干旱区生态环境知识资源中心(Arid): Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

Arid

DOI	10.1109/ACCESS.2020.2988482
	Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data
	Zhang, Xuefei 1,2; Zhang, Hong 1; Wang, Chao 1,2; Tang, Yixian 1; Zhang, Bo 1; Wu, Fan 1; Wang, Jing 1,2; Zhang, Zhengjia 3
通讯作者	Zhang, Xuefei ; Tang, Yixian
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2020
卷号	8 页码:84336-84351
英文摘要	Increasing near-surface temperature over the Qinghai-Tibet Plateau (QTP) has led to permafrost degradation and increasing active layer thickness (ALT). In this study, the ALT was estimated based on ground subsidence monitored by multitemporal interferometric synthetic aperture radar (MT-InSAR) and temporal-spatial multilayer soil moisture data. For the ground subsidence monitoring, a modified Stefan piecewise elevation change model based on air temperature data was integrated into a new small baseline subset (NSBAS) chain. A total of 33 scenes of Sentinel-1 data (S-1) were collected over one year to build the MT-InSAR analysis network. Moreover, both soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data and ERA-Interim reanalysis data were used to build an ALT retrieval model. In particular, the global-scaled soil moisture data (SMAP and ERA-Interim) fraction was separated based on the Sentinel-1 amplitude-based land cover classification results and in situ soil moisture data. A typical ALT estimation method based on the point scale groundwater information was also performed to evaluate the performance of the proposed method. Based on the validation of the ground-based ALT observations, the proposed method outperformed the traditional point scale groundwater information-based method, with a correlation coefficient of 0.67, RMSE of 0.70 and ubRMSE of 0.51, respectively. The ERA-Interim-based estimation results were underestimated due to the overestimation of the ERA-Interim soil moisture data. Obvious differences were observed between the ALT of the alpine meadow areas and alpine desert areas. Our results demonstrate that the combination of temporal-spatial multilayer soil moisture data and the MT-InSAR method with S-1 images is a promising approach for the large-scale characterization of ALT.
英文关键词	Soil moisture Soil measurements Data models Monitoring Temperature measurement Strain Active layer thickness (ALT) permafrost MT-InSAR NSBAS seasonal subsidence
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000549526700009
WOS关键词	GROUND-PENETRATING RADAR ; DISCONTINUOUS PERMAFROST ; RIVER-BASIN ; INTERFEROMETRY ; DEPTH ; DEFORMATION ; CLIMATE ; ALASKA ; HEAT ; MAP
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319584
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 3.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xuefei,Zhang, Hong,Wang, Chao,et al. Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data[J],2020,8:84336-84351.
APA	Zhang, Xuefei.,Zhang, Hong.,Wang, Chao.,Tang, Yixian.,Zhang, Bo.,...&Zhang, Zhengjia.(2020).Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data.IEEE ACCESS,8,84336-84351.
MLA	Zhang, Xuefei,et al."Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data".IEEE ACCESS 8(2020):84336-84351.