# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods
### Modis Npp  
Since the year 2000, the MOD17 product provides spatially and temporally continuous NPP estimates across the globe [17]. The algorithm behind uses the reflectance data from the sensor MODIS (MODerate resolution Imaging Spectroradiometer) of the TERRA and AQUA satellites operated by National Aeronautics and Space Administration of the United States (NASA). MOD17 provides GPP and NPP estimates at a 1-km resolution [2; 17] and incorporates basic biogeochemical principles adopted from Biome-BGC [19]. It integrates a light use efficiency logic using remotely sensed vegetation information to estimate GPP (Equation (1)) with a maintenance and growth respiration module to derive NPP (Equation (2)).  
\[\mathrm{GPP}\,=\,\mathrm{LUE}_{\mathrm{max}}\times\mathrm{f}_{\mathrm{Tmin }}\times\mathrm{f}_{\mathrm{vpd}}\times 0.45\times\mathrm{SW}_{\mathrm{rad}} \times\mathrm{FPAR} \tag{1}\]  
\[\mathrm{NPP}\,=\,\mathrm{GPP}-\mathrm{R}_{\mathrm{M}}-\mathrm{R}_{\mathrm{G}} \tag{2}\]  
LUEmax is the maximum light use efficiency, which get adjusted by \(\mathrm{f}_{\mathrm{Tmin}}\) and \(\mathrm{f}_{\mathrm{vpd}}\) to address water stress due to low temperature (Tmin) and vapor pressure deficit (VPD). SWrad is short wave solar radiation load, of which 45% is photosynthetically active. FPAR is the fraction of absorbed photosynthetic active radiation. \(\mathrm{R}_{\mathrm{M}}\) is the maintenance respiration and is estimated using LAI (Leaf Area Index), climate data and biome-specific parameters. \(\mathrm{R}_{\mathrm{G}}\) is the growth respiration and is estimated to be approx. 25% of NPP. The complete algorithm is documented in [18] and more details are found in the cited literature therein.  
The MOD17 algorithm requires climate data, FPAR and LAI (leaf area index) data as well as land cover data, which is derived from MODIS reflectance data [20]. We obtained the global MODIS NPP product (MOD17A3 Version 055) provided by the Numerical Terradynamic Simulation Group (NTSG) at University of Montana available at [[ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/](ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/)]([ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/](ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/)). This data set (hereafter called MODIS GLOB) covers the period of 2000 to 2012, which is the time period covered by our terrestrial data (see next chapter), and provides the annual NPP in gC\(\cdot\) m\({}^{-2}\cdot\) year\({}^{-1}\).  
The source of FPAR and LAI input is MODIS15 LAI/FPAR Collection 5, which was temporally gap filled to close data gaps due to unfavorable atmospheric conditions such as cloudiness or heavy aerosol presence [9]. For Land cover, we used the land cover product MOD12Q1 Version 4 Type 2 [21] representing the conditions in year 2001.  
Climate data are important input into the MODIS NPP algorithm and climate data have a strong impact on the MODIS NPP results [15; 22]. MODIS GLOB uses the global climate data set NCEP2 [23] described in the following Section 2.2. In Europe, we have high quality daily climate data, the E-OBS data set [24], which was recently downscaled to a 1-km resolution [25].  
We next ran the MOD17 algorithm with the downscaled European climate data [25] and obtained an additional MODIS NPP estimate for the period 2000-2012 (hereafter called MODIS EURO), which differ from MODIS GLOB provided by NTSG only in the used daily climate input data. We used the same FPAR, LAI and Land cover input, as used for the global NPP product, MODIS GLOB. MODIS EURO covers our study region, the EU-28 including Norway, Switzerland and the Balkan states (see Figure 1) and is made available under [[ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO)]([ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO)).