Surface roughness is a key parameter for surface‐atmosphere exchanges of mass and energy. Only a few field measurements have been performed in arid or semiarid areas where it is an important control of the aeolian erosion threshold. An intensive field campaign was performed in southern Tunisia to measure the lateral cover, L_c, and the aerodynamic roughness length, Z₀, over 10 sites with different surface roughnesses. L_c was determined by combining field measurements of the geometry of the roughness elements and simple assumptions on their shapes. Z₀ was experimentally determined from high‐precision wind velocity and air temperature profiles. The resulting data were found to be in good agreement with the existing relationships linking the geometric and the aerodynamic roughness. This suggests that for natural surfaces, Z₀ can be estimated on the basis of the geometric characteristics of the roughness elements. This data set was then used to investigate the capabilities of radar backscatter coefficients, σ₀, to retrieve L_c and/or Z₀. Significant relationships were found between σ₀ and both L_c and Z₀. The SAR/ERS data set is in agreement with the SIR‐C SLR data set from Greeley et al. (1997). On the basis of these two data sets including data from different arid and semiarid areas (North Africa, South Africa, North America), we propose an empirical relationship to retrieve Z₀ using radar observations in the C band from operational sensors.

More and more remote sensing data corresponding to various wavelength domains is becoming available. Visible/infrared data were first used for land cover classification. However, radar data are becoming more widely used for hydrological and agricultural applications. This paper discusses the performance, for land cover type discrimination, of an optical image acquisition and a multitemporal radar series. For the majority of land cover types existing within the test site (representative of northern European agricultural areas), both ERS multitemporal SAR and Landsat multispectral visible/infrared classifications lead to good results, with the latter being more robust. For better identification of cultures that are less represented, the complementarity of the two datasets may be exploited using an efficient data fusion algorithm based on the Dempster-Shafer evidence theory. The performance of this combination was verified on two successive vegetation cycles.

In this paper, a simple model is proposed for measuring the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In such regions, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations over Tunisian test areas, where ground parameters are controlled, are performed and compared with actual ERS2 radar measurements. A very good agreement is found. The model is then used to derive a map of the vegetation cover density for the whole studied site (in Tunisia). The approach used here is based upon supervised classification with classes defined by inverting the model and taking into account ERS calibration error. Each of the four classes thus defined exhibits a good classification rate, greater than 85%. Finally, two important applications for natural resources management are presented: vegetation monitoring and soil moisture monitoring.