## 1 Introduction  
Monitoring of terrestrial carbon fluxes is critical to assess impact of human activities and Climate Change in ecosystem functions and distribution [1]. Vegetation carbon uptake or Gross Primary Production (_GPP_) is described as a combination of the amount of Photosynthetically Active Radiation (_PAR_) absorbed by vegetation and the efficiency with which this is used by photosynthesis [2; 3]. Structural and biochemical variables of vegetation involved in this process can be inferred from radiation reflected or emitted at the top of canopy, and therefore globally mapped by remote sensors [4; 5]. At ecosystem scale, carbon exchanges are measured using the eddy covariance technique [6]. Nonetheless, this approach is spatially limited because the spatial representativeness of the EC flux data is limited (from few meters to ~1 km\({}^{2}\) around the Eddy Covariance (EC) system depending on the measurement and canopy height). The footprint climatology has been demonstrated as an essential tool to get information about the vegetation sampled by the EC flux measurements [7]. In this context, Remote Sensing (RS) becomes an opportunity to provide exhaustive spatial information of plant function and structure and therefore, estimate carbon uptake at global scale [8]. However, spatial and temporal mismatches between optical and flux measurements jeopardize the correct parameterization of predictive models [9; 10]. EC footprint shows a large variability driven by wind direction and atmospheric stability that requires high or medium RS spatial resolution images to be characterized [7; 11]. Such spatial resolutions can only be reached at global scale at the expenses of frequency of acquisition. The impact of pixel-footprint spatial mismatch on _GPP_ estimation becomes more acute in heterogeneous environments that combine species with different ecological and phenological strategies or different land uses [12; 13; 14]. Among these heterogeneous surfaces, mixed tree-grass and shrub-grass vegetation associations are one of the most extensively and widely distributed on Earth [15]. They range from the tropics to the temperate bioclimatic regions covering ~27 million km\({}^{2}\). Despite the diversity of species, Tree-Grass Ecosystem (TGE) are all characterized by a combination of a sparse woody stratum (evergreen or deciduous trees) and grass layer, usually annual [15; 16]. Tree coverage ranges 8-40% [17], which depends on climatic variables, but also resources availability, disturbance and the complex interactions occurring with grasses [16; 18; 19; 20]. TGE are usually grazed; in fact only in development regions they guarantee the livelihoods of more than 600 million people [21]. At the same time, they are subject to large land use and Climate Change pressures [21]. TGE are inherently challenging for ecosystem and Earth system modeling since species interactions, responses to perturbation, their role in the climate system and feedbacks with the atmosphere are not well understood [15; 16; 22]. These ecosystems are also challenging for RS; the integration of heterogeneous signals at the pixel scale hampers the characterization of structure and function of the different vegetation types in these ecosystems [15; 23; 24]. To disentangle the contribution of the different elements in the scene prior knowledge, in-situ measurements or high spatial resolution imagery have been used [12; 24; 25; 26]. The use of repeated high spatial resolution images enables the identification of pure species pixels within the flux footprint and has been proposed to better model the spatial and temporal dynamics of _GPP_ of terrestrial ecosystems [24; 25; 26; 27]. High or moderate spatial resolution data were used to assess the impact of spatial heterogeneity in different MODIS products [28; 29; 30; 31]. More advanced methods combined high spatial resolution information and 2-D footprint modeling to assess footprint induced uncertainty [7] or the spatial representativeness of flux tower measurements [11]. [PERSON] et al.