## 1 Introduction  
The Mediterranean-like ecoregions are expected to face the greatest proportional change in their biodiversity by 2100 because of climate change [1]. These ecoregions (Mediterranean Basin, California, Chile, South Africa and Western Australia) are characterized by hot and dry summers, cool and wet winters, and a large annual rainfall variability. They present a high plant and animal diversity spread within numerous vegetation types [2]. Mediterranean forests in particular range from dense foreststo _dehesa_ pastures, with trees scattered over a grass field. They notably display a high proportion of broadleaved species compared to forests found in northern parts of Europe [2]. While plants have adapted to this climate, the Mediterranean biome is still one of the most imperilled, as increased frequency of heat and drought periods as well as growing anthropogenic pressure are threatening the present equilibrium [3; 4; 5]. To identify key threats and guide forest conservation efforts, it is crucial to understand disturbance regimes, species adaptability, and ecological processes [6]: An improved monitoring of the ecoregions is therefore necessary. Essential biodiversity variables (EBVs) have been recently conceptualized by the Biodiversity Observation Network of the Group on Earth Observations (GEOBON) to unify the global monitoring efforts [7; 8]. Foliar pigment contents (such as chlorophylls a + b content C\({}_{ab}\), carotenoids content Car) and leaf area index (LAI) have been identified as traits associated with the ecosystem structure and its relationship to biodiversity. Indeed, leaf pigments are involved in the photosynthetic process, leaf protection, and also contribute to the ecosystem's biogeochemical and nutrient cycles [9; 10; 11], while LAI controls many biological and physical processes such as gross primary production (GPP), rainfall interception, and heat fluxes [12]. Estimating these variables should therefore be a priority to guide conservation policies, as changes in the temporal patterns of LAI and pigment variations could be due to vegetation stress. Field sampling and laboratory measurements of these traits are not acceptable methods to monitor vegetation, as they are time-consuming, spatially limited, and cannot capture inter- and intraspecies variability. Remote sensing, using either multispectral or hyperspectral sensors, has been extensively used to map LAI and leaf pigments over large swathes [13; 14; 15; 16] and allows for recurrent revisits of the study sites. Spectral images are obtained by sensors measuring forest canopy reflectance over a large spectrum, so that slight variations can be detected in the forest canopy's reflected radiation. However, these variations are not exclusively due to the vegetation. Indeed, illumination, local atmospheric conditions, and ground reflectance also have an influence on the measured reflectance [17; 18; 19]. Ground reflectance contribution to the measured spectrum can be most problematic for open canopies or sparse forests (which have very low LAI) such as woodland savannas, which are one of the major plant communities in the Mediterranean biome. Indeed, while at high spatial resolutions vegetation pixel could be clearly identified, reducing ground influence, pixels from images with medium to low spatial resolution will only have a limited canopy contribution to the overall reflectance. Remote sensing pigment and LAI estimation methods can belong to three main families: Empirical-statistical approaches, which fit a model to a set of validation data acquired in-field; physical approaches, which rely on radiative transfer model (RTM) inversions; and hybrid methods, which are still in their infancy and combine the properties of physically-based methods with the flexibility of the empirical-statistical approaches [20].