Enhancing the Quality of FY-3D MERSI-II TIR Images: An Application to Improve Sea Ice Lead Detection

The challenges of utilizing the 250-m resolution thermal infrared (TIR) data obtained from the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Chinese Fengyun-3D (FY-3D) satellite are bowtie effect and nonuniform brightness stripe noise. While previous solutions have addressed these issues separately, this article introduced a more integrated two-step image quality enhancement strategy for MERSI-II TIR images. It considered the interactions between the two issues and overcame the excessive or inadequate destriping in existing models due to the ideal stripe-type assumption. Specifically, for the bowtie effect, a rigorous geometric model suitable for MERSI-II was constructed by considering the Earth’s curvature and adjusting the preset image width. For the nonuniform brightness stripe noise, a novel adaptive multiscale frequential (AMSF) algorithm was developed. The multiscale spectral detection effectively captured the anomaly frequency, and the adaptive threshold dynamically adjusted the detection range, profiting in preserving details. The proposed strategy was validated on MERSI-II TIR images, outperforming existing methods in quantitative and qualitative assessments with higher efficiency on both bowtie effect and stripe noise removal. Further experiments conducted on Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrated the AMSF algorithm’s applicability to different data. In addition, the 250-m MERSI-II FY-3D data can help us understand the rapid variations of Arctic sea ice leads, which are key features within the sea ice. Using the quality-enhanced images to extract the sea ice leads in winter Arctic Baffin Bay improved the overall accuracy from 0.88 to 0.95, thereby providing more accurate and reliable sea ice lead data.