|Rahman, M. - UNIVERSITY OF ARIZONA|
|Thoma, D. - UNIVERSITY OF ARIZONA|
|Bryant, R. - UNIVERSITY OF ARIZONA|
|HOLIFIELD COLLINS, CHANDRA|
|Orr, B. - UNIVERSITY OF ARIZONA|
|Tischler, M. - US ARMY|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 30, 2006
Publication Date: January 14, 2008
Citation: Rahman, M.M., Moran, M.S., Thoma, D.P., Bryant, R., Holifield Collins, C.D., Jackson, T.J., Orr, B.J., Tischler, M. 2008. Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data. Remote Sensing of Environment. 112:391-402. Interpretive Summary: Information about the distribution of surface soil moisture is important for management of agriculture and natural resources. Theoretically, soil moisture information can be obtained from a satellite image of radar backscatter in combination with a backscatter simulation model. In practice, this approach is untenable because the model requires information about surface roughness, which is rarely known. This study proposes a new way to parameterize the backscatter model by using two radar images acquired at two incident angles. The surface roughness is determined from the multi-angle radar, and consequently, the radar model can be used to determine surface soil moisture from imagery acquired at any time. When tested for a semiarid watershed over a one-year period, this approach derived regional soil moisture estimates that compared well with ground-based measurements. Also, the implementation of this method is rather straight-forward and can be applied operationally at locations where field data are not available. This could be a feasible and economic approach for mapping surface soil moisture over large, inaccessible regions for such important applications as flood prediction and drought assessment.
Technical Abstract: The Integral Equation Model (IEM) is the most widely-used, physically based radar backscatter model for sparsely vegetated landscapes. In general, IEM quantifies the magnitude of backscattering as a function of moisture content and surface roughness, which are known, and the known radar configurations. Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models. This study offers a solution strategy to this problem by the use of multi-angle radar images and thus provides estimates of roughness and soil moisture without the use of ancillary defiled data. Results showed that radar images can provide estimates of surface soil moisture at the watershed scale with good accuracy. Results at the field scale were less accurate, likely due to the influence of image speckle. Results also showed that subsurface roughness caused by rock fragments in the study sites caused error in conventional applications of IEM based on field measurements, but was minimized by using the multi-angle approach.