# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 3 Study Area and Data
### Data Used for the NPP Modeling  
Most datasets used for the current NPP modeling were produced during previous research exercises. The SIOSE (Land Cover and Use Information System of Spain) land cover database was provided by ()Instituto Geografico Nacional through [[http://www.siose.es/web/guest/descargar](http://www.siose.es/web/guest/descargar)]([http://www.siose.es/web/guest/descargar](http://www.siose.es/web/guest/descargar)). It consists of a cartographic scale 1:25000 polygon database generated by photointerpreting and digitalizing reference data, which included SPOT5 and Landsat-5 TM images together with local ground ancillary data [26; 39]. The land cover data contains the percentage of surface occupied by each of the present classes in each polygon. With the help of the global 3 arc second digital elevation model from the Shuttle Radar Topography Mission [27] (downloaded from [[http://edcftp.cr.usgs.gov/pub/data](http://edcftp.cr.usgs.gov/pub/data)]([http://edcftp.cr.usgs.gov/pub/data](http://edcftp.cr.usgs.gov/pub/data))), \begin{table}
\begin{tabular}{c c c c c} \hline
**Forest Type** & **Species** & **Number of Plots** & **GSV (m\({}^{3}\) ha\({}^{-1}\))** & **CAI (m\({}^{3}\) ha\({}^{-1}\) a\({}^{-1}\))** \\ \hline
**EBF** & _Quercus _ilex_ & 266 & 45 & 1.06 \\
**LDBF** & _Quercus robur_ & 182 & 122 & 4.10 \\
**HDBF** & _Fagus sylvatica_ & 255 & 197 & 4.57 \\
**LENF** & _Pinus pinea_ & 122 & 71 & 2.97 \\
**HENF** & _Pinus nigra_ & 307 & 100 & 3.52 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary information about the used third Spanish Forest Inventory (NFI3) plots per forest type. Growing stock volume (GSV) and current annual increment (CAI) are averaged for all plots. the original legend of the land cover data was regrouped into the five forest types mentioned in Table 1 using an 800-m threshold to separate low- and high-altitude classes [19]. The resulting forest type map is shown in Figure 2a. Daily ground measurements of precipitation, and minimum and maximum air temperature were provided by the Spanish Meteorological Agency (www.aemet.es) from \(\sim\)400 meteorological stations distributed throughout the study area. Ordinary kriging was used to interpolate these data and obtain daily 1-km spatial resolution maps during the 2005-2012 period. These maps were used as inputs in MT-CLIM [24] to simulate daylength, daylight average partial pressure of water vapor, and daily average air temperature. Version 4.3 of the code was used and is available at the NTSG website ([[http://www.ntsg.umt.edu/project/mt-clim.php](http://www.ntsg.umt.edu/project/mt-clim.php)]([http://www.ntsg.umt.edu/project/mt-clim.php](http://www.ntsg.umt.edu/project/mt-clim.php))). Figure 2: _Cont._  
SEVIRI products LSA-201 (downward surface shortwave flux estimated every 30 min, MDSSF) and LSA-203 (daily downward surface shortwave flux, DIDSSF) [40] for the period 2007-2012 were acquired from the LSA-SAF server ([[http://landsaf.meteo.pt](http://landsaf.meteo.pt)]([http://landsaf.meteo.pt](http://landsaf.meteo.pt))). The images were reprojected to a regular longitude/latitude 1-km spatial resolution grid. MDSSF was used to calculate DIDSSF when it was unavailable. Incoming global solar radiation was obtained by applying ANN to the abovementioned air temperature and precipitation maps [22] and was used to fill DIDSSF gaps and to calculate DIDSSF for the years 2005 and 2006. To apply the gap-filling procedure, a relationship was used between the two datasets, which were previously validated [22,23]. The main eccclimatic features of the five Spanish forest types derived from the datasets exposed above are summarized in Table 2.