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Conference Paper

Sea ice drift retrieval based on fengyun-3D multi-sensor data

Authors

Wang,  Xue
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Xu,  Zhizhuo
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lu,  Ran
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Chen,  Zhuoqi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Hui,  Fengming
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Cheng,  Xiao
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Wang, X., Xu, Z., Lu, R., Chen, Z., Hui, F., Cheng, X. (2023): Sea ice drift retrieval based on fengyun-3D multi-sensor data, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0771


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016727
Abstract
Under the background of global warming, sea ice changes rapidly. Sea ice drift is an important indicator for sea ice flux, atmospheric and ocean circulation, and ship navigation. Currently, the large-scale observed sea ice drift datasets are mainly obtained based on single-sensor remotely sensed data, which suffer low spatial resolution or poor spatial continuity. Considering that passive microwave radiometer and medium-resolution optical sensor complement each other in terms of spatial resolution and continuity, this study proposed a novel sea ice drift retrieval method based on Fengyun-3D (FY-3D) multi-sensor data. The proposed method is summarized as follows. First, low resolution sea ice drift fields were obtained from FY-3D Microwave Radiation Imager (MWRI) data based on the normalized cross-correlation pattern-matching method. Then, fine resolution vectors were extracted from FY-3D Medium-Resolution Spectral Imager (MERSI) data based on A-KAZE feature-tracking method. Finally, the low resolution pattern-matching vectors and fine resolution feature-tracking vectors were merged together based on Co-Kriging algorithm to obtain the final sea ice drift result. The proposed method was evaluated by comparing the buoy displacements obtained from the International Arctic Buoy Program (IABP) with the retrieved merged vectors from FY-3D remotely sensed images collected in the Beaufort Sea, the East Siberian Sea, and the Fram Strait on 2020. The results showed that the proposed method can retrieve accurate, fine resolution and spatial continuous sea ice motion fields.