# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 4 Discussion  
Top-down satellite driven MODIS NPP (Net Primary Production) estimates using local European daily climate data (MODIS EURO) exhibit smaller differences from the bottom-up terrestrial forest inventory NFI NPP estimates (Table 1) than the original MODIS GLOB estimates using global climate data (Figure 3; Table 2). This confirms that the output from the climate sensitive MOD17 algorithm can be substantially improved by using enhanced daily climate data [22] and supports the findings of the pilot study in Austria [15] by extending the focus to a continental scope. The local European daily climate data [25] used for MODIS EURO reduced across scales from continental (Figure 3) to national scale (Figure 4) substantially the differences between NPP using the MOD17 algorithm and terrestrial forest inventory data (Table 2). Both NPP estimates are also consistent across various gradients (elevation, latitude and longitude in Figure 5 and tree age, tree height, MODIS Land cover type and dominant species in Figures S6-S9). In this study we evaluated MODIS EURO in comparison to the global MODIS NPP dataset [17] using our terrestrial NFI NPP. The specific methodologies and differences of our forest inventory data sets (Table S1) and missing information on fine roots and litter fall do not permit a proper validation of NPP. Since the forest inventory data was collected with a different purpose [10], it contains a different error structure due to the small sample plot size and large grid spacing (one or very few plots within a MODIS pixel) as compared to the continuous 1-km MODIS grid. The large variations and local discrepancies apparent in this study (Figure 3; Table 2) are also reflected in a study on evaluating NPP and GPP (Gross Primary Production) from the MOD17 algorithm for North and South America [37]. While the authors reported no general bias in the MODIS NPP product, they found over- as well underestimation especially for certain locations and forest biomes of more than 30%. This study shows that in Europe discrepancies between MODIS EURO and terrestrial NFI NPP exceeds 10% in three out of twelve countries (Table 2). This study improves the knowledge on explaining discrepancies between remote sensing and terrestrial NPP estimates by highlighting the effect of stand density index (SDI). Forests with stand density of 200 or lower are expected to have gaps, canopy cover below 100% and low competition between trees. Under such conditions the NFI NPP is substantial lower than MODIS NPP (Figures 6 and 7). This can be explained that at low stand density a substantial share of NPP is undetected by the forest inventory system (gaps filled with young trees or shrubs below diameter threshold), while MODIS NPP is able to capture these gaps via leaf area index provided by the satellite [15]. Figure S10 in Supplementary Material confirms that the stand density related trend of \(\Delta\)NPP in Figures 6 and 7 is mainly caused by NFI NPP, which shows a stronger increase with SDI than MODIS NPP. Since we tested this effect with MODIS GLOB as well, we can conclude that any MODIS productivity estimates irrespective from the used climate input cannot detect such important effects adequately. The relatively large pixel size of 1-km apparently does not allow MODIS NPP to capture small scale patterns such as clear-cuts, thinning operations or disturbance events, while a forest inventory can detect them better. This confirms the findings of the pilot study in Austria [15] and indicates that differences in stand density needs consideration also on the much larger European scale. MODIS EURO agrees very well with NFI NPP at average stand densities (Figure 6). This could be explained with the calibration of the BPLUT tables used in the MOD17 algorithm [9] using large-scale global terrestrial NPP data [27]. The calibration data most likely represents average forest conditions and may not capture very open or very dense forests adequately.