Deep learning-based framework for spatiotemporal data fusion: an instance of Landsat 8 and Sentinel 2 NDVI

Multi-source, multi-temporal, and multi-resolution satellite images have been increasing over a couple of years. The spatiotemporal data fusion technique could be a promising solution to achieve the advantage of temporal and spatial details of multi-platform remote sensing images. Due to the unavailability of required temporal resolution of normalized difference vegetation index (NDVI) of high-spatial-resolution dataset (e.g., Sentinel 2 A/B), the remote sensing-based crop monitoring is limiting in several parts of the world, primarily in the monsoon Asia and tropical region. We propose a deep learning-based framework to fill the gap of high-resolution NDVI value using corresponding low-spatial-resolution NDVI images. Time-series NDVI images from Sentinel 2 and Landsat 8 are used to implement the proposed framework. We evaluate our method on two study areas with different climatic conditions: subtropical and temperate. The results demonstrate that spatiotemporal data fusion with our proposed framework is very effective to synthesize and generate new datasets which represents the unique features of both input sources, namely, spatial and temporal details of each input image regardless of the climatic and temporal variations.