Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland

The aerodynamic roughness length (Z_0 m) serves an important role in the flux exchange between the land surface and atmosphere. In this study, airborne lidar (ALS), terrestrial lidar (TLS), and imaging spectroscopy data were integrated to develop and test two approaches to estimate Z_0 m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Z_0 m was analyzed. The comparison of eddy covariance-derived Z_0 m and remote sensing-derived Z_0 m showed that the accuracy of the estimated Z_0 m heavily depends on the estimation model and the representation of shrub (e.g., Artemisia tridentata subsp. wyomingensis) height in the models. The geometrical method (RA1994) led to 9 percent (~0.5 cm) and 25% (~1.1 cm) errors at site 1 and site 2, respectively, which performed better than the height variability-based method (MR1994) with bias error of 20 percent and 48 percent at site 1 and site 2, respectively. The RA1994 model resulted in a larger range of Z_0 m than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z_0 m estimates in the MR1994 model, while the mean, median, and MLD (Median Absolute Deviation from Median Height), as well as AAD (Mean Absolute Deviation from Mean Height) heights from ALS provides the best Z_0 m estimates in the RA1994 model. In addition, the fractional cover of shrub and grass, distinguished with ALS and imaging spectroscopy data, provided the opportunity to estimate the frontal area index at the pixel-level to assess the influence of grass and shrub on Z_0m estimates in the RA1994 method. Results indicate that grass had little effect on Z_0 m in the RA1994 method. The Z_0 m estimations were tightly coupled with vegetation height and its local variance for the shrubs. Overall, the results demonstrate that the use of height and fractional cover from remote sensing data are promising for estimating Z_0 m, and thus refining land surface models at regional scales in semiarid shrublands.