Paper Info
Title
Inferring the impact of radar incidence angle on soil moisture retrieval skill using data assimilation
Abstract
The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil moisture (Θs) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficientlyextensive ground-based Θs observations for validation. Here, we apply a data assimilation-based evaluation technique for remotely-sensed Θs retrievals that does not require groundbased soil moisture observations to examine the sensitivity of skill in surface Θs retrievals to variations in θ. Application of the evaluation approach to the TU-Wien European Remote Sensing (ERS) scatterometer Θs data set over regional-scale (~10002 km2) domains in the Southern Great Plains (SGP) and Southeastern (SE) regions of the United States indicate a relative reduction in correlation-based skill of 23% to 30% for Θs retrievals obtained from far-field (θ > 50°) ERS observations relative to Θs estimates obtained at θ <; 26°. Such relatively modest sensitivity to θ is consistent with Θs 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 θ on Θs retrieval skill.
Year
DOI
Venue
2010
10.1109/IGARSS.2010.5650151
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
backscatter,data assimilation,hydrological techniques,remote sensing by radar,soil,vegetation,ERS scatterometer,Southeastern United States,Southern Great Plains,TU-Wien ERS Water Retrieval Package 5,TU-Wien European Remote Sensing,WARP5 backscatter model,bare soil backscatter model,data assimilation,ground-based soil moisture observations,radar incidence angle,radar-based surface soil moisture retrievals,remote sensing,theoretical backscatter model,vegetation cover,vegetation water cloud canopy model,Data Assimilation,Radar,Remote sensing,Soil moisture
Radar,Vegetation,Computer science,Angle of incidence,Backscatter,Remote sensing,Scatterometer,Water content,Data assimilation,Surface roughness
Conference
ISSN
ISBN
Citations 
2153-6996 E-ISBN : 978-1-4244-9564-1
978-1-4244-9564-1
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Wade T. Crow153855.96
W. Wagner211413.79
Vahid Naeimi313615.29