###### Abstract  
Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 24 widely used GPP models and the quality of major spatial data layers that drive the models. Results show that global GPP products generated by the 24 models varied greatly in means (from 92.7 to 178.9 Pg C yr\({}^{-1}\)) and trends (from \(-\)0.25 to 0.84 Pg C yr\({}^{-1}\)). Model structure differences (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation at the flux tower locations was explained by the spatial dataset), which not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Moving forward, research into the efficacy of model structures and precision of input data may be more important for global GPP estimation. gross primary production; eddy covariance; constraint model precision; remote sensing products biases 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 2, No. 1, pp. 1-10. 1-2  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields  
[PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; et al. Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields  
[PERSON]; [PERSON]; [PERSON]; [PERSON] of GPP estimation and advance carbon cycle science, it is crucial to consider: (1) The impacts of model structure, (2) the determination of parameter values, and (3) the quality of data feeding in GPP models [5; 6]. Model structure is considered one of the most important factors that affect model performance [7; 8]. Yet, large structural differences can be observed among GPP models. For example, the fraction of photosynthetic active radiation (FPAR), an important parameter in the light use efficiency (LUE) models, has been treated in disparate ways, either approximated by the enhanced vegetation index (EVI) in the vegetation photosynthesis model (VPM) [9], as a linear function of normalized difference vegetation index (NDVI) in the eddy covariance-light use efficiency (EC-LUE) model [10], or as a nonlinear function of leaf area index (LAI) according to Beer's Law, among others [11]. A similar situation exists for representing temperature stress (TS), water stress (WS), and their interactions among models. The moderate resolution imaging spectroradiometer (MODIS) model and the VPM model adopt a multiplicative structure to represent the collective influences of WS and TS on GPP [7; 12]. The EC-LUE model, on the other hand, considers that the Liebig's law is ecologically more reasonable in representing the effects of WS and TS [9]. The estimation of model parameters often affect the simulation accuracy of the model, thus rigorous model parameterization and calibration should be adopted in GPP modeling [13].