# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 6 Discussion  
### NPP in Current Condition  
The present study focuses on the use of a modeling strategy for the simulation of annual forest NPP over peninsular Spain through the combination of a production efficiency model, which was used for the estimation of annual forest GPP, and the bio-geochemical model Biome-BGC, which was used to simulate GPP, \(R_{\text{G}}\), and \(R_{\text{M}}\). Both models were previously optimized for their application in the study area [8; 19] and were driven by a combination of remote sensing observations and ground meteorological data. The modeling strategy described in [20] was developed to overcome one of Biome-BGC's limitations, i.e., the assumption of the considered ecosystem to be in equilibrium with its surroundings (climax conditions), through the correction of Biome-BGC's outputs to account for actual conditions of the considered ecosystem and through the use of a production efficiency model for the estimation of GPP. However, other limitations of Biome-BGC and the production efficiency model must be taken into account to evaluate the obtained results. The space where the models are applied is divided into cells and the simulations take place individually in each cell without interaction with other cells during the functioning of the models. Therefore, the spatial relationships and variations observed in the outputs of the models are provided by the ones already present in their inputs. This is important when interpreting the spatial patterns presented in Figure 4. Furthermore, both models assign a single ecosystem functional type (in the case of the present study, a forest type) to each cell and assume that it is homogeneous independently of the spatial resolution at which the models work and that it does not change through time. This affects light use efficiency in the production efficiency model and the ecophysiological parameters in Biome-BGC, but it is also relevant to interpret the results of NPP simulation in future condition because the possibility of the current forest type being replaced by another ecosystem is not contemplated. Only the current forest type reactions to possible variations in air temperature and precipitation due to climate change were therefore analyzed. The two different GPP sources, i.e., the one estimated through the production efficiency model (GPP\({}_{\text{PEM}}\)) and the one simulated using Biome-BGC (GPP\({}_{\text{BGC}}\)), that are involved in the calculation of NPP were validated against daily EC-derived GPP from different ecosystems (forest included) in a previous study [19]. Coefficients of correlation between 0.64 and 0.86 and between 0.52 and 0.75 were respectively obtained for GPP\({}_{\text{PEM}}\) and GPP\({}_{\text{BGC}}\), while RMSE ranged from 0.7 g m\({}^{-2}\) d\({}^{-1}\) to 1.7 g m\({}^{-2}\) d\({}^{-1}\) and from 0.9 g m\({}^{-2}\) d\({}^{-1}\) to 1.8 g m\({}^{-2}\) d\({}^{-1}\). In the present study, new datasets (e.g., the respirations simulated using Biome-BGC and the GSV map) with their own uncertainty were incorporated into the calculation of NPP, which was assessed through an indirect validation against a reference NPP (NPP\({}_{\text{NFH3}}\)) calculated from CAI observations derived from forest inventory measurements. The strength of this approach is to combine the directly derived GPP\({}_{\text{PEM}}\) with all respirations and allocations simulated by the bio-geochemical model Biome-BGC. However, actual NPP is difficult to predict due to the numerous factors and human-induced disturbances affecting it [48]. In the current case, i.e., in all applications over large areas, the modeling approach must be based on easily collectable