# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods
### Terrestrial NFI NPP  
Terrestrial forest data such as national forest inventory (NFI) data assess accumulated carbon on a systematic grid using a permanent plot design. From repeated observations of diameter at breast height (DBH) and/or tree height (H) in combination with biomass functions or biomass expansion factors the carbon accumulation of trees is estimated. Since this method is based on single tree measurements and local biomass studies, NPP derived from forest inventory data incorporates local effects such as weather patterns, climate anomalies, stand age, differences in biomass allocation, site and soil effects and different forest densities due to forest management [15; 27]. We obtained 196,434 forest inventory plots covering 12 European countries. In Europe, each country has its own National Forest Inventory (NFI) system, which all have different measurement periods, sampling designs and methodologies [10] (Table S1 in the Supplementary Material). Thus, we first had to develop a harmonized and consistent terrestrial dataset for estimating Terrestrial NPP. We calculated NPP using the forest inventory data according to Equation (3). \[\mathrm{NPP}\,=\,\mathrm{CARB_{INC}}\,+\,\mathrm{FR_{TO}}\,+\,\mathrm{C_{LF}} \tag{3}\]  
\(\mathrm{CARB_{INC}}\) is the carbon increment of trees (gC\(\cdot\) m\({}^{-2}\cdot\) year\({}^{-1}\)). \(\mathrm{FR_{TO}}\) is the carbon used for fine root turnover [28; 29]. Fine root turnover \(\mathrm{FR_{TO}}\) is assumed to be equal to the carbon flow into litter C\({}_{\mathrm{LF}}\)[27; 30]. Both processes are controlled by the same factors and the assumption of similarity between the above- and belowground turnover of short-living plant organs is supported by recently collected European data on fine root turnover [29] and litter fall [31]. C\({}_{\mathrm{LF}}\) is the flow of carbon into litter (gC\(\cdot\) m\({}^{-2}\cdot\) year\({}^{-1}\)) estimated using a climate-sensitive and species-dependent model [31] and is calculated as:  
\[\mathrm{Broadleaf-dominated}:\,\mathrm{C_{LF}}\,=\,\mathrm{CF}\exp(2.643\,+ \,0.726\,\mathrm{Ln}(\mathrm{T}\,+\,10)\,+\,0.181\,\mathrm{Ln}(\mathrm{P})) \tag{4}\]  
\[\mathrm{Coniferous-dominated}:\,\mathrm{C_{LF}}\,=\,\mathrm{CF}\exp(2.708\,+ \,0.505\,\mathrm{Ln}(\mathrm{T}\,+\,10)\,+\,0.240\,\mathrm{Ln}(\mathrm{P})) \tag{5}\]  
CF is the carbon fraction of dry biomass which is set equal to 0.5 [11]. \(T\) is the mean annual temperature from the year 2000 to 2012 (\({}^{\circ}\)C). \(P\) is the mean annual precipitation 2000 to 2012 [mm]. For temperature and precipitation we use the European climate data [25] to capture important small-scale regional effects such as elevation or topography in a more realistic way. Equation (4) is applied for all plots where broadleaf species contribute most to total basal area and Equation (5) is used for coniferous-dominated plots (see Table S2 of the Supplementary Material). We used data from nine National Forest Inventories (Austria, Czech Republic, Germany, France, Finland, Norway, Poland, Romania, Spain), and three Regional Forest Inventories (Belgium, Estonia, Italy). We grouped our 12 countries in four geographic regions, North Europe, Central-West Europe, Central-East Europe and South Europe [7], to address the large environmental, elevational and climatic gradients in Europe. Countries within a region should have similar climatic and edaphic conditions as well as similar tree allometries and allocation patterns [32]. The original locations of the inventory plots were falsified to the nearest pixel of the MODIS grid to guarantee the locations of the plots remain unknown. Temporal consistency with the MODIS data (available since year 2000) was ensured by using only inventory data, which provide CARBINC (Equation (3)) for the time period 2000 to 2012. Figure 1 shows our study region with the four geographic regions completely covered by MODIS EURO, and the 12 countries, where we have NFI NPP.