Parameterizing the aerodynamic roughness length on a regional scale based on multi-source remote-sensing data

Aerodynamic roughness length (z ₀) is one of those important biophysical parameters that influence energy exchange at the land–atmosphere interface, so it is significant to quantify the z ₀ accurately. In this article, a scheme parameterizing land-surface z ₀ at regional scale has been approached based on multi-resource remote-sensing data, including lidar and optical remote sensing. First, we retrieved the regional vegetation height from lidar data of Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud, and land Elevation Satellite (ICESat), and then the z ₀ values of vegetated land surface were calculated using height data and canopy area index retrieved from remote-sensing data. Finally, the wall-to-wall map of z ₀ in January and July 2008 were developed. The conclusions are as follows. (1) The vertical and horizontal structures of vegetation can be retrieved combining spaceborne lidar data and other optical remote-sensing data, so the vegetation characteristics and their intra-annual diversification of different land surfaces can be presented dynamically. The variation of z ₀ with vegetation phenology can be quantified by modelling with vegetation height and multi-temporal leaf area index from multi-resource remote-sensing data. (2) The z ₀ values of vegetated surface change significantly during leaf-on or leaf-off period in the year, but there are different features in the sparsely or densely vegetated surface. In the sparse vegetation areas, due to the relatively low leaf density in leaf-off season, the value of z ₀ is also low. With the increase of leaf density in leaf-on season, the z ₀ values will also increase. However, the relationship is complicated in the dense vegetation areas in leaf-on season; the z ₀ values may or may not increase, but the zero-plane displacement heights will keep increasing continuously. This operational scheme to parameterize z ₀ based on the vegetation height and canopy area index retrieved from multi-source remote-sensing data can be applied to quantify time serial z ₀ at regional scale. Besides, it can also improve z ₀ parameterization in land models or atmospheric models.