New quantitative and semiautomated methods for analyzing oil slick evolution using a time series of $L$-band synthetic aperture radar (SAR) images with short repeat time are developed and explored. In this study, two methods that are complementary in terms of identifying temporal changes within an oil slick are presented. The two methods reflect two ways of evaluating the oil slicks. The first method identifies regions within the slick that show persistently high damping ratio (the contrast between clean sea and oil intensity), using higher damping values as a proxy for increasing oil thickness. This method also weights the age of the scenes as the algorithm incorporates new images. The second method outputs the short-term drift pattern and the changes in the damping ratios and copolarization ratios between two scenes, proxies for thickness, and emulsification. Both methods can aid in identifying regions of high priority for oil recovery. Due to the simplicity of the methods, they can be adapted to time-series data from different types of sensors, e.g., optical and SAR imagery. The methods are demonstrated on three $L$-band uninhabited aerial vehicle SAR UAVSAR time series acquired in November 2016 over a persistent seep in the Mississippi Canyon Block 20 of the Gulf of Mexico. The results of the two methods clearly show the movement and the weathering of the oil as a function of both time and location.

中文翻译：

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从 SAR 图像的快速重复时间序列导出的面向溢油响应的信息产品

开发和探索了新的定量和半自动方法，用于使用具有短重复时间的 $L$ 波段合成孔径雷达 (SAR) 图像的时间序列分析浮油演化。在这项研究中，提出了两种在识别浮油内时间变化方面互补的方法。这两种方法反映了评价浮油的两种方法。第一种方法识别浮油中显示持续高阻尼比（清洁海和油强度之间的对比）的区域，使用更高的阻尼值作为增加油厚度的代理。当算法合并新图像时，该方法还会对场景的年龄进行加权。第二种方法输出短期漂移模式以及两个场景之间的阻尼比和共极化比的变化，代表厚度，和乳化。这两种方法都有助于确定石油采收的高优先级区域。由于这些方法的简单性，它们可以适用于来自不同类型传感器（例如光学和 SAR 图像）的时间序列数据。这些方法在 2016 年 11 月在墨西哥湾密西西比峡谷 20 区块的持续渗漏中获得的三个 $L$ 波段无人飞行器 SAR UAVSAR 时间序列上得到了证明。这两种方法的结果清楚地显示了油的运动和风化随时间和位置的变化。这些方法在 2016 年 11 月在墨西哥湾密西西比峡谷 20 区块的持续渗漏中获得的三个 $L$ 波段无人飞行器 SAR UAVSAR 时间序列上得到了证明。这两种方法的结果清楚地显示了油的运动和风化随时间和位置的变化。这些方法在 2016 年 11 月在墨西哥湾密西西比峡谷 20 区块的持续渗漏中获得的三个 $L$ 波段无人飞行器 SAR UAVSAR 时间序列上得到了证明。这两种方法的结果清楚地显示了油的运动和风化随时间和位置的变化。