# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 2 Methods
### Satellite Datasets  
NEE is the result of the synergistic effect of photosynthesis and respiration, and is influenced by a variety of biological, atmospheric, hydrological, and soil factors. At the ecosystem scale, the GPP is mainly affected by environmental factors (such as solar radiation, air temperature, water vapor pressure difference, and water availability) and biological factors (such as canopy development and phenology). Ecosystem respiration includes autotrophic respiration and heterotrophic respiration, among which soil respiration accounts for the largest proportion. Ecosystem respiration is closely related to plant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **TAP** & **MAT** & **Elevation** & **Latitude** & **Longitude** & **Period** \\ \hline YCA & 582 & 13.1 & 23.4 m & 36\({}^{\circ}\)57\({}^{\prime}\)30\({}^{\prime\prime}\) & 116\({}^{\circ}\)38\({}^{\prime}\)26\({}^{\prime\prime}\) & 2003–2010 \\ CBF1 & 713 & 3.6 & 738 m & 42\({}^{\circ}\)24\({}^{\prime}\)9\({}^{\prime\prime}\) & 128\({}^{\circ}\)05\({}^{\prime}\)45\({}^{\prime\prime}\) & 2003–2010 \\ DHF & 1956 & 20.9 & NULL & 23\({}^{\circ}\)10\({}^{\prime}\)25\({}^{\prime\prime}\) & 112\({}^{\circ}\)32\({}^{\prime}\)3.7\({}^{\prime\prime}\) & 2003–2010 \\ QYF & 1542.4 & 17.9 & NULL & 26\({}^{\circ}\)44\({}^{\prime}\)29\({}^{\prime\prime}\) & 115\({}^{\circ}\)03\({}^{\prime}\)29\({}^{\prime\prime}\) & 2003–2010 \\ BNF & 1490 & 21 & 750 & 21\({}^{\circ}\)55\({}^{\prime}\)39\({}^{\prime\prime}\) & 101\({}^{\circ}\)15\({}^{\prime}\)55\({}^{\prime\prime}\) & 2003–2010 \\ HBG & 535.2 & \(-1.2\) & 3358 & 37\({}^{\circ}\)40\({}^{\prime}\) & 101\({}^{\circ}\)20\({}^{\prime}\) & 2003–2010 \\ DXG & 450 & 1.3 & 295.7 & 30\({}^{\circ}\)29\({}^{\circ}\)50\({}^{\prime\prime}\) & 91\({}^{\circ}\)03\({}^{\prime}\)58\({}^{\prime\prime}\) & 2004–2010 \\ NMG & NULL & 0.9 & 1200 & 43\({}^{\circ}\)32\({}^{\prime}\) & 116\({}^{\circ}\)40\({}^{\prime}\) & 2004–2010 \\ HBW & 490 & \(-1.1\) & 3202 & 37\({}^{\circ}\)37\({}^{\prime}\) & 101\({}^{\circ}\)19\({}^{\prime}\) & 2004–2009 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site characteristics used in this study. The mean annual temperature (MAT, \({}^{\circ}\)C) and total annual precipitation (TAP, mm) are for the time period (Period).  
Figure 1: The locations of nine flux tower sites in China.  
photosynthesis, metabolism, and litter fall. Specifically, autotrophic respiration can be empirically expressed as a function of air temperature and GPP, while heterotrophic respiration is often modeled as a function of soil temperature, moisture, and substrate [27]. Clearly, these variables directly or indirectly affect the NEE, many of which can be assessed using satellite remote sensing and reanalysis data. For example, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are biophysical indicators of vegetation canopy development, phenology, and greenness, and are closely related to seasonal variations in photosynthetic intensity and biomass of vegetation [28].  
Therefore, this study mainly selected Ta, Ma, Rn, NDVI, EVI, fraction of photosynthetically active radiation (FPAR), and leaf area index (LAI), which are strongly related to ecosystem carbon flux, as explanatory variables for NEE simulation. This study used the Moderate Resolution Imaging Spectroradiometer (MODIS) 4-day composite NDVI and EVI product (MYD13A2) with a spatial resolution of 1000 m, from 2003 to 2010. The MCD15A3H product of the MODIS provides two data products, the FPAR and LAI products; the time resolution is 4 days, and their spatial resolution is 500 m. We used the Google Earth Engine (GEE) to download the data, in order to unify the spatial resolution of the two, resample the data to 500 m space resolution, and used the cubic spline interpolation method to interpolate the remote sensing satellite data into daily intervals.