Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

M. M. Rahman, M. S. Moran, D. P. Thoma, R. Bryant, C. D. Holifield Collins, T. Jackson, B. J. Orr, M. Tischler

Research output: Contribution to journalArticlepeer-review

113 Scopus citations


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 unknown, 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 field 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.

Original languageEnglish (US)
Pages (from-to)391-402
Number of pages12
JournalRemote Sensing of Environment
Issue number2
StatePublished - Feb 15 2008


  • Active microwave
  • Integral Equation Model
  • Radar
  • Soil moisture
  • Surface roughness

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


Dive into the research topics of 'Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data'. Together they form a unique fingerprint.

Cite this