*2.5. Experiment Configuration and Validation*

The original MOD17A2H GPP products used the GMAO Reanalysis data as the driving metrological database, and calculated the GPP with the biome based parameters look up table on a global scale. To validate and improve the performances of the MODIS GPP estimations and quantify the uncertainty of the MODIS GPP simulation algorithm (MOD17 model), we replaced the satellite-derived and meteorological inputs in the MOD17 model and compared the modeled GPP estimates with flux tower observations with the following experiment configurations: (1) We firstly assessed the performance of original MOD 17A2H GPP product at spatial resolution of 500m with the tower based GPP. The results of the model validation, in this study, is called GPP\_MODIS; (2) we used in situ meteorological data to run the MOD17 algorithm to understand the influence of meteorological inputs (i.e., incoming solar radiation, minimum temperature and vapor pressure deficit) on GPP modelling rather than the GMAO Reanalysis dataset, we called this GPP\_Insitu; and (3) we compared the performances between the calibration of one parameter only (εmax) and calibration of all parameters of the MOD17 model to examine the sensitivity of the water and temperature-limited parameters on GPP estimation. The results are called GPP\_LUEopt and GPP\_Fiveopt, respectively. To understand the effects of parameter uncertainty on GPP simulation, we compared the calibrated MOD17 model algorithm with in situ meteorological inputs from the flux tower network. Similar to GPP\_Insitu, GPP\_LUEopt, and GPP\_Fiveopt were also calculated using the in situ meteorology data. However, for GPP\_LUEopt, we only optimized the parameter of εmax by Bayesian approach and other parameters used the default BPLUT parameters in the MOD17 algorithm. Whereas, for GPP\_Fiveopt we optimized all the five parameters using the Bayesian approach.
