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Abstract:
Sea surface temperature (SST) is one of the important parameters of global ocean and climate research, which can be retrieved by satellite infrared and passive microwave remote sensing instruments. While satellite infrared SST offers high spatial resolution, it is limited by cloud cover. On the other hand, passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall, coastal effects, and high wind speeds. To achieve high-precision, comprehensive, and high-resolution SST data, it is essential to fuse infrared and microwave SST measurements. In this study, data from the Fengyun-3D (FY-3D) medium resolution spectral imager Ⅱ (MERSI-Ⅱ) SST and microwave imager (MWRI) SST were fused. Firstly, the accuracy of both MERSI-Ⅱ SST and MWRI SST was verified, and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST. After pretreatment and quality control of MERSI SST and MWRI SST, a Piece-Wise Regression method was employed to correct biases in MWRI SST. Subsequently, SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date. Finally, an optimal interpolation method was applied to fuse the FY-3D MERSI-Ⅱ SST and MWRI SST. The results demonstrated a significant improvement in spatial coverage compared to MERSI-Ⅱ SST and MWRI SST. Furthermore, the fusion SST retained true spatial distribution details and exhibited an accuracy of –0.12±0.74℃ compared to OSTIA SST. This study has improved the accuracy of FY satellite fusion SST products in China.
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