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Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar.
Crow, Wade T; Milak, Sushil; Moghaddam, Mahta; Tabatabaeenejad, Alireza; Jaruwatanadilok, Sermsak; Yu, Xuan; Shi, Yuning; Reichle, Rolf H; Hagimoto, Yutaka; Cuenca, Richard H.
Afiliação
  • Crow WT; USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA.
  • Milak S; USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA and SSAI, Lanham, MD, USA.
  • Moghaddam M; University of Southern California, Los Angeles, CA, USA.
  • Tabatabaeenejad A; University of Southern California, Los Angeles, CA, USA.
  • Jaruwatanadilok S; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
  • Yu X; Depart. of Geological Science, University of Delaware, Newark, DE, USA.
  • Shi Y; Depart. of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, USA.
  • Reichle RH; NASA Goddard Space Flight Center, Global Model and Assimilation Office, Greenbelt, MD, USA.
  • Hagimoto Y; Oregon State University, Corvallis, OR, USA.
  • Cuenca RH; Oregon State University, Corvallis, OR, USA.
IEEE J Sel Top Appl Earth Obs Remote Sens ; 11(12): 4578-4590, 2018 Dec.
Article em En | MEDLINE | ID: mdl-32577149
ABSTRACT
The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of sub-grid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here we compare 3-arc-sec (~100-m) spatial resolution AirMOSS RZSM retrievals from P-band radar acquisitions over 9 separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Hydrology Model (Flux-PIHM). The two products demonstrate comparable levels of accuracy when evaluated against ground-based soil moisture products and a significant level of temporal cross-correlation. However, relative to the AirMOSS RZSM retrievals, Flux-PIHM RZSM estimates generally demonstrate much lower levels of spatial and temporal variability, and the spatial patterns captured by both products are poorly correlated. Nevertheless, based on a discussion of likely error sources affecting both products, it is argued that the spatial analysis of AirMOSS and Flux-PIHM RZSM fields provide meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IEEE J Sel Top Appl Earth Obs Remote Sens Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IEEE J Sel Top Appl Earth Obs Remote Sens Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos