气候变化领域集成服务门户

Spatiotemporal complete sea surface temperature (SST) dataset with higher accuracy and resolution is desirable for many studies in atmospheric science and climate change. The purpose of this study is to establish the spatiotemporal data fusion model, the Hierarchical Bayesian Model (HBM) based on Robust Fixed Rank Filter (R-FRF), that merge Moderate Resolution Imaging Spectroradiometer (MODIS) SST with 4-km resolution and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) SST with 25-km resolution through their spatiotemporal complementarity to obtain fusion SST with complete coverage, high spatial resolution, and fine spatial pattern. First, a bias correction model was applied to correct satellite SST. Second, a spatiotemporal model called R-FRF was established to model potential spatiotemporal process of SST. Third, the R-FRF model was embedded in the hierarchical Bayesian framework, and the corrected MODIS and AMSR-E SST are merged. Finally, the accuracy, spatial pattern and spatial completeness of the fusion SST were assessed. The results of this study are the following: (a) It is necessary to carry out bias correction before data fusion. (b) The R-FRF model could simulate SST spatiotemporal trend well. (c) Fusion SST has similar accuracy and spatial pattern to MODIS SST. Though the accuracy is lower than that of the AMSR-E SST, the fusion SST has more local detail information. The results indicated that fusion SST with higher accuracy, finer spatial pattern, and complete coverage can be obtained through HBM based on R-FRF.