# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Method** & **Pathway** & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Unit** & \
\begin{tabular}{c} **Study** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Coverage** \\ \end{tabular} & **Reference** \\ \hline BEPS & EPM & C3 & day & 0.07\({}^{\circ}\) & gC/m\({}^{2}\) & 1981–2019 & Global & Jv et al. (2021) [25] \\ BESS & EPM & C3 & day & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1982–2019 & Global & Li et al. (2023) [26] \\ EC-LUE & LUE & C3/C4 & 8 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1982–2018 & Global & Zhang et al. (2020) [4] \\ MODIS & LUE & C3/C4 & 8 days & 500 m & kgC/m\({}^{2}\) & 2003–2020 & Global & Running et al. (2021) [27] \\ VPM & LUE & C3/C4 & 8 days & 0.05\({}^{\circ}\) & kgC/m\({}^{2}\) & 2000–2016 & [PERSON] & [PERSON] et al. (2017) [3] \\ Random & ML & C3 & 10 days & 0.10\({}^{\circ}\) & gC/m\({}^{2}\) & 1999–2020 & [PERSON] & Zeng et al. (2020) [28] \\ VODCA-2 GPP & VOD & C3 & 8 days & 0.25\({}^{\circ}\) & gC/m\({}^{2}\) & 1988–2020 & Global & Benjamin et al. (2022) [29] \\ GLO-PEM & LUE & C3/C4 & 8 days & 10 m & gC/m\({}^{2}\) & 1981–2023 & China & [PERSON] et al. (1995) [10] \\ GLASS & LUE & C3 & 8 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1981–2018 & Global & [PERSON] et al. (2021) [30] \\ FluxSat v2.0 & ML & C3 & day & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 2000–2020 & Global & Joiner et al. (2019) [31] \\ SMUF & SIF & C3 & 4 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 2010–2019 & Region & Wu et al. (2021) [32] \\ SBI4 & EPM & C3/C4 & Monthly & 0.50\({}^{\circ}\) & gC/m\({}^{2}\) & 2000–2018 & Global & [PERSON] et al. (2021) [33] \\ SMAP L4 & EPM & C3 & day & 0.09\({}^{\circ}\) & gC/m\({}^{2}\) & 2015–2024 & Global & Kimball et al. (2021) [34] \\ CARDMDMM & EPM & C3 & day & 0.50\({}^{\circ}\) & gC/m\({}^{2}\) & 2001–2016 & USA & [PERSON] et al. (2021) [35] \\ NIRv-Index & SIF & C3 & day & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1982–2018 & Global & Wang et al. (2020) [36] \\ PML-V2 & LUE & C3/C4 & Monthly & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1982–2014 & Global & Zhang et al. (2020) [37] \\ BCC-ESM1 & EPM & C3/C4 & 8 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 2002–2019 & Global & Chen et al. (2019) [38] \\ CNRM- & EPM & C3/C4 & Monthly & 2.81\({}^{\circ}\) & gC/m\({}^{2}\) & 1850–2014 & Global & Wu et al. (2020) [39] \\ CMe-1 & EPM & C3/C4 & Monthly & 1.41\({}^{\circ}\) & gC/m\({}^{2}\) & 1850–2014 & Global & Program et al. (2019) [40] \\ Neural & ML & C3 & 4 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 2000–2022 & Global & Zhang et al. (2018) [41] \\ MuSyQ & LUE & C3 & 8 days & 0.05\({}^{\circ}\) & gC/m\({}^{2}\) & 1981–2018 & [PERSON] & [PERSON] et al. (2021) [42] \\ Blue Carbon & EPM & C3 & 16 days & 250 m & gC/m\({}^{2}\) & 2000–2019 & USA & [PERSON] et al. (2020) [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GPP product information. EPM: ecological process model; LUE: light-use efficiency; ML: machine learning; VOD: vegetation optical depth; SIF: solar-induced fluorescence; Region: country/continent; Pathway: photosynthetic pathway.