## 1 Introduction  
As extensive changes in biodiversity are occurring globally, ecosystem studies have been focusing on investigating how species composition, distribution, and abundance affect ecosystem functioning. To improve our understanding of such interactions, a deeper insight into processes that regulate vegetation biodiversity and productivity is fundamental [1]. Plant primary production (expressed as above-ground biomass) and productivity (the rate of new biomass generation, estimated through biomass harvesting or expressed as Net Ecosystem Exchange, NEE; [2]) are key metrics of ecosystem functioning. Although positive correlations between biodiversity and productivity (BPRs) were often highlighted in grassland ecosystems [2; 3; 4; 5], other studies showed negative relationships [6; 7; 8]. A humped-back model was recently proposed for high above-ground biomass rates based on the hypothesis that plant diversity peaks at intermediate productivity, while at low and high productivity biodiversity is lower, as only a few species are able to deal with stress and competition, respectively [8]. Uncertainties about the methodologies beneath BPR studies also exist. The analysis of the factors affecting BPRs is often carried out at small scale using a \"within-site\" approach to keep environmental conditions constant as much as possible among treatments [9] and is often based on manipulation of species richness. To this regard, [PERSON] et al. [6] highlighted the limits of manipulations experiments and the need for more analyses focused on mature natural ecosystems. The scale-dependency of BPRs is under intense debate [7; 9; 10]. Remote Sensing (RS) techniques provide strong potential for across-scale BPRs spatial patterns investigations: Since ground surveys are time-consuming and costly, larger areas can be analyzed using remotely-sensed proxies of diversity and productivity, leading to innovative insights in ecological applications. However, only sporadic experimental RS studies have been carried out to analyze the scale-dependency of optical diversity and productivity relationships. Airborne hyperspectral sensors are able to retrieve and combine detailed information on biochemistry and structure and represent a solid basis for across-scale investigations and for the validation of existing and upcoming satellite observations [2]. Solar radiation interactions with vegetation in different spectral domains provide different information about the observed ecosystems. The visible (VIS) spectral region is dependent on biochemistry and pigments absorptions, while the relatively high reflectance in the near-infrared (NIR) is linked to structure and internal leaf and canopy scattering [11]. By combining structural and biochemical information obtained from light reflected at different wavelengths, optical data can be adopted to map both vegetation productivity and biodiversity [2; 12; 13]. Canopy structure can be described by structural traits such as Leaf Area Index (LAI), aboveground biomass (AGB), and the proportion of photosynthetic and non-photosynthetic elements. In herbaceous plants, AGB is a spatially and temporally-dynamic trait retrievable from RS observations [14].