Improving gross primary production estimation accuracy on the Qinghai-Tibet Plateau considering the effect of atmospheric CO2 fertilization

Involving the effect of atmospheric CO2 fertilization is effective for improving gross primary production (GPP) estimation accuracy using light use efficiency (LUE) model. However, the widely used LUE model, the remote sensing-driven Carnegie-Ames-Stanford Approach (CASA) model, is scarcely considering the effects of atmospheric CO2 fertilization, which cause GPP estimation uncertainties. Therefore, this study proposed an improved GPP estimation method based on CASA model integrating the atmospheric CO2 concentration and generated a long time series GPP dataset with high precision for the Tibetan Plateau. The CASA model was improved by considering the atmospheric CO2 effect on vegetation productivity and distinguishing the CO2 gradients differences within the canopy and leaves brought by the influence of leaf stomatal conductance and leaf saprophyte activity. A 500m monthly GPP dataset for the Tibetan Plateau from 2003 to 2020 were generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the CASA model (R2 = 0.67, RMSE = 499.32 g C/m2/year), and MODIS GPP products. The GPP on Qinghai-Tibet Plateau increased significantly with the increase of atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved GPP estimation method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on Qinghai-Tibet Plateau.