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This article studies the behavior of the backscattering coefficient of a sparse forest canopy composed of relatively short black spruce trees. Qualitative analysis of the multiangular data measured by the RADARSAT synthetic aperture radar (SAR) sensor shows a good agreement with surface and vegetation volume scattering fundamental behaviors. For a quantitative analysis, allometric equations and measurements of tree components collected within the framework of the Extended Collaboration to Link Ecophysiology and Forest Productivity (ECOLEAP) project are used, in an existing multilayer radiative transfer model for forest canopies, to simulate the RADARSAT SAR data. In our approach, the fractional cover of trees estimated from aerial photographs is used as a weighting parameter to adapt the closed-canopy backscattering model to the sparse forest under study. Our objective is to analyze the sensitivity of the backscattering coefficient as a function of sensor configuration, soil wetness, forest cover, and forest structural properties in order to determine the suitable soil, vegetation, and sensor parameters for a given thematic application. For the entire incidence angle domain (20[degrees] to 50[degrees]) of the sensor, simulations show that over a sparse forest composed of mature trees the monitoring of the ground surface is possible only under very wet soil conditions. Therefore, this article informs about the ability of the RADARSAT SAR sensor in monitoring wetlands. In addition, it is shown that the discrepancies between the simulation results and the RADARSAT SAR measurements are mainly related to the natural variability of soil and vegetation characteristics over the sites of study. Index Terms--Allometric equations, Black spruce trees, model simulations, RADARSAT synthetic aperture radar (SAR) data, sparse forests.

RADARSAT synthetic aperture radar (SAR) data acquired at C Band, HH polarization, and for the 20 [degrees]-27 [degrees] and 45 [degrees]-49 [degrees] incidence angle ranges were available over northern Quebec, Canada, (54 [degrees] N, 72 [degrees] 12'W), in the fall of 1996, the winter of 1997, and the spring of 1997. The main land occupation of this area is sparse black spruce (Picea mariana) forests. Vegetation characteristics are jointly used with snow and soil observations coinciding with the satellite overpasses to simulate the seasonal changes in the backscattering coefficient of the subarctic forest. The aim of this study is twofold. First to evaluate the effects of the seasonal changes in vegetation on the RADARSAT SAR data, and second to use backscattering models as a tool for a better interpretation and understanding of the RADARSAT SAR data over snow-covered forested areas. Simulations show the importance of the surface-vegetation interaction term and the wet snow surface roughness on the discrimination between open forest and denser forest, and on the contrast between wet snow and dry snow covers. When comparing the simulations to the RADARSAT SAR data, the poorest results are obtained in the spring for a rough wet snow. It is shown that they are mainly due to a crude evaluation of the vegetation dielectric constant rather than to uncertainties introduced by the spatial variability in the wet snow surface roughness. Index Terms--Dry snow, RADARSAT (synthetic aperture radar (SAR) data, seasonal changes, subarctic forest, surface-vegetation interaction term, wet snow, wet snow surface roughness.

A study was conducted to assess the potential of C-band synthetic apertuer radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE < 20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0 [degrees] C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen. A follow-up of this study would be to test if the SWE-R ratio relationship could be generalized. Preliminary results show that a similar relationship can also be obtained with ERS-1 data on another test area. Index Terms - C-band, dry snowpack, synthetic aperture radar (SAR).