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United States Department of Agriculture

Agricultural Research Service

Title: Inferring the impact of radar incidence angle on soil moisture retrieval skill using data assimilation)

item Crow, Wade
item Wagner, Wolfgang
item Naemi, Vahid
item Wien, Tu

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/15/2010
Publication Date: 7/30/2010
Citation: Crow, W.T., Wagner, W., Naemi, V., Wien, T. 2010. Inferring the impact of radar incidence angle on soil moisture retrieval skill using data assimilation. In: Proceedings of the International Geoscience and Remote Sensing Symposium Proceedings, July 25-30, 2010, Honolulu, Hawaii. 2010 CDROM.

Interpretive Summary:

Technical Abstract: The impact of measurement incidence angle (') on the accuracy of radar-based surface soil moisture (') retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficiently extensive ground-based ' observations for validation. Here, we apply a data assimilation-based evaluation technique for remotely-sensed _s retrievals that does not require ground-based soil moisture observations to examine the sensitivity of skill in surface ' retrievals to variations in '. Application of the evaluation approach to the TU-Wien European Remote Sensing (ERS) scatterometer ' data set over regional-scale (10002 km2) domains in the Southern Great Plains (SGP) and Southeastern (SE) regions of the United States indicates a relative reduction in correlation-based skill of 23% to 30% for ' retrievals obtained from far-field (' > 50 degrees) ERS observations relative to ' estimates obtained at ' < 26 degrees. Such relatively modest sensitivity to ' is consistent with ' retrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 (WARP5) backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a vegetation water cloud canopy model is shown to overestimate the impact of ' on ' retrieval skill.

Last Modified: 05/23/2017
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