## 1 Introduction  
Our planet faces global warming and climate change-induced hydro-meteorological challenges that ought to be mitigated [1]. Droughts and extreme heat waves pose a great threat to global vegetation health [2]. Hence, gaining a deeper understanding of the correlations among different climatological, environmental, and biophysical factors has become increasingly crucial [3]. Droughts limit optimal vegetation growth, disturb water relations, and impair water use efficiency in plants [4], and, as these natural disasters are more frequent, it is an urgent issue to monitor the global ecosystem's health [5]. To understand the fundamental drivers of drought evolution, even the smallest changes in the environment demand spatially explicit monitoring. Abrupt increments in temperatures, and uneven temporal distributions of precipitation lead to dryer soils, ultimately causing droughtsthat negatively affect the ecosystem, agriculture, and water resources [6; 7]. Linking spatially explicit vegetation dynamics with climatic factors can facilitate the interpretation of ecosystem feedback to natural stresses [8]. The exploitation of optical satellite data streams opened up possibilities for effectively monitoring the onset, duration, and magnitude of droughs [9]. Throughout the literature on assessing vegetation dynamics and health by means of satellite imagery, vegetation indices were mainly investigated due to their long-term availability and wide range of applications [10]. Alternatively, more advanced metrics, such as quantitative biophysical variables or solar-induced chlorophyll fluorescence (SIF), for evaluating vegetation productivity can yield more consistent findings. While most of the light absorbed by leaves is utilized for carbon assimilation, a fraction is released as heat and emitted as SIF [11]. Quantifiable biophysical variables and SIF not only aid in deciphering the captured SIF signal to estimate photosynthetic activity but also form the essential foundation for monitoring ecosystem dynamics across the spatiotemporal domain [12]. Hence, the sensitivity of vegetation to environmental drivers, understood as the set of physical variables influencing vegetation physiology with statistical meaning, is far better assessed by investigating distinct satellite vegetation products (VP). These include biophysical variables, namely: (1) the fraction of absorbed photosynthetically active radiation (FAPAR) and (2) the leaf area index (LAI). Additionally, (3) SIF and the (4) normalized difference vegetation index (NDVI) are also considered. The targeted VPs can be described as follows:  
The FAPAR quantifies the proportion of solar radiation within the 400-700 nm spectral range that is absorbed by the vegetation, relative to the total radiation absorbed at the surface [13; 14]. It is an important variable, as it contributes to the monitoring of primary productivity [15]. Additionally, the FAPAR serves as an observational constraint when simulating carbon fluxes between vegetation and the atmosphere in terrestrial biosphere models [14; 16]. The LAI is described as half of the total intercepting leaf area per unit of ground surface area [17]. It strongly contributes to canopy photosynthesis and evapotranspiration [15]. The LAI is considered a key component of biogeochemical cycles in ecosystems [18]. SIF, which represents the radiant flux emitted in the wavelength range from 650 to 800 nm, serves as the most direct measurable indicator of the photosynthetic processes in plants [11; 19; 20]. The NDVI is the most extensively used vegetation index used in Earth observation (EO) [21]. Through the computation of the difference between bands that measure red and near-infrared (NIR) reflectance, the NDVI highlights the distinctive signatures of green vegetation [22].