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

Aerodynamic roughness length (z0) is one of those important biophysical parameters that influence energy exchange at the land–atmosphere interface, so it is significant to quantify the z0 accurately. In this article, a scheme parameterizing land-surface z0 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 z0 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 z0 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 z0 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 z0 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 z0 is also low. With the increase of leaf density in leaf-on season, the z0 values will also increase. However, the relationship is complicated in the dense vegetation areas in leaf-on season; the z0 values may or may not increase, but the zero-plane displacement heights will keep increasing continuously. This operational scheme to parameterize z0 based on the vegetation height and canopy area index retrieved from multi-source remote-sensing data can be applied to quantify time serial z0 at regional scale. Besides, it can also improve z0 parameterization in land models or atmospheric models.