## 1 Introduction  
Leaf area index (LAI) is perhaps the most crucial plant biophysical trait which provides valuable information on vegetation structure and functioning [1; 2]. It is an essential input into models that determine ecosystem productivity and describe energy and mass exchanges in the plant, soil, and atmosphere [3]. LAI plays a key role in climate modelling, and biodiversity monitoring hence is recognised as an essential climate variable [4] while proposed as an essential biodiversity variable [5; 6]. The strong relationship that exists between LAI and other plant structural and functional parameters such as the fraction of photosynthetically active radiation (FPAR), specific leaf area (SLA), yield, nitrogen content, biomass, aboveground net primary productivity (NPP), canopy cover fraction and stem density [7; 8; 9; 10; 11; 12; 13; 14; 15; 16] highlight its crucial contribution towards monitoring vegetation growth, productivity and generation of relevant biodiversity information. Nevertheless, in situ measurements of LAI whether using direct (mainly destructive) measurements or indirect methods which are largely based on optical instruments [17; 18; 1; 19] and more recently on smartphones [20; 21; 22] are costly, scant and prone to measurement inconsistencies, even though they potentially offer the most accurate information at plot level. Remote-sensing data play a prominent role in estimating and mapping the spatial distribution of LAI. The spectral reflectance of vegetation canopies, neglecting the peripheral factors such as variable solar illumination and sensor observation angles, soil and atmosphere, are primarily altered by variation of biochemical and biophysical traits. While biochemical traits at the leaf level generally suffer from poor signal transmission to the canopy level [23; 24; 25; 26; 27], biophysical traits such as LAI has significant contribution in canopy reflectance spectra [28; 29; 30] and its' magnitude [31]. Until recently, the scarcity and cost of hyperspectral data and the coarse spectral information of broadband satellites were the major shortcomings in estimating and mapping LAI accurately from remote-sensing data. The advent of new satellites such as Sentinel-2 with a revisit time of five days (with combined use of Sentinel-2A and Sentinel-2B) has opened prospects of obtaining reasonably comprehensive spectral information for improved estimation of vegetation traits over large spatial extents regularly throughout the season. In addition to the visible, near-infrared (NIR), and shortwave infrared (SWIR) bands, Sentinel 2 has a series of spectral bands in the red-edge region. The red-edge bands are the unique features that distinguish satellites such as Sentinel-2 and RapidEye from other currently available multispectral satellites. The red-edge spectral region is recognised as important for estimation of vegetation biochemical and biophysical traits, particularly from hyperspectral data [32; 33; 34; 35; 36; 37]. Although LAI as a biophysical parameter has shown a good correlation with data from the red edge region in several hyperspectral studies [38; 39; 40], nonetheless, some contradictory results have been observed by others [41; 42; 43; 44]. These inconsistent observations in the literature call for further investigation of the sensitivity of this relationship utilising red edge spectral data of the recently available broadband satellites such as RapidEye and Sentinel-2. The retrieval of LAI from remote-sensing measurements using either empirical or inversion of physical models has pros and cons [3; 45]. However, in regional and global studies, where in situ data is impaired, inversion of physical models involving one or more radiative transfer models (RTM) is the conceivably superior approach [45]. These models must be inverted in order to retrieve plant traits from remote-sensing data [46]. A number of algorithms for RTM inversion have been developed [45], whereas the commonly used method is the look-up table (LUT) [47; 48].