Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Abstract

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05° latitude by 0.05° longitude at 8-day interval by revising a light use efficiency model (i.e. EC-LUE). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 84 towers from the FLUXNET2015 dataset, covering nine major ecosystem types of the globe, were used to calibrate and validate the model. The revised EC-LUE model could explain 83 % and 68 % of the spatial variations in the annual GPP at 42 calibration and 43 validation sites, respectively. In particular, the revised EC-LUE model could very well reproduce (~ 74 % sites R2 > 0.5; averaged R2 = 0.65) the interannual variations in GPP at 51 sites with observations greater than 5-years. At global scale, sensitivity analysis indicated that the long-term changes of environmental variables could be well reflected in the global GPP dataset. The CO2 fertilization effect on the global GPP (0.14 ± 0.001 Pg C yr−1) could be offset by the increased VPD (−0.16 ± 0.02 Pg C yr−1). The global GPP derived from different datasets exist substantial uncertainty in magnitude and interannual variations. The magnitude of global summed GPP simulated by the revised EC-LUE model was comparable to other global models. While the revised EC-LUE model has a unique superiority in simulating the interannual variations in GPP at both site level and global scales. The revised EC-LUE model provides a reliable long-term estimate of global GPP because of integrating the important environmental variables. The dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v1 (Zheng et al., 2019).

The Global Land Surface Satellite (GLASS) Product Suite

“…The EC-LUE model has been validated throughout North America, Europe and East Asia using measurements from eddy covariance towers (Li et al 2013;Yuan et al 2014;Yuan et al 2010;Yuan et al 2007 (Jia et al 2018c). Evaluation at 85 eddy covariance towers, distributed globally, demonstrated that the GLASS GPP product is able to represent inter-annual variations and long-term trends because it integrates important environmental variables (Zheng et al 2019).…”

Response of Gross Primary Productivity to Drought Time‐Scales Across China

“…Remote sensing GPP products are usually developed based on the light use efficiency (LUE) model (Monteith, 1972), which is much simpler than process-based models due to only a few parameters are used. Many global remote sensing GPP products are currently available and have been widely used at different spatial scales (Zheng et al, 2020). While, they may insufficiently reflect the effects of soil moisture on photosynthesis and thus poorly reproduce the interannual variations in GPP (Stocker et al, 2019).…”

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

“…The GLASS data are available online at http://glass-product.bnu.edu.cn/ (accessed on 1 January 2021). They have been extensively validated and are in agreement with FLUXNET observations and also been proven to be a reliable long-term estimate for global GPP [65,66].…”