气候变化领域集成服务门户(CCPortal): Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river

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DOI	10.1016/j.rse.2020.111684
	Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river
	Brombacher J.; Reiche J.; Dijksma R.; Teuling A.J.
发表日期	2020
ISSN	00344257
卷号	241
英文摘要	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 Þjórsá (Thjórsá) 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. © 2020 Elsevier Inc.
英文关键词	Discharge estimation; High latitudes; Hydrology; Iceland; Machine learning; Near-daily; Sentinel-1; Sentinel-2
语种	英语
scopus关键词	Climate change; Decision trees; Hydrology; Learning systems; Random forests; Satellites; Supervised learning; Discharge estimation; High Latitudes; Icelands; Near-daily; Sentinel-1; Sentinel-2; Rivers; backscatter; diurnal variation; hydrological regime; latitude; machine learning; precipitation intensity; rating curve; river discharge; Sentinel; supervised learning; Iceland
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179389
作者单位	Hydrology and Quantitative Water Management Group, Wageningen University & Research, Netherlands; Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Netherlands
推荐引用方式 GB/T 7714	Brombacher J.,Reiche J.,Dijksma R.,et al. Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river[J],2020,241.
APA	Brombacher J.,Reiche J.,Dijksma R.,&Teuling A.J..(2020).Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river.Remote Sensing of Environment,241.
MLA	Brombacher J.,et al."Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river".Remote Sensing of Environment 241(2020).