###### Abstract  
Involving the effect of atmospheric CO\({}_{2}\) fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie-Ames-Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO\({}_{2}\) fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO\({}_{2}\) concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai-Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO\({}_{2}\) on vegetation productivity and discerning variations in CO\({}_{2}\) gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai-Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R\({}^{2}\) = 0.68, RMSE = 406 g C/m\({}^{2}\)/year) than the original model (R\({}^{2}\) = 0.67, RMSE = 499.32 g C/m\({}^{2}\)/year) and MODIS GPP products (R\({}^{2}\) = 0.49, RMSE = 522.56 g C/m\({}^{2}\)/year). The GPP on the Qinghai-Tibet Plateau increased significantly with the increase in atmospheric CO\({}_{2}\) concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai-Tibet Plateau. G 2024 16 1856 111856 111856 11 April 2024 16 11 2024 12 May 2 Some researchers have combined flux observation data from ground-based measurements with statistical methods to analyze the seasonal variations in GPP and its reaction to climate fluctuations [3], while there is a lack of spatio-temporal information in these analyses [4]. Leveraging remote sensing technology, many models have been devised, enabling the spatio-temporal quantification of GPP over large areas. These models are primarily categorized into four groups [5]: (1) statistical models--estimation by constructing empirical relationships with various vegetation indices; (2) process-based models--quantifying carbon exchange by simulating key processes in vegetation during growth; (3) LUE models--estimation of biological processes based on vegetation photosynthesis; (4) machine learning models--estimation by constructing complex nonlinear relationships between various indicators and GPP from training datasets. Among these, LUE models have gained widespread usage due to their theoretical robustness and practical convenience, and the CASA model is a typical representative that has been widely used [6]. Past studies have unveiled that the fluctuations in atmospheric CO\({}_{2}\) concentration over the last two decades have affected various aspects of plant physiology which have been associated with the promotion of plant root and seedling growth, increased leaf thickness, reduced transpiration rates, and enhanced water utilization efficiency. Thus, these favorable effects have facilitated greater organic matter synthesis in plants, leading to a consequential stimulation of vegetation growth. Moreover, seasonal variations in atmospheric CO\({}_{2}\) concentration are predominantly driven by vegetation growth, and the seasonality of GPP shows clear consistency with atmospheric CO\({}_{2}\) concentration [7]. Researchers have indicated that atmospheric CO\({}_{2}\) affects carbon uptake during vegetation photosynthesis by influencing the photosynthetic enzyme system, the amount of photosynthetically active radiation (PAR) captured by leaves, and the ability to export and convert photosynthetic products [4]. The fertilization effect of atmospheric CO\({}_{2}\) was defined in the C-fix model as the carbon assimilation when the CO\({}_{2}\) was higher than the baseline, and the carbon mass fluxes over Europe were estimated using this principle [2].