## 1 Introduction  
Sea ice motion and deformation measured from satellite remote sensing have played a significant role in understanding the dynamic polar climate system and to predict its ongoing and further long-term changes. Sea ice motion has been measured using low-resolution (e.g., a spatial resolution of 1 km or larger pixel size) satellite remote sensing datasets such as passive microwave data (e.g., Scanning Multichannel Microwave Radiometer (SMMR) [1], Special Sensor Microwave/Imager (SSM/I) [1; 2; 3; 4; 5] and Advanced Microwave Scanning Radiometer for the Earth Observing System (EOS) (AMSR-E) [4; 6; 7]) and optical image data (e.g., Advanced Very High Resolution Radiometer (AVHRR) [8; 9]) from a regional-scale (e.g., polynya or basin) to the entire Arctic or Southern Ocean. Relatively higher spatial resolution satellite radar remote sensing with a spatial resolution from several tens of meters to around a hundred meters) has rarely been applied to the measurement of sea ice motion (e.g., RADARSAT Geophysical processor system (RGPS) [10], GlobICE using Advanced Synthetic Aperture Radar (ASAR) on Envisat, with a spatial resolution of 150 m [11], Envisat ASAR with a spatial resolution of 75 m [12], and RADARSAT-2 ScanSAR of a spatial resolution of 100 m resampled from 50 m [13]). These mid-resolution SAR successfully provided the products from polynya [14] or basin-scale to the entire arctic coverage by systematic data acquisition plan. Radar remote sensing has been recognized as an optimum tool for sea ice motion retrieval because of its all-weather capability and day/night operation; however, operational sensors that can acquire data with proper revisit intervals are limited. The representative and universal technique to measure sea ice motion by satellite remote sensing is by matching a block of prior images to a corresponding block of posterior images by choosing a maximum cross-correlation (MCC) coefficient, with consideration of the image acquisition time interval [2; 4; 15]. The image blocks are usually matched in the areas where prominent and contrast features such as the boundary between ice and ocean are dominant. The drawback of the MCC approach is quantization noise, a degree of discreteness of the motion vector field, related to image pixel size [4; 10]. Sea ice motion can be directly linked to sea ice deformation because the deformation is calculated from spatial gradients of motion [16]. Therefore, satellite radar remote sensing with sub-daily to few days data acquisition intervals has been widely applied to measure the deformation [16; 17]. Few sub-daily deformation measurements were reported in Reference [18]; however, further studies regarding longer periods with short and continuous observation for more detailed understanding of the deformation as a mixture of inertial motion, tidal effect, and wind forcing are necessary [19]. As low-contrast backscatter conditions on the sea ice surface during the melting season or sea ice edge area increases uncertainties in block matching between sequential images, the deformation data were commonly limited to the spring, fall and winter seasons [7], or restricted on inner pack ice areas except ice margins [16]. The possible utility of high-resolution optical satellite images for sea ice motion retrieval was suggested via visual inspection [20]. High-resolution optical airborne observation was applied to measure high temporal and spatial resolution sea ice motion in near real-time [21], but limited to coastal areas because of the coverage restriction of aerial survey. Small-scale kinematics (i.e., deformation) including opening and closing along the fractures in ice cover that can be connected to cracks exposing ocean and altering surface heat balance, and forcing ice into pressure ridges increasing drags between ice-ocean were detected from literal image derived product (LIDP), 1-m resolution panchromatic optical images [22].