Accurate estimation of vegetation biophysical variables such as the vegetation canopy height ( ) is of great importance to the applications of the land surface models. It is difficult to obtain the data of at the regional scale or larger scale, but the remote sensing provides the most useful and most effective method. The leaf area index (LAI) is closely related to the , and we analyzed its relationship with the correlation analysis based on the dataset at 86 site-years of field measurements from sites worldwide in this study. The result indicates that there is significant positive exponent correlation between these two parameters and the change of LAI would exert great impacts on . The higher the LAI is, the higher the is, and vice versa. Besides, the coefficients of different land cover types are very heterogeneous, and LAI of the needleleaf forest shows strong correlation with , while that of the cropland shows weak correlation with . The results may provide certain reference information for the extraction of the data of at the regional scale with the remote sensing data. 1. Introduction The vegetation plays an important role in the transfer of heat, momentum, and substance in the Earth system [1], and the accurate estimation of vegetation biophysical variables is of great importance to the agricultural, ecological, and meteorological applications [2]. The structural factors of vegetation, such as the leaf area index (LAI) and vegetation canopy height ( ), have direct influence on the surface albedo, surface roughness, surface temperature, surface moisture, and so forth, all of which are important input parameters of the models such as land surface models (LSM), regional climate model (RCM), and global climate model (GCM) [3]. Therefore, the data accuracy of the vegetation density and height may have great impacts on the uncertainties in the simulation results with these models. Both LAI and are important vegetation physiological parameters that have close relationship with the ecological, hydrological, and climatic models. is an important ecological metric that can provide essential information to scientists interested in understanding or modeling a wide range of atmospheric, hydrological, biophysical, and ecological processes in the forest and shrubland [4], while LAI is also one of the most important vegetation parameters and land property indices that serve as a primary controlling factor of the exchange of energy, water, and carbon fluxes between the terrestrial ecosystems and the atmosphere [5–8]. As the primary attribute of the vertical
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