## 3 Methods
### Measuring Sea Ice Motion and Deformation
#### 3.1.2 Maximum Cross-Correlation Approach for Measuring Sea Ice Motion  
Six image pairs composed of four sequential images were considered for measuring sea ice motion (Table 4). Intervals between the image acquisition times of each image pair varied from about 2 h to about 27 h. Among the image pairs, four pairs consisted of images from different sensors, i.e., the KOMPSAT-2 and KOMPSAT-3, and two pairs consisted of single sensor images. In the MCC approach, cross-correlation coefficients were calculated between the template window of the prior image and the search area of the posterior image by sliding a search window of identical size of the template window within the search area, before the window pairs revealing maximum cross-correlation coefficient were designated as matching pairs. To speed up the computation of the cross-correlation coefficients and to avoid false matching occurring in the not overlapped region (without the necessity of prior knowledge about practical moving distance of ice floes), hierarchical fashion was applied. First, the template window size and the sliding distance were set as 75 pixels (300 m) and 300 pixels (1200 m) for the 4 m spatial resolution images, and 75 pixels (1125 m) and 80 pixels (1200 m) for the 15 m spatial resolution images, respectively, to initialize coarse grid points; next, the sea ice motion was delineated from this coarse grid. The search area was formed with the edges apart from the center coordinates of the template window in the posterior image for every template window sliding. The edge lengths of the square-shaped search area varied from three to six times that of the median magnitude of the motion vectors extracted from the coarse grid to form the search area of enough size including common trackable ice floes in sequential images, with consideration of the template window size. After determining the search area, a finer grid was established as the sliding distance of 30 pixels (120 m) for the 4 m spatial resolution images, and 8 pixels (120 m) for the 15 m spatial resolution images, respectively, and the motion was delineated with the revised sliding distance. This fine grid used to extract the sea ice motion also composed cells for calculating sea ice deformation. To prevent errors possibly occurring at image borders as a false matching [24], the template windows consisting only of non-zero pixels were considered in the matching procedure, and then the threshold applied to the cross-correlation coefficients for sea ice motion retrieval was set to 0.6. Moreover, remaining outlier motion vectors from the false matching (that could also occur in the regions not exactly overlapped position) between sequential images were filtered and removed where vectors were outside three times the inter-quartile range, if necessary, as the magnitude and direction of the outliers showed large variance compared with the gentle gradient of magnitude and direction in the highly correlated fine grid motion vector field. Sea ice coverage, a ratio of ice pixels to template window area, in the template window was calculated to understand the effects of complexity of image texture reflecting sea ice conditions on the cross-correlation coefficient. Among various approaches for identifying sea ice concentration from optical images, simple and automated [PERSON]'s thresholding [34], maximizing inter-class variance, and minimizing intra-class variation of two cluster representing bi-modal histogram [35] were applied. Additionally, to consider the effect of image complexity in the template window, reflecting randomness of shape features of sea ice such as ice floe size, pressure ridge and melt pond distributions,