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

June 1, 2017

The aerodynamic roughness length (Z0 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 Z0 m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Z0 m was analyzed. The comparison of eddy covariance-derived Z0 m and remote sensing-derived Z0 m showed that the accuracy of the estimated Z0 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 Z0 m than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z0 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 Z0 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 Z0m estimates in the RA1994 method. Results indicate that grass had little effect on Z0 m in the RA1994 method. The Z0 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 Z0 m, and thus refining land surface models at regional scales in semiarid shrublands.

Citation Information

Publication Year 2017
Title Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland
DOI 10.14358/PERS.83.6.415
Authors Aihua Li, Wenguang Zhao, Jessica J Mitchell, Nancy F. Glenn, Matthew J. Germino, Joel B. Sankey, Richard M. Allen
Publication Type Article
Publication Subtype Journal Article
Series Title Photogrammetric Engineering and Remote Sensing
Index ID 70189872
Record Source USGS Publications Warehouse
USGS Organization Forest and Rangeland Ecosystem Science Center