## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Models Type** & **Model(s)** & & **Fording Data** & **Method Used** & **Spatial** & **Data** & **References** \\ \hline  & & & & Land modelling & & & \\  & & & & (Trend)-\(\approx\)8 & 2.8125\({}^{\circ}\) \(\times\) 2.8125\({}^{\circ}\) & & \\ Process-based models & & - & CO\({}_{2}\) concentration & & & 1.875\({}^{\circ}\) \(\times\) 1.875\({}^{\circ}\) & & \\ Process-based models & & - & Climate data (CRU, CRU-JRA55) & with 80.51, S1, S2, and S3 & 1.25\({}^{\circ}\)\(\times\) 1.875\({}^{\circ}\) & 1959–2020 & [PERSON] et al. [45] \\  & & - & Land-use change. & simulations and & 1\({}^{\circ}\)\(\times\) 1.5 & & [PERSON] et al. [25] \\  & & & additional trendy & 0.9375\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\) & & \\  & & & & simulations & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & & \\ \hline  & & - & FLUNNET eddy covariance dataset & Upscaling the EC data & & & \\  & & - & Meteorological dataset (CRURA+v1, & using machine learning & & \\  & FluxCom & & ERA; GSWP3 & methods (g, g, RF and & & \\  & & - & CERES-GPC; WPDE) & ANN)Two setups & 0.5’ \(\times\) 0.5\({}^{\circ}\) & 1981–2016 & [PERSON] et al. [17] \\  & & - & Remote sensing data: MODIS-based & (METEO+ 88, and RS) & & & \\ Eddy covariance (EC) & & & flux data & & & \\ flux data-driven models & & - & FLUNNET eddy covariance dataset. & & & \\  & & - & Meteorological dataset (CRU-PIK, & & \\  & Model Tree & & GFCC products, and ECMWF & & technique (model tree & \\  & Ensembles (MTE) & - & ERA-ittem reanalysis data) & ensembles) to upscale the EC sites into the & 0.5” \(\times\) 0.5” & 1982–2008 & [PERSON] et al. [46] \\  & & & Remote sensing data (AFAR) & & the EC sites into the & & \\  & & & products of GIMMS, SeaWIS, & gridded dataset & & \\ \hline Satellite-based models & & - & Meteorological dataset & LUE scheme, & & \\  & & & (NCEP Reanalysis) & process-based Farquhar & & \\  & & - & Land-cover data & von [PERSON], and & & \\  & & - & Satellite data (MODIS PAR) & Berry (FCB) model & & \\ \hline \end{tabular}
\end{table} 
Table 1: The widely used models for the prediction GPP dataset; their structure, forcing data, data period, and spatial resolution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models Type** & **Modelio** & \multicolumn{2}{c}{**Fording Data**} & **Method Used** & \multicolumn{1}{c}{**Spatial**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**References**} \\ \hline \multirow{8}{*}{Stellline-based models} & \multirow{8}{*}{VPM} & - & Meteorological dataset (NCEP ID) & \multirow{8}{*}{LUE scheme} & 0.05\({}^{\circ}\times\) 0.05\({}^{\circ}\) & \multirow{8}{*}{2000-2016} & \multirow{8}{*}{[PERSON] et al. [21]} \\  & & - & Remote sensing data (FVI, PAR, & & & \\  & & - & Ls, LsINW) & & & \\  & & - & Land-cover data (MCD12Q1 C51) & \multirow{8}{*}{MODS algorithm} & 0.05\({}^{\circ}\times\) 0.05\({}^{\circ}\) & \multirow{8}{*}{1982–2016} & \multirow{8}{*}{[PERSON] et al. [88]} \\  & & - & C4 crop percentage, major crop types & & & \\  & & - & C4 vegetation percentage map & & & \\  & & (SISCP ID) & & & & \\ \cline{2-8}  & \multirow{8}{*}{GIMMS} & - & Meteorological dataset (NCEP-DOE & \multirow{8}{*}{2000-2016} & \multirow{8}{*}{[PERSON] et al. [21]} \\  & & - & Reanalysis II, CRU-NCEP) & & & & \\  & & - & Remote sensing data: GIMMS’s FPAR & & & \\  & & and LAI data & & & & \\ \cline{2-8}  & \multirow{8}{*}{GLAS and Revised EC-LUE} & - & Meteorological dataset (HERRA-2) & \multirow{8}{*}{MODS algorithm} & \multirow{8}{*}{1982–2016} & \multirow{8}{*}{[PERSON] et al. [88]} \\  & & - & Remote sensing data: GIMMS’s FPAR & & & \\  & & and LsI data & & & & \\ \cline{2-8}  & & - & Land-cover data (MCD12Q1 product) & \multirow{8}{*}{LUE principle} & 0.05\({}^{\circ}\times\) 0.05\({}^{\circ}\) & \multirow{8}{*}{1982–2018} & \multirow{8}{*}{[PERSON] et al.