An Algorithm for Sea Ice Drift Retrieval Based on Trend of Ice Drift Constraints from Sentinel-1 SAR Data.

Zhang, X.; Zhu, Y. X.; Zhang, J.; Meng, J.; Li, X., and Li, X., 2020. An algorithm for sea ice drift retrieval based on trend of ice drift constraints from Sentinel-1 SAR data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 113-126. Coconut Creek (Florida), ISSN 0749-0208. Realizing high-resolution (sub-kilometer scale) automatic detection of sea ice drift from massive Synthetic Aperture Radar (SAR) data is an important requirement for mastering polar sea ice dynamics and better understanding global climate change. A fully automatic SAR sea ice drift detection method has been developed in this paper, which is based on the idea of multi-scale observation. First, ice cracks or lead structures was detected from down-sampled sequential SAR images. Because the ice cracks or leads have scattering intensities and shape features which differ significantly from the surrounding ice regions, these structures can help to detect the overall drift of the sea ice region which we called the trend of ice drift (TID). Then, a modified feature tracking (FT) method was used to extract sea ice drift vectors from the original SAR imagery by considering the constraints of TID. The aims of the proposed algorithm are to improve the accuracy of sea ice drift retrieval and the spatial density of ice drift vectors, as well as reducing execution time. Three pairs of dual-polarization Sentinel-1 SAR images of the Arctic were used for experimental validation. The experimental results show that the proposed method takes the least time, and the extracted sea ice drift vector is far more than the classical Speeded-up Robust Features (SURF) method and Normalized cross-correlation (NCC) method in both spatial density and area coverage. In terms of the accuracy of sea ice drift speed and direction detection, for HH polarization, the sea ice drift speed retrieved by the proposed method has a Root Mean Square Error (RMSE) of 0.158 cm/s and a relative error (RE) of 1.838 %; and the RMSE of retrieved sea ice drift direction is 0.112° and the RE is 0.267 %. For HV polarization, the RMSE of sea ice drift velocity inversion is 0.138 cm/s and RE is 1.504 %; the RMSE of sea ice drift direction inversion is 0.123°and RE is 0.753 %. For the dual-polarization SAR data, compared with SURF and NCC methods, the proposed algorithm performs better. [ABSTRACT FROM AUTHOR]