## 1 Introduction  
Mediterranean grasslands provide different ecosystem, social, and economic services to the Mediterranean basin, occupying approximately 1.3 million km\({}^{2}\) of the terrestrial surface [1; 2]. They help to maintain biodiversity, improve soil protection, and lead to important water and climate interactions [3; 4]. They are also determinants for livestock production, especially sheep and cattle [5; 6]. As an example, pastoral exploitation is the first economic activity in the Spanish mountains, constituting the economic base of the rural environment. As a whole, forest and agricultural pastures occupy more than 27 million hectares, i.e., more than 55% of the Spanish surface [7]. Mediterranean grassland dynamics are conditioned by the influence of the Mediterranean climate, which results in large inter-annual and seasonal variability. This leads to a complex pattern of CO\({}_{2}\) exchange between the atmosphere and the ecosystem. On the one hand, the assessment of CO\({}_{2}\) balance dynamics through photosynthesis (Gross Primary Production, GPP) and ecosystem respiration (Reco) has become a fundamental issue due to the potential impact that it may have on climate change [8; 9; 10]. Thus, it is important to estimate and model GPP to quantify whether Mediterranean grasslands act as sinks or sources of CO\({}_{2}\) over the years depending on the various factors that can interact with them, such as climate, disturbing agents (plagues, fires, natural disasters. \(\ldots\)), and human factors, among others [8; 11]. Furthermore, in the Mediterranean environment, all the atmospheric general circulation models predict an increase in the frequency and intensity of drought periods as a consequence of climate change [12]. Hence, water stress in vegetation is expected to increase, leading to changes in biomass production as well as in the structure and spatial distribution of plant ecosystems [13; 14; 15]. On the other hand, estimating GPP is essential for farmers to make sustainable management of ecosystems, especially grasslands, combining social, economic, and environmental factors [16; 17; 18]. This motivates the study of grasslands from a dynamic point of view, that is, analyzing its temporal evolution at different spatial scales. In this sense, remote sensing (RS) is accepted as the most powerful tool for studying ecosystems at different spatial and temporal scales [19; 20]. In the last two decades, several research studies have assessed the use of RS to estimate GPP in different ecosystems [21; 22; 23]. Most RS models used to estimate GPP are based on the Light Use Efficiency concept proposed by [PERSON] [24] (Equation (1)):  
\[GPP=\varepsilon\times APAR=\varepsilon\times fPAR\times PAR \tag{1}\]  
where \(\varepsilon\) is the light use efficiency conversion coefficient, and the Absorbed Photosynthetically Active Radiation (\(APAR\)) is the product of the Fraction of Photosynthetically Active Radiation (\(fPAR\)) by the Photosynthetically Active Radiation (\(PAR\)). On the other hand, several researchers have evidenced that \(GPP\) is related to spectral vegetation indices. [PERSON]. [25] proposed to multiply the chlorophyll content by \(PAR\) in the Greenness and Radiation model, obtaining better results in estimating crops' \(GPP\). After that, [PERSON] et al. [26] applied this model using several spectral indices to estimate crops' \(GPP\), obtaining good results. [PERSON] et al. [27] proposed the concept of ecosystem light use efficiency (eLUE), that is, the product of fPAR by \(\varepsilon\), which is related to spectral vegetation indices (Equation (2)):  
\[GPP=eLUE\times PAR \tag{2}\]  
They proposed that \(eLUE\) was a function of the Enhanced Vegetation Index (EVI) and that multiplying EVI by the \(PAR\) at the Top of the Atmosphere (\(PAR_{\text{TOA}}\)) was appropriate to obtain a robust estimation of \(GPP\) in savannah ecosystems.