# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 2 Methods and Data
### Data Source
#### 2.2.2 Data Driving the Model  
ERA-Interim is a widely used reanalysis dataset that provides comprehensive and high-quality global atmospheric and surface parameters spanning several decades [55; 58]. ERA-Interim, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), offers a wealth of information on atmospheric variables such as wind, temperature, humidity, and pressure, as well as surface parameters like sea ice, sea surface temperature, and soil moisture. The dataset contains 69 global variables with a temporal and spatial resolution of 1 h and 0.1 degree, respectively. We applied two variables from the reanalysis dataset as driving variables for the model, including 2 m temperature (T2m; K) and 2 m dewpoint temperature (D2m; K).  
Google Earth Engine (GEE), developed by Google, is a cloud-based geospatial computing platform designed to allow users to leverage Google's powerful computing power and computing resources to analyze and process large amounts of geospatial data [59; 60]. It integrates satellite imagery, geospatial datasets, and other Earth observation data to support a wide range of applications in environmental monitoring, land use analysis, disaster response, and more. The global surface solar radiation downwards (SSRD) at a 0.1-degree spatial resolution and 1-h temporal resolution was extracted from the dataset [56].  
MODIS, which stands for Moderate-Resolution Imaging Spectroradiometer, is a state-of-the-art Earth observation instrument aboard the NASA Terra and Aqua satellites [57; 61]. Its broad spectral coverage allows it to monitor a variety of Earth processes, including cloud cover, sea surface temperature, vegetation status, land cover changes, and more. We retrieved two variables from the dataset, namely leaf area index (LAI, m\({}^{2}\)m\({}^{-2}\)), with a temporal and spatial resolution of 4 days and 500 m, respectively, and vegetation types (Landcover), with a temporal and spatial resolution of one year and 500 m, respectively. We obtained LAI data with a temporal resolution of 1 day by removing outliers [62] and fitting the double logistic equation [63] and data with a spatial resolution of 0.1 degree by resampling.  
The accurate simulation of water, carbon, and energy exchanges between the atmosphere and biosphere relies heavily on understanding the global distribution of C3 and C4 plants. The distinctive physiological and functional characteristics of these plant types play a pivotal role. To achieve this understanding, we utilized the 1-degree spatial distribution map of global C3 and C4 vegetation [17]. This incorporation of the distribution data enabled us to account for the diverse physiological processes inherent to C3 and C4 vegetation, thereby enhancing the precision of our simulations.  
The vegetation clumping index (CI) quantifies the degree of deviation from the random distribution of leaves and serves as an important structural parameter of the canopy that controls photosynthesis and evapotranspiration processes in terrestrial ecosystems [64; 65]. The global clumping index (CI), with a temporal and spatial resolution of 8 days and 500 m, respectively, was download from National Ecological Science Data Center [54].