## 2 Materials and Methodology
### Data Description  
#### 2.2.1 State-of-the-Art Carbon Productivity Datasets  
GPP data were collected from four global datasets that included:  
* The revised EC-LUE-based GLASS-GPP: we employed the yearly GPP data at a spatial resolution of 0.05\({}^{\circ}\) in the period between 1982 and 2016 ([[https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)]([https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)) (accessed on 18 March 2020)). This dataset was generated based on the revised EC-LUE algorithm [23] while considering the principle of LUE [19; 54; 55]. The main version of the EC-LUE was forced by four variables: the photosynthetically active radiation (PAR), the normalized difference vegetation index (NDVI), the Bowen ratio of sensible to latent heat flux, and the air temperature [24]. To precisely detect the long-term change in GPP, GLASS-GPP employed the revised EC-LUE algorithm, in which other variables were added to the revised version to account for the impact of several factors; e.g., the CO\({}_{2}\) concentrations, radiation, and vapor pressure deficit (VPD) [23]. The model was superior in the sense that it detected the inter-annual variability in GPP both globally and at the site level. The revised EC-LUE incorporated key environmental factors into the estimation process, which resulted in long-term confident estimates of GPP worldwide. * FluxCom-GPP: this dataset was generated through upscaling of EC flux tower stations. The monthly FluxCom-GPP, which is available at a spatial resolution of 0.5\({}^{\circ}\), is an ensemble of daily remotely sensed and observed data that spans the period between 1982 and 2016 [18; 56; 57]. Three machine learning algorithms (i.e., multivariate regression splines, artificial neural networks, and random forests) were forced by the meteorological data of the CRU JRA version 1.1 [58] to generate the gridded dataset of the FluxCom-GPP. Details of the machine learning algorithms and the training and validation framework are outlined in [PERSON] et al. [18]. The dataset is available for the globe via the FluxCom platform ([[http://www.fluxcom.org](http://www.fluxcom.org)]([http://www.fluxcom.org](http://www.fluxcom.org)) (accessed on 6 July 2021)). * The Global Inventory Modeling and Mapping Studies (GIMMS-GPP): this dataset is available on a yearly basis at a grid interval of 0.05\({}^{\circ}\) and covers the period of 1982-2016 [48]. In this dataset, the GIMMS-FPAR (fraction of photosynthetically active radiation) data, leaf area index (LAI) data, and daily CRU-NCEP weather data were used to generate the gridded dataset of the GIMMS-GPP following [PERSON] et al. [59]. Due to CRUNCEP's reliability and widespread use in many models, the \"CRUNCEP P1 Standard\" model parameterizations were run. * The vegetation photosynthesis model (VPM-GPP): over 17 years (2000-2016), the VPM-GPP dataset has been made available at a 0.05\({}^{\circ}\) spatial resolution ([[https://doi.org/10.6084/m9.figshare.c.3789814](https://doi.org/10.6084/m9.figshare.c.3789814)]([https://doi.org/10.6084/m9.figshare.c.3789814](https://doi.org/10.6084/m9.figshare.c.3789814)) (accessed on 6 July 2021)). Using the LUE scheme, the VPM-GPP dataset was generated and GPP was estimated as the amount of light absorbed by chlorophyll in vegetation (i.e., the PAR absorbed by chlorophyll). The VPM algorithm was used to generate the VPM-GPP gridded dataset using different MODIS datasets (e.g., EVI, LSWI, nighttime LST, and land-cover types) as well as daily NCEP reanalysis II temperature and radiation data, [21]. GPP data based on the eddy covariance (EC) tower: the EC method is the most popular and straightforward way to compute the carbon flux at the ecosystem level [60]. Since 2000, EC measurement has been performed continuously in the Yatir Forest in Israel [61; 62] based on the European approach; since then, it has been part of the EuroFlux and FLUXNET communities.