# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 5 Results  
### NPP in Current Condition  
The assessment of estimated NPP per forest type is illustrated by the scatterplots in Figure 3. The largest NPP\({}_{\text{NFI3}}\) explained variance amongst forest types was obtained for EBF with almost 70% (\(R^{2}=0.69\)); \(\sim\)60% (\(R^{2}=0.60\), \(R^{2}=0.62\), and \(R^{2}=0.59\) respectively) was obtained for LDBF, HDBF, and LENF; and slightly less than 50% (\(R^{2}=0.46\)) for HENF. Small scattering was observed for EBF except for a great NPP\({}_{\text{NFI3}}\) plot, which was heavily underestimated. A general overestimation, more notable for great NPP values, was also observed. LDBF and HDBF exhibited similar behavior: estimates generally followed the 1:1 line until around 300 g m\({}^{-2}\) a\({}^{-1}\), where scattering appeared (more for LDBF),and they saturated at around 700 g m\({}^{-2}\) a\({}^{-1}\). LENF presented a constant underestimation, but with small scattering. HENF exhibited more scattering than LENF, but estimates followed the 1:1 line until around 200 g m\({}^{-2}\) a\({}^{-1}\), where saturation appeared. The accuracy statistics resulting from the NPP assessment are reported in Table 4. The greatest difference between the two NPP\({}_{\text{EST}}\) to explain the NPP\({}_{\text{NFI3}}\) variance was found for LENF with 10 percentage points (pp) (from \(R^{2}\) = 0.59 with GPP\({}_{\text{PEM}}\) to \(R^{2}\) = 0.49 with GPP\({}_{\text{BGC}}\)). Around half this difference was found for EBF, LDBF, and HENF (respectively from \(R^{2}\) = 0.69, \(R^{2}\) = 0.60, and \(R^{2}\) = 0.46 with GPP\({}_{\text{PEM}}\) to \(R^{2}\) = 0.74, \(R^{2}\) = 0.56, and \(R^{2}\) = 0.51 with GPP\({}_{\text{BGC}}\)), while only 2 pp were found for HDBF (from \(R^{2}\) = 0.62 with GPP\({}_{\text{PEM}}\) to \(R^{2}\) = 0.60 with GPP\({}_{\text{BGC}}\)). More of the NPP\({}_{\text{NFI3}}\) variance was explained for broadleaved forests than for needleleaved forests independently of the used GPP. According to the calculated MBE, NPP was overestimated in broadleaved forests but underestimated in needleleaved forests. In addition, the MBE obtained for HDBF and LENF was an order of magnitude greater than for the rest of the forest types. RMSE was high for all forest types compared to their corresponding mean NPP\({}_{\text{NFI3}}\) values (114 g m\({}^{-2}\) a\({}^{-1}\), 418 g m\({}^{-2}\) a\({}^{-1}\), 421 g m\({}^{-2}\) a\({}^{-1}\), 287 g m\({}^{-2}\) a\({}^{-1}\), 209 g m\({}^{-2}\) a\({}^{-1}\) respectively for EBF, LDBF, HDBF, LENF, and HENF). Figure 3: Scatterplots of estimated NPP (NPP\({}_{\text{EST}}\)) vs. reference NPP (NPP\({}_{\text{NFI3}}\)) per forest type. NPP\({}_{\text{EST}}\) was calculated with GPP\({}_{\text{PEM}}\) and NFI3 GSV. 1:1 line in blue. Regression line in red. Figure 4a,b show, respectively, the average annual forest NPP calculated using Equation (4) with GPP\({}_{\text{PEM}}\) and with GPP\({}_{\text{BGC}}\). The spatial pattern was the same in both cases: NPP was larger in the NW and decreases towards the SE, except in the most southern and the most eastern (this one located in the north too) regions, where NPP was also great. NPP calculated with GPP\({}_{\text{BGC}}\) was generally smaller than NPP calculated with GPP\({}_{\text{PEM}}\). Table 5 summarizes the basic statistics of the mentioned GPPs and their corresponding NPPs per forest type. GPP\({}_{\text{PEM}}\) maxima are greater than GPP\({}_{\text{BGC}}\) maxima for broadleaved forest, but the opposite relationship occurred for needleleaved forests. GPP\({}_{\text{PEM}}\) means were greater than GPP\({}_{\text{BGC}}\) ones for all forest types. NPP maxima and means exhibited a similar behavior. However, these differences do not seem very relevant, especially the differences in the mean values.