## 4 Results and Analysis
### Stand Density Effects  
MODIS NPP estimates vary by climate input data (ZAMG, NCEP2, GMAO) and are in general higher compared to the terrestrial NPP (NFI in Figure 2). The local daily climate data set provided by the Austrian National Weather Service (ZAMG) exhibits the lowest discrepancy _versus_ the terrestrial NPP estimates (RMSE 276.1 gC/m\({}^{2}\)/year, difference of medians 93.5 gC/m\({}^{2}\)/year, difference of means 42.8 gC/m\({}^{2}\)/year). Forest management reduces stand density [47, 48, 49, 50, 51, 52]. [PERSON] _et al_. [3] suggested that stand density explains the observed discrepancies between \"space-based\" MODIS _versus_ \"ground-based\" NPP estimates. Stand density is important considering that forest management operations change the number of trees, which directly effects the calculation of the terrestrial NPP estimates see Equations (5)-(7). In the MODIS NPP algorithm, on the other hand, forest dynamics are characterized by the two input variables FPAR and LAI, as the land cover type is kept constant. Both variables are derived from spectral properties of the vegetation, in particular the spectral signal of the red and near infrared bands [37, 39]. Intensity of forest management in Austria is regulated (crown cover  
Figure 2: Comparisons of the NPP estimates where (1) _NFI_—the terrestrial NPP; (2) _ZAMG_—MODIS NPP estimates using the daily weather data from the Austrian National Weather Centre; (3) _NCEP2_—MODIS NPP estimates using the daily weather data of the so called NCEP_DOE_II [42], and (4) _GMAO_—MODIS NPP using the daily climate data from NASA Global Modeling and Assimilation Office [41]. The box represents the Median and the 25 th and 75 th quartile, the whiskers extend to 1.5 of the interquartile range, values outside this range are indicated by circles, and on the top the number of values represented by the boxplots is given. after thinnings of at least 60%, while clearcuts bigger than 0.5 ha need special registration and may not exceed 2 ha) [47]. Thus, it can be assumed that forest management in Austria has only a small and temporary influence on FPAR and LAI. The results of the analysis (not shown) support this as MODIS NPP show no correlation with stand density, while terrestrial NPP clearly does, which will be shown shortly. For assessing the potential management impacts on the results we obtain for each inventory plot two stand density measures: (i) the _CCF_--Crown Competition Factor according to [PERSON] _et al._[27] Equation (11) as well as (ii) the _SDI_--Stand Density Index according to [PERSON] [28] in Equation (14). The mean and the variations across all plots are given in Table 1.  
Next, we calculate the differences (\(\Delta\)_NPP_) between the \"space-based\" MODIS NPP (using the Austrian daily climate data ZAMG) and the \"ground-based\" NPP using the NFI data set. After grouping the results by _CCF_ and _SDI_ classes, the median and the first and third quartile of \(\Delta\)_NPP versus SDI_ and _CCF_ class for the total data set are plotted. A direct comparison of terrestrial inventory plots with a MODIS pixel requires the assumption that a plot is actually representative for an area, which is discussed and questioned by previous research [48, 49]. However, it allows us to track the effect of stand variables, which would be otherwise impossible. A distinct stand density related trend is visible with an overestimation of the terrestrial NPP by MODIS NPP at low stand density and an underestimation at high stand density, which is consistent whether using _SDI_ or _CCF_ (Figure 3). The MODIS NPP algorithm uses information on the land cover or vegetation type provided by the classification system of the University of Maryland (MODIS Collection 5 global land cover) [8, 40]. In total, there are 14 land cover classes, of which five deal with forests and characterize the biophysical properties expressed by the parameters of the Biome Property Look-Up-Tables (BPLUT) [7, 8]. These