Abstract Climate change is a threat to many high-latitude regions. Changing patterns in precipitation intensity and increasing glacial ablation during spring and summer have major influence on river dynamics and the risk of widespread flooding. To monitor these rapid events, more frequent discharge observations are necessary. Having access to near-daily satellite based discharge observations is therefore highly beneficial. In this context, the recently launched Sentinel-1 and 2 satellites promise unprecedented potential, due to their capacity to obtain radar and optical data at high spatial (10 m) and high temporal (1–3 days) resolutions. Here, we use both missions to provide a novel approach to estimate the discharge of the Þjorsa (Thjorsa) river, Iceland, on a near-daily basis. Iceland, and many other high-latitude regions, are affected by frequent cloud-cover, limiting the availability of cloud-free optical Sentinel-2 data. We trained a Random Forest supervised machine learning classifier with a set of Sentinel-1 backscatter metrics to classify water in the individual Sentinel-1 images. A Sentinel-2 based classification mask was created to improve the classification results. Second, we derived the river surface area and converted it to the effective width, which we used to estimate the discharge using an at-a-station hydraulic geometry (AHG) rating curve. We trained the rating curve for a six-month training period using in situ discharge observations and assessed the effect of training area selection. We used the trained rating curve to estimate discharge for a one-year monitoring period between 2017/10 and 2018/10. Results showed a Kling-Gupta Efficiency (KGE) of 0.831, indicating the usefulness of dense Sentinel-1 and 2 observations for accurate discharge estimations of a medium-sized (200 m width) high-latitude river on a near-daily basis (1.56 days on average). We demonstrated that satellite based discharge products can be a valuable addition to in situ discharge observations, also during ice-jam events.

中文翻译：

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Sentinel-1 和 2 高纬度地区近乎每日的流量估算：冰岛 Þjórsá 河的案例研究

摘要 气候变化对许多高纬度地区构成威胁。春季和夏季降水强度模式的变化和冰川消融的增加对河流动态和大面积洪水的风险有重大影响。为了监测这些快速事件，更频繁的放电观察是必要的。因此，能够获得近乎每天基于卫星的放电观测是非常有益的。在这种情况下，最近发射的 Sentinel-1 和 2 卫星有望获得前所未有的潜力，因为它们能够以高空间（10 m）和高时间（1-3 天）分辨率获取雷达和光学数据。在这里，我们使用这两个任务提供了一种新方法来估计冰岛 Þjorsa (Thjorsa) 河几乎每天的流量。冰岛和许多其他高纬度地区，受频繁云覆盖的影响，限制了无云光学 Sentinel-2 数据的可用性。我们使用一组 Sentinel-1 反向散射指标训练了一个随机森林监督机器学习分类器，以对单个 Sentinel-1 图像中的水进行分类。创建了基于 Sentinel-2 的分类掩码以改善分类结果。其次，我们导出河流表面积并将其转换为有效宽度，我们用它来使用站内水力几何 (AHG) 评级曲线来估计流量。我们使用原位排放观察对为期六个月的培训期的评级曲线进行了培训，并评估了培训区域选择的效果。我们使用经过训练的评级曲线来估计 2017/10 和 2018/10 之间为期一年的监测期的排放量。结果显示 Kling-Gupta 效率 (KGE) 为 0.831，表明密集​​的 Sentinel-1 和 2 观测值对于中型（200 m 宽）高纬度河流几乎每天（1.56平均天数）。我们证明了基于卫星的放电产品可以是对原位放电观测的有价值的补充，在冰塞事件期间也是如此。