PS 40-106 - Mapping global gross primary productivity on the Google Earth Engine platform: developing and applying an improved process-based ecosystem model

Abstract: Terrestrial gross primary productivity (GPP), the largest component flux of the global carbon cycle, plays a crucial role in connecting the global carbon and water cycles as well as the energy balance between the atmosphere, biosphere, hydrosphere and pedosphere. Despite the development of various approaches and models for estimating terrestrial GPP at different scales, significant discrepancies and uncertainties remain in long-term global GPP simulations. Therefore, it is of great value and necessity to better understand and accurately estimate the spatial and temporal patterns of terrestrial GPP. In this study, we have developed a comprehensive process-based ecosystem model, driven by new reanalysis climate data and satellite-based leaf area index (LAI) data, to estimate global GPP at 0.25° spatial resolution and 3-hour temporal intervals from 2001 to 2020 on the Google Earth Engine (GEE) platform. We have improved the performance of global terrestrial GPP simulation by 1) reconstructing satellite-based LAI data using double logistic function to reduce biases and errors caused by cloud contamination and composite technique; 2) integrating a two-stream two-leaf solar radiation transfer model (RTM) within a canopy that considers multiple scattering and radiation partitioning; and 3) leveraging the high-performance computing capabilities of the GEE platform. Our integrated RTM has been shown to reduce the root mean square error (RMSE) and bias by up to 72% and 81% compared to the Beer’s law model, and up to 63% and 75% based on the two-stream big-leaf model, respectively. Our model exhibits significant improvements and robust performance in estimating GPP, especially in areas with dense vegetation cover. The annual global terrestrial GPP simulated by our model ranged from 118 PgC to 134 PgC, with an average of 128 PgC, during the period from 2001 to 2020, and showed a significantly increasing trend with an average rate of 0.71 PgC/yr globally. Additionally, our sensitivity analysis demonstrated that the simulated GPP was most sensitive to the biophysiological parameters Vcmax25 and LAI, highlighting the need for accurate biophysiological parameters at large scales.