## 1 Introduction  
The Donana National Park (DNP), located in the southwestern part of the Iberian Peninsula, is one of the most important wetlands in Europe and is home to a great diversity of ecosystems and species. It is estimated that more than 300 species of birds, 1300 species of vascular plants, and 27 species of continental fish live in Donana [1]. The ecosystems of Donana play a key role in the carbon cycle, storing and releasing large amounts of CO\({}_{2}\) through photosynthesis and respiration [2]. Within the animal community, in addition to wild ungualtes, there are native breeds of domestic livestock in an extensive regime, such as the \"reuterta\" horse and the \"mostrenca\" cow, which graze in these ecosystems, and thus accounting for carbon assimilation allows us to know the availability of forage. This scenario makes carbon assimilation in Donana a very relevant parameter to monitor. Carbon assimilation is essential for understanding the underlying mechanisms of the global carbon cycle [3] and is defined as a derived metric to study and manage biodiversity change [4, 5]. However, climate change is affecting plant functioning and altering plant carbon fluxes, with a decrease in carbon assimilation that is particularly pronounced in Mediterranean ecosystems such as the southwestern part of the Iberian Peninsula [6, 7, 8]. A commonly used indicator of carbon assimilation is gross primary production (GPP) [9, 10], which is the rate of carbon fixation per unit time and over a defined area [11; 12; 13]. In general, methods for measuring GPP and CO\({}_{2}\) fluxes have mostly been developed and evaluated for arable land (e.g., [14; 15; 16]). Therefore, the application of these methods in natural vegetation is the next challenge. This is particularly important in Mediterranean ecosystems, where vegetation represents different layers, low vegetation cover and many different species and plant functional types. Flux towers or eddy covariance (EC) towers provide the necessary measurements to estimate surface energy and carbon fluxes in natural land cover and to calibrate and evaluate land surface models [17; 18]. However, an EC only covers an area (footprint) that varies with the tower height, target surface properties, and the wind direction and speed [19]. For regional scale assessment, it is necessary to combine it with other data sources such as Earth observation imagery to estimate GPP at both spatial and temporal scales [20; 21]. There are several remote sensing-based models for estimating GPP, which can be broadly classified into four types: (1) statistical models, (2) light use efficiency (LUE) models, (3) process models integrated with RS parameters, and (4) machine learning approaches [22]. Recently, LUE models have experienced rapid development [23] and have been successfully applied to estimate GPP in different ecosystems and climates using different sources of remote sensing imagery (e.g., [24; 25; 26]). However, there are few studies on carbon assimilation by remote sensing in the Donana ecosystems (e.g., [27; 28; 29]), and these are mostly focused on the Donana marshes. Furthermore, there are no estimates for sparse xeric scrub, which are needed to improve our knowledge of the role of sparsely vegetated areas in carbon assimilation [30]. LUE models are based on the work of [PERSON] [31; 32] and assume that GPP is proportional to the product of the absorbed photosynthetically active radiation (APAR) by vegetation and the efficiency with which this radiation is converted into biomass. APAR is usually calculated as the product of FAPAR and incoming photosynthetically active radiation (PAR), which can be operatively derived from satellite data [33; 34]. LUE depends on environmental and physiological factors that may limit photosynthesis, such as temperature, humidity, water stress, or nutrition. This means that there are several ways to approach GPP estimation, with LUE applications focusing more on meteorological variables [35] and others using water stress indices [36].