# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 5 Conclusions  
We estimated global GPP from 2000 to 2022 based on GPU by integrating the Farquhar model for C3 plants and the Collatz model for C4. The proposed model showed strong performance in simulating both spatial and interannual variations in GPP globally. It explained 72.3% of the spatial variation at all validation sites and showed interannual changes consistent with other models. We assessed the contributions of climate factors to GPP trends and found that these environmental variables exhibit substantial long-term changes and contribute significantly to vegetation production on an interannual scale. Atmospheric CO\({}_{2}\) concentration is an important environmental factor that increases global annual GPP according to the SEM model. The model we developed was able to effectively capture temporal and spatial changes in GPP and provides a reliable alternative for long-term estimates of global GPP. Figure 9: The basic usage of structural equation modeling (SEM) in path analysis with mediation. (**A**) The causal relationships include total effects and their corresponding path coefficients. The acronyms in the models are carbon dioxide (CO\({}_{2}\)), air temperature (Temperature), GPP, and Precipitation. * indicates statistical significance (\(p<0.05\)), and *** indicates extremely significant (\(p<0.001\)). Model fit was assessed using [PERSON]’s test of d separation (Fisher’s C = 0.104, df = 2, \(p=0.95\)). (**B**) Standardized effects of driving factors (CO\({}_{2}\), Temperature, and Precipitation) on GPP. While the Light-Use Efficiency (LUE) model has been widely used to estimate vegetation productivity, it has encountered several limitations, including the failure to account for environmental stressors, inconsistencies in spatial and temporal scales, region-specific parameterization, and the inability to fully capture the dynamic interactions within complex ecosystems [97; 98; 99; 100]. Although vegetation index models have been widely used to monitor vegetation condition and productivity, they are limited by their sensitivity to atmospheric conditions, soil background effects, and saturation in densely vegetated areas, which reduces their accuracy in estimating biophysical parameters [2; 101]. AI models for estimating gross primary production (GPP) often encounter limitations due to their dependence on the quality and quantity of input data, the complexity of ecosystem processes, and difficulties in generalizing across different biomes, leading to uncertainties in model predictions [102; 103]. Ecosystem process-based models for estimating GPP have the advantage of integrating detailed physiological and ecological mechanisms, which enable more accurate predictions of carbon fluxes at different temporal and spatial scales [81; 104; 105]. Our dataset provides a higher temporal resolution than the Blue Carbon, SiB4, BCC-ESM1, and CNRM-CM6-1 datasets; higher spatial accuracy than CARAMOM, BCC-ESM1, and CNRM-CM6-1; and a longer temporal span than BEPS, BESS, Blue Carbon, SiB4, SMAP L4, and CARAMOM. The proposed model generated a global GPP dataset with a resolution of 10 km, but this moderate spatial resolution led to the loss of important fine-scale details, resulting in an oversimplified representation of heterogeneous landscapes and lower accuracy in detecting small-scale ecological processes and land use changes. We developed the model using parallel computing techniques, including GPUs and multithreading, which significantly enhanced the simulation speed. Users applying this model to simulate global GPP are required to set up the development environment and possess proficiency in computer skills, such as C++ programming standards. These technical demands pose considerable challenges for beginners. Furthermore, since the model was designed for a global scale, recalibration of parameters is necessary to improve simulation accuracy when applying it to smaller-scale GPP simulations.