气候变化领域集成服务门户(CCPortal): Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

CCPortal

DOI	10.1080/01431161.2024.2349265
	Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals
	Altuntas, Cemali; Erdogan, Bahattin; Tunalioglu, Nursu
发表日期	2024
ISSN	0143-1161
EISSN	1366-5901
起始页码	45
结束页码	11
卷号	45 期号:11
英文摘要	Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems - Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.
英文关键词	GNSS-IR; GPS; snow; statistics; climate change
语种	英语
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001226490200001
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290168
作者单位	Yildiz Technical University
推荐引用方式 GB/T 7714	Altuntas, Cemali,Erdogan, Bahattin,Tunalioglu, Nursu. Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals[J],2024,45(11).
APA	Altuntas, Cemali,Erdogan, Bahattin,&Tunalioglu, Nursu.(2024).Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals.INTERNATIONAL JOURNAL OF REMOTE SENSING,45(11).
MLA	Altuntas, Cemali,et al."Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals".INTERNATIONAL JOURNAL OF REMOTE SENSING 45.11(2024).