The Impact of Radar Incidence Angle on Soil-Moisture-Retrieval Skill

The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil-moisture (Θ
s
) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficiently extensive ground-based Θ
s
observations for validation. Here, we apply a data-assimilation-based evaluation technique for remotely sensed Θ
s
retrievals that does not require ground-based soil-moisture observations to examine the sensitivity of skill in surface Θ
s
retrievals to variations in θ. Past results with the evaluation approach have shown that it is capable of detecting relative variations in the anomaly correlation coefficient between remotely sensed Θ
s
retrievals and ground-truth soil-moisture measurements. Application of the evaluation approach to the Vienna University of Technology (TU Wien) European Remote Sensing (ERS) scatterometer Θ
s
data set over regional-scale ( ~ 1000
²
km
²
) domains in the Southern Great Plains and southeastern (SE) regions of the U.S. indicate a relative reduction in correlation-based skill of 23% to 30% for Θ
s
retrievals obtained from far-field (θ>50°) ERS observations relative to Θ
s
estimates obtained at θ <; 26°. Such relatively modest sensitivity to θ is consistent with Θ
s
retrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 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 Θ
s
retrieval skill.