# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
2 Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 2  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China; [EMAIL_ADDRESS] (T.C. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (T.C. ); [EMAIL_ADDRESS] (J.W.) 2 Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 2  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China; [EMAIL_ADDRESS] (T.Y. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (T.C. ); [EMAIL_ADDRESS] (J.W.) 2 Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 2  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China; [EMAIL_ADDRESS] (T.Y. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (G.L. ); [EMAIL_ADDRESS] (T.C. ); [EMAIL_ADDRESS] (J.W.) 2 Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 2  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China; [EMAIL_ADDRESS] (T.Yand FPAR products generated from individual satellite data have been demonstrated to be spatially and temporally discontinuous on regional and global scales, and are less representative in some vegetation types [5; 6; 7; 8]. Thus, to generate more accurate and consistent GPP and NPP products, more consistent and higher quality LAI and FPAR datasets should be used. Global LAnd Surface Satellite (GLASS) LAI products were developed based on time-series MODIS and Advanced Very High Resolution Radiometer reflectance data using General Regression Neural Networks (GRNNs) trained with fused time-series LAI from MODIS and Carbon CYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) [9; 10; 11]. Studies [9; 10; 12] have shown that the GLASS LAI product achieves a higher precision than MODIS LAI and is more temporally continuous and spatially complete with smoother trajectories. Moreover, [PERSON] et al. [13] developed the GLASS FPAR product with continuous trajectories and high accuracy based on the GLASS LAI product. Therefore, more continuous results with higher precision may be obtained when estimating global vegetation productivity using GLASS LAI and FPAR products. For the estimation of vegetation productivity, many models have been developed on both regional and global scales. Climate productivity models, such as the Miami model, Thornthwaite Memorial model [14], and Chikugo model [15], are based on the relationships between GPP/NPP and climate factors. LUE models have great potential to address the spatial and temporal dynamics of GPP/NPP [16], such as the Carnegie, Ames, Stanford Approach (CASA) [17] and the GLObal Production Efficiency Model (GLO-PEM) [18]. The basis of eco-physiological process models is ecological and biophysical process theories, such as the CENTURY [19], Terrestrial Ecosystem Model (TEM) [20], and Biome Bio-Geochemical Cycle (Biome-BGC) model [21].