and adopted in researches on agriculture, ecology, environment, and so on. Although these approaches have achieved remarkable performance in experiments and applications, most of them are designed to fuse all involved bands using the same model with the same parameters, which ignores the band difference. The ignorance may limit the fusion quality for some bands. To address this problem, we propose a novel spatiotemporal data fusion approach based on parameter selection (PSDFA) in this paper. The core idea of the newly proposed PSDFA is producing the synthetic image pairs using available data via three means firstly, then selecting the similar image pair for each band to provide the parameters that are needed for their fusion. The PSDFA can not only be applied in local computers, its simplified version can also be implemented in Google Earth Engine (GEE), which is a powerful and widely used cloud platform for remote sensing data computing. To test the PSDFA, we conduct two experiments, one in local computers and another in GEE. In local computers, the PSDFA is compared with five state-of-the-art fusion methods on two public Landsat-MODIS datasets. In GEE, it is used to produce the monthly 30m image series in two study sites in the USA and compared with another GEE-based fusion approach. The experimental results demonstrate the outstanding performance of the proposed PSDFA in both local computers and GEE.