Developing and evaluating the feasibility of a new spatiotemporal fusion framework to improve remote sensing reflectance and dynamic LAI monitoring

Multi-sensor fusion provides an effective way for applications requiring remote sensing data with high spatiotemporal resolution. Especially for agricultural areas with complex planting structures and rapid changes in crop phenology, more detailed and dense time-series remote sensing data are necessary. The Sentinel-2 Multispectral Imager (S2-MSI) sensor with high spatial resolution (10–60 m) and temporal resolution (5–10 days) plays a key role in spatiotemporal fusion. But the inconsistent spatial resolution of the various bands hinders its potential application at 10 m resolution, and the multiple available fine images it provides are not fully utilized for spatiotemporal fusion. It is worth exploring how to maximize the spatial and temporal resolution of S2-MSI images to help improve the effect of spatiotemporal fusion and the dynamic monitoring of rapid crop growth. In this research, a new spatiotemporal fusion (STF) framework is developed to fuse the S2-MSI image (10 m) enhanced by Super-Resolution for multispectral Multiresolution Estimation (SupReME) algorithm and MODIS image (460 m) with a large spatial ratio (46). The proposed fusion method in the new STF framework combines the existing STF methods with Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm, abbreviated as CA-STF. The accuracy of the fused reflectance and its capability for dynamic LAI monitoring were tested in Daman Superstation of Heihe watershed. The results indicate that: (1) the new STF framework is competent to fuse multi-source images with a ratio of 46 and outperforms the existing STF methods for both near-real-time and post-growth applications; (2) the proposed CA-STF method improves the fusion accuracy and spatial details even if only two S2-MSI images are available, especially for post-growth applications; (3) the vegetation indices (VIs) calculated from the fused images by the new STF framework provide a better correlation with LAINet measurements and improve dynamic LAI monitoring in accuracy and spatial details. This study proposes a framework to maximize the spatial and temporal resolution of S2-MSI images for spatiotemporal fusion. The synthetic daily time-series images with a high resolution of 10 m will have great potential for monitoring the dynamic changes of the land surface.