Spatiotemporal Fusion With Only Two Remote Sensing Images as Input

Abstract

Spatiotemporal data fusion is an effective way of generating a dense time series with a high spatial resolution. Traditionally, the spatiotemporal fusion models, especially the popular ones such as the spatial and temporal adaptive reflectance fusion model, require at least three images as input, i.e., a coarse-resolution image on the target date and a pair of fine- and coarse-resolution images on the reference date. However, this cannot always be satisfied, as the high-quality coarse-resolution image on the reference date may be unavailable in some application scenarios. This led to efforts to achieve data fusion only using the other two images as input. In this article, we proposed an effective strategy that can be combined with any spatiotemporal fusion model to accomplish the fusion with simplified input. To confirm the validity of the method, we comprehensively compared the fusion performances under the two input modalities. In total, 38 tests were conducted with Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and Sentinel-2 land surface reflectance products. Results suggest that by applying the proposed method, the fusion performance with only two input images is comparable or even superior to that with three input images. This article challenges the stereotype that spatiotemporal data fusion strictly needs at least three input images. The proposed method extends the application scenarios of spatiotemporal fusion, and creates opportunities to fuse sensors with barely overlapping temporal coverages, such as the Landsat 8 Operational Land Imager and the Sentinel-2 MultiSpectral Instrument.