## 5 Discussion  
Obtaining daily local climate data (Austrian National Weather Centre ZAMG) for running the MOD17 algorithm reflects the heterogenity of the Austrian landscape more realistically and thus improves the resulting predictions [15]. The ZAMG data set uses more than 200 daily weather stations  
Figure 8: Difference between MODIS NPP and NFI NPP (MODIS minus NFI) grouped by Water balance deficit. Left original MODIS NPP (\(\Delta\)NPP) is used, right the detrended MODIS NPP (Corr. \(\Delta\)NPP) is used. Properties of illustration analogous to Figure 2.  
across Austria while the NCEP2 (NCEP_DOE_II) [42] and GMAO (NASA Global Modeling and Assimilation Offices) [41] include less than 40 stations. In addition, the spatial resolution of the ZAMG data set is at 0.0083 \(\times\) 0.0083\({}^{\circ}\) (approx. 1 \(\times\) 1 km). This resolution is more detailed than the two global climate data sets GMAO (0.5 \(\times\) 0.67\({}^{\circ}\)), and NCEP2 (1.875 \(\times\) 1.875\({}^{\circ}\)). This is an additional explanation for the higher accuracy in the local data set. Since the NPP is strongly driven by daily climate data [44] the variation of the resulting predictions using the Austrian local climate data set (ZAMG) is higher but better reflects the existing growing conditions (Figure 2). The NPP estimates derived from 8939 plots of the National Austrian Forest Inventory (NFI) exhibit an Austrian average of 486.3 gC/m\({}^{2}\)/year (standard deviation 244.3 gC/m\({}^{2}\)/year). Applying the MODIS NPP algorithm using the local Austrian climate data (ZAMG) result in an average of 579.8 gC/m\({}^{2}\)/year (standard deviation 113.4 gC/m\({}^{2}\)/year). This is a difference between the means of 93.5 gC/m\({}^{2}\)/year. Such differences may have several reasons. For instance, the BPLUTs might not be appropriate for European forest conditions. The difference could also be explained by inconsistency in the definition of NPP (inventory data represent trees with dbh \(>\) 5 cm, MODIS LAI all vegetation on a 1 \(\times\) 1 km pixel). Other issues could be missing fineroot turnover rates in terrestrial NPP calculations, missing carbon increment of trees that died between the inventory measurements or differences in the spatial resolution of the two products. The terrestrial NPP estimates, _SDI_ and _CCF_, are calculated based on the stem numbers in Equations (5)-(7). Thus, any variation in _SDI_ or _CCF_ directly reflects differences in the coverage of the forest area with trees. In our study of Austrian forests, we assume that the detected differences in MODIS _versus_ terrestrial NPP (see Figures 3 and 4) estimates are mainly caused by forest management. However, other events also result in changes to stand density (e.g., wind, drought stress, _etc._). The observed trend by _SDI_[26] and _CCF_[27] (Figures 3 and 4) and the fact that no other analyzed variable (Figures 6-8) can explain the detected apparent deviations of the two NPP estimates supports the hypothesis of [PERSON] _et al._[3]. Our results thus coincide with [PERSON] _et al._[3], that MODIS NPP estimates provide the productivity of fully stocked forests, which are represented only by forest areas without recent changes in stand density. Forest management, such as thinning operations, instantly reduces volume increment per unit area but concentrates the remaining volume increment to fewer trees [52]. Stand density therefore strongly affects tree diameter development. Terrestrial NPP estimates based on diameter or height data are directly affected by any changes in stand density, while the NPP estimates provided by the MODIS NPP algorithm are not. As long as interventions do not strongly reduce the spectral signal of red and near infrared--the two bands used to derive the MODIS inputs FPAR and LAI--forest management is not well detected by satellite-driven NPP estimates (see Figure 3).