Abstract

Optical satellite imagery is an important Earth observation data source, yet when clouds are present, they provide limited utility for land surface applications. Synthetic Aperture Radar (SAR)-Optical data fusion models predict the missing reflectance values through the correlation between optical images and cloud-insensitive SAR images often using deep learning to train the model. However, most existing SAR-Optical data fusion methods did not incorporate temporal correlation optimally as they were not trained on dense and localized time-series data. Herein, we develop a new SAR-Optical data fusion method that incorporated spatial, temporal, and cross-data-source correlation in the same framework. The method uses spatio-temporal (ST) partitioning and pixel-wise multiple linear regression (MLR) and is named ST-MLR. The parsimonious structure of ST-MLR provides training-efficient model development, enabling the incorporation of full spatio-temporal information for a specific site. ST-MLR was validated with NDVI as the target in seven sites across a wide range of environments and landcovers. Both quantitative and qualitative results demonstrated the potential of ST-MLR to reproduce the target variable accurately with respect to both spatial and temporal dynamics. Although ST-MLR had relatively less accuracy when reconstructing multi-band images than when reconstructing the NDVI, its results were comparable to existing reconstruction methods in this regard. Compared with traditional optical image reconstruction methods and deep learning SAR-Optical fusion methods, ST-MLR is a simple, fast and reasonably accurate model, especially when filling large spatial gaps. ST-MLR is accessible to anyone regardless of compute capability as it can be implemented on Google Earth Engine — a public cloud computing platform. ST-MLR can be used as a benchmark to evaluate the performance of more complicated models such as those based on deep learning. The ST-MLR code is publicly available at https://github.com/yongjingmao/SAR-OPT_fusion_GEE.

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