BACKSCATTERING BEHAVIOR AND SIMULATION COMPARISON OVER BARE SOILS USING SIR-C X-SAR AND ERASME-1994 DATA OVER ORGEVAL/

During April 1994, the three-frequency radar system flew on the Space Shuttle Endeavour, known as SIR-C/X-SAR mission (Shuttle Imaging Radar C/X-Synthetic Aperture Radar). Over the Orgeval watershed (France), t he ground condition stayed very wet throughout the 5-day SAR mission. The SAR imagery allows a data collection over a range of roughness con ditions on bare soils. Three classes were identified: very smooth sowi ngs with crusted top layer, cloddy surfaces, and different ploughed fi elds for future crops. To complement the Shuttle Radar data (three fre quencies, L, C, X, incidence range from 44 degrees to 57 degrees), the helicopter-borne scatterometer ERASME (C- and X-bands, copolarized co nfigurations) were used. Merging of the two databases was possible. As a result, incidence angles ranging from 25 degrees to 50 degrees are available in C- and X-bands for the copolarized cross sections. Then t he major objective of the article is, over this available surface back scattering models to natural surfaces, the theoretical integral equati on model (IEM) of Fung et al. (1992) and the empirical model of Oh et al. (1994). It shows adequacies and limits. The IEM model reproduces w ell radar scatter over smooth surfaces, but fails over rough surfaces, predicting a flatter response with incidence angle than the observed signals in C- and X-bands. Difference in backscatter response due to d irection angles (perpendicular and parallel to the row direction) is d ifficult to reproduce over smooth surfaces by this model integrating a nisotropic surface but may be due to the unadequacy of the surface rep resentation. The Oh algorithm agrees well with the backscatter respons e over rough surfaces at medium incidence angle, but fails with a syst ematic underestimation over smooth conditions. As a conclusion, furthe r developments are necessary on derivation of theoretical solutions ov er rough surfaces and on validation of semiempirical algorithms over d ata sets of various training sources (radar and natural conditions). ( C)Elsevier Science Inc., 1997.