Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024581 |
Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach | |
Kathuria, Dhruva1; Mohanty, Binayak P.1; Katzfuss, Matthias2 | |
2019-12-10 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:12页码:10443-10465 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Surface soil moisture (SSM) has been identified as a key climate variable governing hydrologic and atmospheric processes across multiple spatial scales at local, regional, and global levels. The global burgeoning of SSM datasets in the past decade holds a significant potential in improving our understanding of multiscale SSM dynamics. The primary issues that hinder the fusion of SSM data from disparate instruments are (1) different spatial resolutions of the data instruments, (2) inherent spatial variability in SSM caused due to atmospheric and land surface controls, and (3) measurement errors caused due to imperfect retrievals of instruments. We present a data fusion scheme which takes all the above three factors into account using a Bayesian spatial hierarchical model (SHM), combining a geostatistical approach with a hierarchical model. The applicability of the fusion scheme is demonstrated by fusing point, airborne, and satellite data for a watershed exhibiting high spatial variability in Manitoba, Canada. We demonstrate that the proposed data fusion scheme is adept at assimilating and predicting SSM distribution across all three scales while accounting for potential measurement errors caused due to imperfect retrievals. Further validation of the algorithm is required in different hydroclimates and surface heterogeneity as well as for other data platforms for wider applicability. |
英文关键词 | soil moisture remote sensing scaling multi-instrument data fusion spatial hierarchical model uncertainty physical controls |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000501832600001 |
WOS关键词 | AMSR-E ; EVOLUTION ; ASSIMILATION ; VALIDATION ; HYDROLOGY ; SCALES |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/223966 |
专题 | 资源环境科学 |
作者单位 | 1.Texas A&M Univ, Biol & Agr Engn, College Stn, TX 77843 USA; 2.Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA |
推荐引用方式 GB/T 7714 | Kathuria, Dhruva,Mohanty, Binayak P.,Katzfuss, Matthias. Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach[J]. WATER RESOURCES RESEARCH,2019,55(12):10443-10465. |
APA | Kathuria, Dhruva,Mohanty, Binayak P.,&Katzfuss, Matthias.(2019).Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach.WATER RESOURCES RESEARCH,55(12),10443-10465. |
MLA | Kathuria, Dhruva,et al."Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach".WATER RESOURCES RESEARCH 55.12(2019):10443-10465. |
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