Surface Roughness and Spectral Analysis Using Airborne Lidar Digital Elevation Models (DEMs) for Modeling and Calibration/Validation of GNSS-R Land Returns

Surface roughness serves as one of key inputs to traditional scattering models to predict bistatic scattered fields; these model predictions can then be used to support the calibration and/or validation of GNSS-R land observations. Lidar datasets over the San Luis Valley and the vicinity of White Sands were acquired in two airborne campaigns to provide information on surface properties for this application, including non-detrended and detrended root mean square (rms) height, mean slopes, and the variance of slopes over a fixed patch size of 30 meters. A spectral analysis of surface roughness using the power spectral density (PSD) was also conducted. The 2-D PSD shows the different characteristics of ‘flat’ and ‘rough’ surfaces. A normalized 1-D PSD derived by averaging 2-D results over azimuth. A parameterization method is proposed to describe this 1-D spectrum using a power law function with an exponent and a scale length. The parameterized spectrum can be applied in further studies of scattering models and the cal/val of GNSS-R land returns.