## 1 Introduction  
Terrestrial ecosystems play an important role in the global carbon cycle due to their capacity to sequester part of the fossil carbon emitted by anthropogenic activities. Such a capacity is of great interest in order to comply with the commitments of the Kyoto Protocol. In the context of global climate change and sustainable development, agro-forestry management activities play a key role through mitigation. However, agro-forestry ecosystems are also affected by climate change and their contribution to carbon sequestration may be influenced by stresses (e.g. heat-waves, drought, diseases and natural disturbances) [1]. Fast growing forests [2], and in particular poplar plantations, are a typical land-use in the Lombardy region (Northern Italy) covering about 4.5% of the agricultural area and 6.5% of the forestry area [3]. It is therefore relevant to investigate the sink capacity of these ecosystems, both at site level with field measurements and at regional scale with modeling tools. In recent years, a number of process-based models have been developed for estimating carbon and water fluxes at different spatial and temporal scales [4, 5]. Among these, BIOME-BGC [5, 6] is a widely employed ecosystem model designed to simulate plant physiological processes and soil biogeochemistry with a very detailed scheme and at a fine temporal scale (from daily to yearly). BIOME-BGC has been applied with success to different types of forest ecosystems, from Mediterranean [7] to coniferous species [6, 8]. However, to our knowledge, it is rare to find applications on agricultural [9] and agro-forestry ecosystems such as poplar plantations. Spatial analysis was also proven feasible in recent studies in which BIOME-BGC was successfully applied to model net primary production [10] and gross primary production (GPP) at regional and continental scales [11, 12]. Model parameterization and corroboration can benefit from the availability of continuous measurements of carbon, water and energy fluxes between ecosystems and the atmosphere with the eddy covariance (EC) technique [13]. Moreover, these measurements represent an important data source for the optimization of uncertain or unknown model parameters by using an inverse modeling approach [14]. In fact, when parameters cannot be measured directly, inverse modeling allows exploitation of one or more measurable model outputs (e.g. carbon fluxes) to optimize the values of one or more unknown model parameters (e.g. allocation ratios). This optimization is performed by retrieving the set of model parameters that minimizes the difference between simulated and observed data. Many inverse modeling approaches, such as Monte Carlo methods, gradient-based optimization algorithms, look-up tables and neural networks have been proposed in literature [15,16] and successfully applied in earth observation [17-19], biogeochemistry [20,21] and phenological studies [22,23]. Besides EC, remote sensing (RS) is another tool that can be exploited to gather spatially and temporally distributed information which is well suited for regional applications of process-based models. As a consequence, a number of techniques for the assimilation of RS time series at different spatial resolution into process-based models have been developed [24]. These techniques can be grouped into three main categories: i) determination of model initialization parameters (e.g. phenological parameters, leaf area index), ii) update of model state variables through direct ingestion of RS data (forcing) and, iii) estimation of state variables or model parameters through model inversion against RS data (recalibration or optimization). The accuracy of modeled GPP is dependent on a correct parameterization of plant ecophysiology and stand characteristics (e.g. standing biomass and phenology) [25]. For regional scale mapping, such parameters are unknown and have to be determined for each spatial location.