## 5 Summary and Conclusions  
In this paper we present a modeling study conducted at site level which represents a first step toward the analysis of the carbon budget of poplar plantations at regional scale. We developed a PROSAILH-BGC coupling the RT Model PROSAILH with the BIOME-BGC model. This coupling allowed us to arrive at a more realistic description of the light regime within the canopy and to simulate the vegetation index NDVI, with the MODIS satellite sensor spectral and overpass-dependent characteristics, as a function of LAI provided by the ecosystem part of the model. This new feature of the model enabled us to assimilate high temporal frequency MODIS NDVI observations into the process-based model. With this study we also provide a set of relevant ecophysiological parameters well suited for the application of PROSAILH-BGC for poplar plantations. The accuracy of the optimized model simulations, evaluated using an EC dataset not exploited in any calibration increased with respect to the PROSAILH-BGC applied using literature-based parameterization. Modeled NDVI time series simulated MODIS data quite realistically (r\({}^{2}\) = 0.75) and key phenological dates were retrieved with far better accuracy than the ones modeled by the internal phenological model: ONDAY and OFFDAY were determined with a mean error of 6 and 7 days, respectively, while with the internal phenological model the mean error was 17 days for ONDAY and 20 days for OFFDAY. The error in the dates estimated with second-step optimization may be due to the development of a green understory which affected the NDVI signal immediately before tree budburst and persisted after overstory leaf senescence. In the same computational step, maximum leaf carbon was also retrieved with an average error of 4.1%. Globally, the two-step optimization process allowed fairly accurate estimates of GPP both at daily (r\({}^{2}\) = 0.72, EF = 0.70, RMSE = 1. 80 gCm-2 day-1) and yearly time steps. In particular, for the annual cumulated GPP we found a sensible reduction in the underestimation of modeled GPP after the two-step optimization compared to the results obtained using Reference Model 1, Reference Model 2 and  
Figure 6: a) Time courses of modeled (red straight line) and observed (blue dotted line) GPP for 2002 and 2003. b) Scatterplot of observed and modeled GPP, data from both the growing seasons were plotted with exclusion of data of the dormant period. The black straight line is the regression line, the dashed lines represent the 95 confidence intervals, the grey line is the 1:1 line. b[0] is the intercept, b[1] is the slope and \(p\) is the significance of the linear regression analysis observed vs modeled. also using the first-step optimized PROSAILH-BGC with phenology determined by the internal routine. For the application of the model over large areas (e.g. regional scale), the unknown phenological parameters as well as the site structural parameters have to be specified in some way. These parameters set to a nominal value (e.g. based on few field observations) or modeled with the internal phenological routine may lead to errors in GPP estimations. This observation underlines the usefulness of the proposed procedure which provides a reliable estimate of such spatially variable parameters based on RS observations. In summary, the proposed approach appears useful for modeling gross primary production both at site-level and at regional scale. In fact, we were firstly able to determine the correct species-dependent parameterization of the process-based model and, afterwards, we were able to assimilate remotely-sensed NDVI time series for the determination of spatially varying variables such as those related to phenology, canopy development and site standing biomass. Further developments should be focused on the application of the method to different forest species and the development of a stable method for the optimization of other site characteristics required by the model (e.g.