Surface and aerodynamic roughness in arid and semiarid areas and their relation to radar backscatter coefficient

Abstract

[1] 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, Z 0 , 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. Z 0 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, Z 0 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, s 0 , to retrieve L c and/or Z 0 . Significant relationships were found between s 0 and both L c and Z 0 . 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 Z 0 using radar observations in the C band from operational sensors.

Aerodynamic roughness height for gravel-mantled megaripples, with implications for wind profiles near TARs on Mars

“…Studies of the averaging interval used for wind profile measurements indicate little loss of precision occurs when going from recording periods of 15 min to less than 1 min (Namikas et al, 2003), in agreement with recent tests that gave consistent results using our equipment for sampling periods ranging from 10 s to 7 min. Longer recording periods (up to several hours) and towers with more anemometers (>5) would certainly improve the accuracy of z 0 obtained from wind velocity profiles (Bauer et al, 1992;Wieringa, 1993;Marticorena et al, 2006), but the short time available at each site in the Puna led to the choice to collect multiple wind profiles at multiple locations at diverse sites rather than collect long time averages for a single tower location at one site (but see Section 4). Due to the length of time required to commute to and from the study sites, all wind measurements were obtained during the afternoon at times when the horizontal winds were strong so that inertial forces should dominate the wind flow, but we do not have the information needed to make a more detailed assessment of atmospheric stability (e.g., Marticorena et al, 2006) at each Puna site.…”

A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

“…Most currently used land surface models require estimates of aerodynamic roughness length to characterize the momentum transfer between surface and atmosphere [2][3][4].…”

Topographic Local Roughness Extraction and Calibration Over Martian Surface by Very High Resolution Stereo Analysis and Multi Sensor Data Fusion

“…As demonstrated in Marticorena et al, (2006) work, if the roughness parameters are converted into the aerodynamic roughness length Zo with the help of wind tunnel experiment, it will be extremely useful to classify planetary surfaces by their origins and provide important clues to simulate the surface process over target planet. Together with it, the detailed modeling of beam broadening effect and multi look parameters with the parameters extracted from the fine resolution data analysis such as sub meter HiRISE stereo intersection points are strongly required.…”

“…For example, the stereo coverage of sub meter HiRISE optical image is only less than 1% of martian surface. Therefore, the direct extraction of height variation by the lateral cover measurement from 3D range data sources as shown in Marticorena et al (2006) is extremely difficult and rarely possible for the purposes in all above topics. Hence, the circuitous approaches to extract the local roughness parameters from the medium resolution remote sensing data were employed.…”