Space-borne SAR is the primary data source for operational monitoring and mapping of sea and lake ice at the Canadian Ice Service (CIS). In addition to RADARSAT-2, recently available Sentinel-1 A and B have provided more capability with enhanced revisit frequency and extended spatial coverage. Considering that the three-satellite RADARSAT Constellation Mission (RCM) will be launched in 2018, the CIS will be receiving hundreds of SAR images daily with almost a complete coverage of the CIS’ seasonal areas of interest. In order to efficiently use and analyze such a large amount and a wide areal extent of data, short-term (i.e. within a day) high-quality mosaic products are of interest. We have developed such sample mosaic products using Sentinel-1 and RADARSAT-2 data. However, it has been noted that there is a border noise issue inherent to Sentinel-1 data at image edges. Such noise needs to be removed before generating a seamless mosaic. Complicating matters further, the level of border noise varies scene to scene and sometimes the noise is even higher than that of valid data, so a simple threshold masking approach is not feasible. A method using line-by-line scanning and filtering is proposed, which traces an extreme jump between two neighboring pixels along a scan line. The results show this method locates and allows us to remove the noise precisely while retaining the rest of the valid data. Mosaicking SAR image frames or swaths acquired at different times, look directions, and observation angles is a challenge due to the scene-to-scene signal and tonal variations. For visual display, analysis, and interpretation – such as that done at the CIS – a tone-balanced smooth mosaic is of interest and value to ice analysts in displaying overall ice distribution and in viewing and comparing cross-region ice conditions. To address this, a scene boundary balancing method is developed to generate a seamless tone-balanced mosaic product. These short-term mosaic products can also be used as baseline data for further processing where raw data with absolute values are not critical, such as animation of a time series and calculation of macroscopic ice drift.
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