This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted.

The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented.

Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{P}}$ ) and (ii) time invariant ( ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{T}}$ ), based on model simulations at the L band. Results show that the ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{T}}$ has a stronger sensitivity to vegetation properties compared with ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{P}}$ . ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{T}}$ is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that ${\mathrm{MVI}}_{\mathrm{B}}^{\mathrm{T}}$ has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission that includes different models for forward and retrieval processes is presented. This OSSE is implemented to study the errors related to the use of simple retrieval models in passive microwave applications. To this end, a theoretical forward model was introduced, which is suitable to reproduce some of the complexities related to canopy vegetation scattering. So far, this OSSE has been successfully exploited to study the artifacts in the retrieved soil moisture associated to: 1) uncertainties and aggregation of the ancillary parameters needed for the retrieval, and 2) instrumental noise effects. In this paper, we attempt to model the influence of this “model asymmetry” (different forward and inverse model) in the estimated soil moisture. These asymmetries are related to the fact that the emissivity of real surfaces is complex and strongly dependent on land cover type and condition. In particular, surface covered by average to dense vegetation presents complex scattering properties, related to canopy structure. Using this theoretical model, the difficulties related to retrieving soil moisture from passive data with a simple model are studied. The accuracy of the soil moisture estimation is analyzed in order to illustrate the impact of discrepancies between both models. In general, retrieved soil moisture performs worse over dense vegetated areas and under wet conditions. Furthermore, accuracy is highly dependent on land cover.