## 5 Discussion
The horizontal positioning error of GPS, e.g., the Navman Jupiter 21 GPS receiver used for some ITPs [32], is known to be 5.2 m (2d RMS) [47], and other kinds of receivers equipped in the ITPs may cause a similar number of positioning errors. The geolocation accuracy of the KOMPSAT-2 and KOMPSAT-3 satellite images assessed in December 2014 were reported as 62.8 m and 40.6 m CE90 (circular error at 90%), respectively [48]. Although the errors in the sea ice motion were combined with the GPS positioning error of the ITP80, the geolocation error of the satellite images, and measurement unit (e.g., the sliding distance of template window or pixel size) of the MCC approach, the RMSEs of the sea ice motion measurement in this study were lower than previous studies using low-resolution remote sensing datasets (e.g., about 323 m for the RGPS using 100 m spatial resolution SAR data [17] and about 3-12 km for low-resolution passive microwave data [40]). The cross-correlation coefficient and the sea ice coverage of the image blocks from the prior image showed negative correlations as negative [PERSON]'s tau coefficients in the image pairs of both spatial resolutions, indicating that lower sea ice concentration was more favorable as a condition to measure precise sea ice motion in the high sea ice concentration generally in the range of 90-100% SIC from NSIDC's passive microwave remote sensing based data as in this study (Table 6). On the other hand, the cross-correlation coefficient and the entropy of the image blocks from the prior image showed positive correlations as positive Kendall's tau coefficients in the image pairs of both spatial resolutions, indicating that higher complexity, i.e., combinations of smaller ice floe size, complicated boundary of ice floe, dense pressure edge and numerous melt pond, was a more favorable condition to measure precise sea ice motion (Table 6). The use of a less saturated band image also prevented the hampering of precise matching in the high sea ice concentration conditions from dimming and flattening small-scale morphological features such as the pressure edge and its shadow on the sea ice surface. In Figures 10-12, a higher entropy of the image blocks, which was a more favorable condition to measure sea ice motion using the MCC approach, was from sea ice coverages around 50%. This could be linked to the capability that enabled precise image block matching in sea ice melting season or sea ice edge area occupying a complex mixture of sea ice and open ocean using the multi-sensor high-resolution optical satellite images. The results of the sea ice deformation measurements illustrate that prominent differences of the sea ice displacement lengths were revealed in the divergence (Figure 13a), and individual floes as unit moving plates were clearly separated by high shear (Figure 13b) from the image pairs of the 4 m spatial resolution. Thus, this small-scale deformation from high-resolution images can support understanding the interaction among individual ice floes, the aggregation of ice floes with over time, and the formation of leads or ridges [49]. Conversely, the openings and closings, and shear between the ice floes were less detectable from the image pairs resampled to the spatial resolution of 15 m (Figure 13) as the detailed ice floe moving was more homogenized from quantization to larger pixel size as quantization noise. The wider distributions of spiky peaks in the histograms of the divergence and shear in the image pairs resampled to the spatial resolution of 15 m (Figure 14) also reflect less sensitivity of deformation measurement for the enlarged pixel size, indicating discretization uncertainty [17]. Increased detection limits of deformations were also identifiable from the wider distribution of peaks in the histograms.