# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China; [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (R.S.) 1 State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China; [EMAIL_ADDRESS] (T.  
relationship based approaches is that the resulting formulas are influenced by vegetation type and soil background. SMA methods estimate FCover at a sub-pixel level based on endmembers from remote sensing data [1; 13; 14]. These methods assume that the pixel composite spectral responses are the linear sum of the reflectance measurements of the individual endmembers, weighted by their relative proportions [15]. The endmembers can be either developed from laboratory or field spectra or derived directly from remote sensing data. These SMA methods, especially the pixel dimidiate model, were widely used to estimate FCover and achieved good results at the regional scale [16]. However, it is difficult to determine the endmembers and the spectral of the endmembers at the global scale for FCover estimation because the land surface is complex and the spectral characteristics of objects are varied. Physical methods are based on the inversion of canopy radiative transfer models describing the transfer of solar radiation in vegetation canopies. Since the physical methods can be adjusted for a wide range of situations [17], radiative transfer models are increasingly used in the inverse mode to estimate FCover from remotely-sensed data. However, inversion techniques based on iterative minimization of a cost function require hundreds of runs of the canopy radiative transfer model for each pixel; therefore, they are computationally too demanding [17]. For operational applications, artificial neural networks (ANNs) are popular inversion techniques that are based on a pre-computed reflectance database [18]. Nevertheless, currently available empirical, SMA, and physical methods generally use only single-phase remote-sensing data to retrieve FCover values. A consequence of using limited information during the inversion process is that the FCover products generated by these methods are spatially incomplete and temporally discontinuous, and also insufficiently accurate to meet the requirements of various applications [19]. [PERSON] _et al._ evaluated the performance of the GEOV1 FCover product and demonstrated that the GEOV1 FCover product presented the higher percentage of missing values at high latitudes in the northern hemisphere, with a wide variability as a function of the period of the year, mainly due to snow coverage changes along the year as well as increase in observations under dark conditions, particularly above the polar circle in winter, and the equatorial region also presents a large fraction of gaps (up to 50%) as a consequence of the higher cloudiness [19]. The RMSE of the GEOV1 FCover product is around 0.1. It is still unable to meet the threshold accuracy requirements (\(\pm\)0.05). The low accuracy and poor quality, in many cases, among existing FCover products require improvements or new products. Furthermore, currently available inversion methods are generally parameter-specific algorithms to separately retrieve land surface parameters from satellite observations, which results in a lack of physical consistency between current land surface parameter products [20; 21]. To improve the quality and accuracy of satellite products, the Global LAnd Surface Satellite (GLASS) product suite was developed, and the phase I products include leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, downwelling shortwave radiation, and photosynthetically active radiation (PAR) [22; 23].