Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes

Images of key phenological periods play a vital role in agricultural applications as they capture the unique spectral characteristics of crops. Unfortunately, acquiring high-spatial-resolution images of the key phenological period from a single satellite platform remains challenging due to its short duration and synchronization with the rainy season. Spatiotemporal fusion (STF) is an effective tool for the prediction of missing high spatial resolution images on the required date, however, most STF methods assume a uniform relationship between the reflectance on the base date and the predicted date for the same land-cover type, which fails to hold in agricultural scenarios because of the diverse phenological changes among different crop types or different growth processes, even within a single crop type. To address these challenges, we propose a novel spatiotemporal fusion method for agricultural scenarios called Agri-Fuse. Agri-Fuse emphasizes change information and classifies change types based on the difference image derived from the fine image on the base date and the coarse image on the predicted date. Considering the various regression relationships between reflectance on the base date and the predicted date for different change types, a category-based linear regression model was built, and we introduced an unmixing model to solve these regression coefficients. We validated the performance of Agri-Fuse at two experimental sites where diverse phenological changes occurred during the key phenological periods of the same and different crop types. Compared to the four typical fusion methods (FSDAF, RASDF, STARFM, and Fit-FC), Agri-Fuse achieved the best performance in preserving both spectral fidelity and spatial details. Ablation experiments further confirmed the effectiveness of the proposed change-type classification and category-based regression in capturing diverse phenological change information. Thus, Agri-Fuse is expected to precisely reconstruct high-spatial-resolution images of key phenological periods necessary for agricultural applications.