Reconstructing cloud-contaminated NDVI images with SAR-Optical fusion using spatio-temporal partitioning and multiple linear regression

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.