###### Abstract
LAI retrieval methods from EO data have traditionally included either empirical approaches, through statistical relationships between LAI ground measurements and vegetation indices (VIs), or more physical-based approaches using radiative transfer models (RTMs). Although simple to apply, VIs are often developed for a particular scale (i.e., leaf or canopy-scale; see [20]) and the empirical retrieval of crop variables using VIs can be location-specific [21]. The application of RTMs is often limited by the availability of input parameters and computational expense when simulating the complex interactions of radiation within the crop canopy [22]. Alternatively, machine learning algorithms are increasingly used to develop adaptive relationships between spectral data and crop variables. Recent research has involved applying machine learning approaches to multispectral data for the retrieval of crop variables (e.g., [23, 24, 25, 26, 27]). In comparison to the traditional approaches, the performance of machine learning methods can be more sensitive to the number of ground measurements used in the training dataset. A robust evaluation of the uncertainties of machine learning LAI retrievals from EO data is, therefore, often limited by the availability of ground measurements [28,29]. To match the typical scale at which precision agricultural management could be realistically applied using existing machinery, [PERSON] [5] recommends the use of EO satellite sensors with spatial resolutions of 20 m or less, where we define the spatial resolution of a sensor as the linear dimensions of the ground area represented by each pixel [30]. The recent and freely available data from the European Space Agency's (ESA) Sentinel-2 dual-satellite constellation, with a spatial resolution of up to 10 m and an average global revisit time of 5 days, could be used to estimate LAI and fulfil the requirements of precision agriculture in near real-time [7,31]. In contrast to previous satellite missions (including SPOT-6/7 and Landsat-8/9) the Sentinel-2 multispectral instrument includes measurements at 20-m resolution in three red-edge wavebands (centred on 705, 740 and 783 nm). This red-edge spectral region has an enhanced sensitivity to canopy chlorophyll content [32, 33, 34] and, therefore, has been demonstrated to improve the quantification of LAI [35,36]. Research has assessed the performance of crop LAI retrievals from Sentinel-2 (e.g., [7,36, 37]). For instance, using multiple retrieval approaches, [PERSON] et al. [36] demonstrated R\({}^{2}\) values greater than 0.5 for the estimation of winter wheat LAI from Sentinel-2. These past studies have based this evaluation on ground measurements and not explicitly addressed the uncertainties in LAI retrievals. Multispectral sensors mounted on UAV platforms can offer a relatively low-cost, frequent and flexible solution for acquiring very-high-spatial-resolution imagery (<5 cm), which could be complementary to EO data [38]. The operational use of UAVs for deriving within-field variability across large areas is, however, often restricted by regulations concerning payload and maximum horizontal and vertical distances from the UAV operator. Nonetheless, the availability of UAV multispectral data can allow the derivation of products that can be compared more readily to those derived from satellites. [PERSON] et al. [39] included a statistical comparison between Normalised Difference Vegetation Index (NDVI) maps derived from UAV and high-resolution (6.5 m) RapidEye satellite observations. Although an agreement was achieved between the UAV and satellite data, NDVI uses the visible red spectra, which is strongly absorbed by chlorophyll, becoming saturated at intermediate to high levels of chlorophyll concentration [40]. There is no recorded evaluation of co-located satellite and UAV red-edge measurements, which avoid these saturation issues. Calibrating LAI models from Sentinel-2 red-edge 20-m spectral observations using ground LAI measurements is challenged by major scale mismatches and in-situ sampling problems.