Publications

Chen, S; Wang, J; Gong, P (2023). ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications. REMOTE SENSING OF ENVIRONMENT, 294, 113616.

Abstract
Dense time-series high-resolution satellite images are extremely valuable for long-term monitoring of land dynamics. Spatiotemporal fusion (STF) techniques have been developed to integrate multi-resolution satellite images to produce data with high spatial resolution and temporal frequency. Due to the large volume and diversity of higher resolution global Earth Observation (EO) data, large-scale data processing methods need to be computationally efficient, free of parameter fine-tuning, and adaptive to various data structures without process customization. These requirements are especially critical for the production of global Seamless Data Cube (SDC). Considering the limitations of existing STF methods, we propose a ROBust OpTimization-based (ROBOT) fusion model that exploits time-series information to obtain more accurate predictions. ROBOT maintains stable under varied data conditions by adopting a temporal-coherence regularization term. And being free of parameter tuning, ROBOT can be applied to arbitrary spatiotemporally distributed data without repetitive process customization. Its performance was compared with eight representative STF methods. Results show that ROBOT outperforms existing STF methods in most cases and is computationally efficient, about 4000-fold faster than ESTARFM and 600-fold faster than FSDAF. The proposed method has demonstrated its potential for global-scale SDC generation to support subsequent remote sensing applications.

DOI:
10.1016/j.rse.2023.113616

ISSN:
1879-0704