Abstract Changes in climate, warming temperatures and increased precipitation are impacting surface water resources in the Northwest Territories, Canada. Satellite remote sensing is an important tool to monitor variability in lake surface area, but monitoring depth is challenging. The distribution of bottomfast ice within a lake provides an indicator of depth and previous research shows that as lake ice develops and becomes bottomfast it exhibits a distinct signature when observed using multi-temporal Synthetic Aperture Radar (SAR) data. This research proposes an efficient computational technique for identifying bottom-fast ice across lakes in the Northwest Territories using multi-temporal SAR backscatter images and applies a function called dynamic time warping (DTW), which provides a shape-based similarity metric for time series data. We used backscatter profiles from surveyed lakes with known bottomfast ice to generate a DTW similarity metric on a pixel by pixel basis for a set of lakes. The similarity metric was used to categorize ice status as bottomfast or floating ice with 89.1% accuracy. DTW is an effective technique to map bottomfast ice using SAR time series and has potential to address limitations of other approaches where certain ice structures over deep lakes can produce backscatter responses similar to bottomfast ice.

中文翻译：

---

通过合成孔径雷达图像时间序列数据挖掘绘制西北地区Bottomfast Lake冰面图

摘要 气候变化、气温升高和降水增加正在影响加拿大西北地区的地表水资源。卫星遥感是监测湖泊表面积变化的重要工具，但监测深度具有挑战性。湖内底冰的分布提供了深度的指标，之前的研究表明，随着湖冰的发展和成为底冰，当使用多时相合成孔径雷达 (SAR) 数据观察时，它表现出明显的特征。这项研究提出了一种有效的计算技术，用于使用多时相 SAR 反向散射图像识别西北地区湖泊中的底部快速冰，并应用称为动态时间扭曲 (DTW) 的函数，该函数为时间序列数据提供基于形状的相似性度量. 我们使用来自具有已知底部快速冰的调查湖泊的反向散射剖面，为一组湖泊逐个像素地生成 DTW 相似性度量。相似性度量用于将冰状态分类为底部快速或浮冰，准确率为 89.1%。DTW 是一种使用 SAR 时间序列绘制底部快速冰的有效技术，并且有可能解决其他方法的局限性，在这些方法中，深湖上的某些冰结构可以产生类似于底部快速冰的反向散射响应。