A new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR Wide images for subsequent assimilation in ice numerical models is presented. First, we extended our previously introduced ice and water detection approach operating at a 2.05 km $\times2.05$ km spatial scale to a set of 19 different spatial scales ranging from 2.05 km (41 pixels) down to 0.25 km (5 pixels). As the spatial resolution was increased, the overall accuracy of ice and water detection stayed at a very high level across all scales (between 99.5% and 99.8%), but the number of water retrievals substantially dropped. Second, we designed an approach for estimating ice concentration in a 2 km $\times 2$ km ( $40\times40$ pixels) area consisting of $64\,\,5 \times 5$ pixel blocks. The $5\times 5$ pixel blocks which are initially classified as unknowns are iteratively combined in clusters with effective spatial scales larger than 5 pixels. The clusters are further classified as ice or water using the ice probability model corresponding to the effective spatial scale. The $40\times40$ pixel area becomes populated with high-resolution ( $5\times 5$ pixels) ice and water retrievals, and the ice concentration is estimated based on the number of ice and water retrievals. The proposed approach produces a much better agreement with the Canadian Ice Service Image Analysis ice concentrations (root-mean-square error (RMSE) = 2.2%) compared to the original 2-km ice/water detection approach (RMSE = 19.9%). The developed technique will be adapted to the RADARSAT Constellation Mission data for data assimilation in Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

中文翻译：

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使用多个空间尺度的冰水反演从双极化 SAR 图像中获得冰浓度

提出了一种从 RADARSAT-2 双极化 HH-HV ScanSAR Wide 图像自动检索冰浓度的新技术，用于随后在冰数值模型中同化。首先，我们扩展了之前介绍的在 2.05 公里处运行的冰水探测方法。 $\times2.05$ 公里空间尺度到一组 19 个不同的空间尺度，范围从 2.05 公里（41 个像素）到 0.25 公里（5 个像素）。随着空间分辨率的提高，冰水检测的整体精度在所有尺度上都保持在非常高的水平（99.5% 到 99.8% 之间），但水检索的数量大幅下降。其次，我们设计了一种估算 2 公里范围内冰浓度的方法。 $\times 2$ 公里（ $40\times40$ 像素）区域由 $64\,\,5 \times 5$ 像素块。这 $5\乘以5$ 最初被分类为未知的像素块被迭代地组合成具有大于 5 个像素的有效空间尺度的集群。使用与有效空间尺度相对应的冰概率模型，将聚类进一步分类为冰或水。这 $40\times40$ 像素区域被高分辨率（ $5\乘以5$ 像素）冰和水检索，冰浓度是根据冰和水检索的数量估计的。与原始的 2 公里冰/水检测方法（RMSE = 19.9%）相比，所提出的方法与加拿大冰服务图像分析冰浓度（均方根误差 (RMSE) = 2.2%）的一致性要好得多。开发的技术将适用于 RADARSAT 星座任务数据，用于加拿大环境和气候变化区域冰海预测系统中的数据同化。