A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

Abstract

Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

Changes in the Shadow: The Shifting Role of Shaded Leaves in Global Carbon and Water Cycles Under Climate Change

“…With the GLOBMAP LAI (V3), the BEPS-simulated global GPP increases from 119.1 Pg C/year in 1982 to 133.2 Pg C/year in 2016 with an average of 124.4 ± 4.3 Pg C/year and a trend of 0.38 Pg C/year (p < 0.01;Figure 1); these estimates are close to recent report with a trend of 0.39Pg C/year (2000Pg C/year ( -2016 and a global GPP of 129.4 Pg C/year in 2016(Zhang, Xiao, Wu, et al, 2017). Comparing to the simulation using GLOBMAP LAI V2, the annual GPP estimates are consistently larger by 3 Pg C/year in 1982-2004, and since 2005, there is a significant growth in GPP driven by the LAI V3 with MODIS sensor degradation corrected (Figure S3).…”

A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

“…This trend is close to the GPP trend derived from the satellite-data driven vegetation photosynthesis model (VPM) (0.32% year -1 ) (Zhang et al, 2017a), but much greater than GPP derived from other remote sensing data-driven models (FluxCOM 0.01% year -1 10 (Tramontana et al, 2016), BESS GPP 0.22% year -1 (Jiang and Ryu, 2016), MODIS C6 0.26% year -1 (Zhao et al, 2005), and WECANN -0.8% year -1 [affected by the decreasing GOME-2 SIF trend (Zhang et al, 2018b)] (Alemohammad et al, 2017)). …”

“…During this process, we used a gap-filling and smoothing algorithm to reconstruct the surface reflectance for the seven bands. The detailed description of the gap-filling algorithm can be found in Zhang et al (2017a). In this study, we slightly modified the algorithm by not applying the Best Index Slope Extraction (BISE) algorithm and Savitzky-Golay (SG) filter.…”

Terrestrial CO2 Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

“…Source and sinks of CO 2 at regional to continental scales remain poorly understood. Even though multiple GPP products on daily scales (usually 8‐day) are available, their performances vary substantially when validated against EC observations (Zhang et al, 2017b). This is at least partly due to our limited understanding on the photosynthesis and respiration processes, including their diurnal variations.…”

“…EVI is responsive to canopy structure variations, including leaf area index, canopy type, plant physiognomy, and canopy architecture (Gao et al, 2000). In this study, EVI was calculated from the MOD09A1 C6 500 m 8‐day land surface reflectance data set, retrieved from the MODIS satellite (Huete et al, 2002; Zhang et al, 2017b). Figure 1c shows the distribution of summertime averaged EVI in Northeast China in 2016.…”