## 1 Introduction
It is also common knowledge that a NIR spectra obtained from forage samples have absorption bands that are correlated with specific compounds. The spectra regions between 1650-1670 and 2260-2280 nm are correlated with C-H bonds in lignin and cellulose [14], and the 2100-2200 nm region absorption bands are correlated with protein functional groups [15]. Though the NIR spectroscopy technique has been widely used in Europe to measure feed quality and to predict the nutritional value of forage [16], few studies related to its application in dryland biodiverse pastures of the Mediterranean region can be found, thus highlighting the interest of this work. On the other hand, in recent years, satellite data have been increasingly deployed for RS applications due to their ready access, low cost, and geographic scale [8]. Satellite images can provide synoptic information on vegetation characteristics of large areas [17]. RS, based on the correlation between vegetation indices obtained from satellite-images and some crop characteristics, is becoming an increasingly appealing technology [7]. The NDVI is related to high levels of chlorophyll, which is correlated with vegetative vigor and, consequently, with high CP levels [18]. These indices that are associated with more traditional indicators provide farmers with the information needed for formulating the most appropriate strategy for management and feeding of the livestock [19]. Over the past few years, there has been a notorious improvement in the optical resolution of satellite images, providing now a 10-30 m pixel resolution and a five-day repeat cycle [1]. Therefore, RS techniques may progressively develop into important tools for the monitoring and management of such complex ecosystems while allowing for, when necessary, the reconstruction of historical trends through the use of satellite image time series [7]. Nonetheless, the use of optical satellite imagery requires a clear, cloud-free sky that may not always be the case during the rainy season in temperate and rainy regions. In the Montado, as in other forestry ecosystems, there is an added limitation due to lack of visibility of the plants located under the tree canopies. This limitation of RS-based methods creates an opportunity for the use of PS to monitor the understory vegetation [5; 20]. These two methods can be used in a complementary way, with PS providing a greater resolution and access to the understory even on cloudy days [5]. Figure 1, based on data published by [PERSON] et al. [1], shows the interest of NDVI measurement (by RS or PS) as an indicator of the seasonal evolution of pasture quality over the vegetative cycle and of the inter-annual variability that might result from the precipitation pattern. The three years under consideration in this study showed different patterns of rainfall distribution (Figure 1a): 2015/16 with regular rainfall distribution, 2016/17 with a relatively dry spring, and 2017/18 with a very rainy spring. Considering the NDVI reference value of 0.6 (Figure 1b), which was a sudden and significant decrease, the pasture CP consequently contents fell below the animal maintenance [1], it is evident that animal supplementary feed is required between the end of spring and the beginning of autumn, and it can last between four and six months. This inter-annual variability reinforces the interest in having expeditious tools to support farmers in the dynamic management of animal grazing. The purpose of this study was to evaluate two technological and expeditious approaches (Figure 2) for estimating and monitoring the evolution of the quality parameters in Mediterranean pastures during the 2018 and 2019 growing seasons: (i) NIRS combined with multivariate data analysis and (ii) RS using images provided by Sentinel-2 satellite to calculate the NDVI and the normalized difference water index (NDWI).