# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 1 Introduction  
The signature of the Kyoto Protocol has renewed interest in the study of forests. Forests act as potential carbon sinks for the mitigation of climate change impacts, but in turn, they are also affected by climate. Specifically, Mediterranean forests are very sensitive to climate change effects. According to [1], an increment of annual mean air temperature of 3-4 \({}^{\circ}\)C and a reduction of annual precipitationof 20% would lead to a decrease in photosynthesis and a decline of biomass accumulation during the current century. In this context, the net primary production (NPP) of terrestrial ecosystems is a variable that can reflect these changes, and its monitoring becomes key to apply the needed adaptation and mitigation actions. NPP is a function of gross primary production (GPP), the flux of carbon fixed by plants through photosynthesis, minus plant autotrophic respiration (\(R_{\text{A}}\)), the flux of carbon released back to atmosphere due to internal plant metabolism. Therefore, NPP quantifies the flux of carbon stored by plants in their structure. Three main methods used for the estimation of vegetation carbon fluxes and storage are (1) field measurements, (2) remote sensing, and (3) bio-geochemical modeling, which are all briefly described below. (1) Field measurements are the most accurate and are usually considered as the reference. The eddy covariance (EC) technique allows the estimation of net ecosystem carbon exchange (defined as NPP minus heterotrophic respiration) and total respiration from spectral and micrometeorological continuous measurements [2]. However, it is a site-specific method that only accounts for several hundreds of squared meters around the measurement site. For its best functioning, the required local conditions include flat upwind terrain and lack of horizontal air mass movements. Thus, it is not particularly useful for the study of spatial patterns. Forest inventories (both at regional and national scales) provide periodic estimates of forest attributes such as volume and biomass [3], from which carbon storage can be estimated and categorized by forest type, species, and for different administrative units (e.g., province, region, and state). This methodology can be applied at large scales, but it is expensive to implement in terms of invested resources and time [4]. For example, in the particular case of the study area (pensilular Spain, see Section 3), only one national inventory is performed every 10 years [5]. (2) Remote sensing data derived from measurements of radiation reflected or emitted by the Earth's surface, which are available over extensive areas, allow carbon fluxes and storage to be estimated [6; 7]. Several efforts to estimate GPP have recently been made in the study area [8; 9; 10] using the Monteith approach [11], but the estimation of NPP is more problematic due to the difficulty in the estimation of \(R_{\text{A}}\)[12; 13]. Optical data for the estimation of carbon storage are available at a suitable frequency and globally, but they suffer from saturation of the signal and the conditions of the atmosphere [4]. Radar data are not affected by atmospheric conditions but also suffer from signal saturation, and the complex processing needed for their use is problematic in topographically rugged areas [14]. (3) Bio-geochemical models such as Biome-BGC [15; 16] can simulate vegetation processes (including carbon fluxes and storage) and have been recently applied in Mediterranean regions with success [17; 18; 19]. These models, however, require a large amount of input data, which can be classified into ecophysiological parameters and drivers (site physical data and meteorological time series). Moreover, Biome-BGC assumes that the considered ecosystem is in equilibrium with its surroundings, and its outputs must be corrected to account for actual ecosystem conditions.