globalchange  > 气候减缓与适应
DOI: 10.1002/2017JD027478
Scopus记录号: 2-s2.0-85041026697
论文题名:
Multi-Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints
作者: Molero B.; Leroux D.J.; Richaume P.; Kerr Y.H.; Merlin O.; Cosh M.H.; Bindlish R.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期:1
起始页码: 3
结束页码: 21
语种: 英语
英文关键词: satellite validation ; soil moisture ; spatial representativeness ; spatial scales ; timescales ; wavelet decomposition
Scopus关键词: data set ; decomposition analysis ; downscaling ; optimization ; satellite ; satellite data ; soil moisture ; spatial data ; timescale ; transform ; wavelet analysis ; Australia ; Little Washita River ; New South Wales ; Oklahoma [United States] ; United States ; Yanco
英文摘要: We conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per-timescale basis by comparison to large spatial scale data sets (the in situ spatial average, SMOS, AMSR2, and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), percentage of correlated areas (CArea), and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings, and TC is suitable for week and month scales but not for other scales where data set cross-correlations are found low. In contrast, WCor and CArea give consistent results at all timescales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks. ©2017. American Geophysical Union. All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114688
Appears in Collections:气候减缓与适应

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作者单位: CESBIO (Centre d'Études Spatiales de la BIOsphère), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France; CNRM (Centre National de la Recherche Météorologique), Météo-France, CNRS, Toulouse, France; USDA-ARS-Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States

Recommended Citation:
Molero B.,Leroux D.J.,Richaume P.,et al. Multi-Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(1)
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