###### Abstract
The mismatch arises because typical in-situ measurements of LAI using gap-fraction approaches (e.g., [22]) only record the canopy structure close to the user, i.e., within ~1 m radius, an order of magnitude smaller than the satellite pixel. The heterogeneity in crop LAI at a scale of 2 to 3 m would therefore introduce potentially strong biases into a calibration [41]. Accessing multiple points within a field in order to collect a sufficient number of LAI measurements to capture the sub-pixel variability would be impossible without the observer causing some damage to the crop. To avoid crop damage, observers tend to collect data around the edges of fields or along farm vehicular access tracks (referred to hereafter as tramlines). Bias problems in retrievals will be amplified by this non-random sampling of crop variability. Thus, recent research has aimed to use UAV sensors to bridge this scaling gap between ground and EO data, including the calibration of EO-derived VIs using UAVs (e.g., [38; 42]). However, there has been a scarcity of studies that have applied a UAV calibration to retrieve measurable biophysical parameters describing the crop canopy, such as LAI. Another weakness in these past studies is that UAV sensors have measured different spectra to the satellite. UAV sensors should have the same spectral bands to those of satellite-based sensors for robust scaling studies [43]. Here, for the first time, we show how a UAV multispectral sensor with bands matching those of Sentinel-2 can be used to calibrate Sentinel-2 retrievals of crop LAI, with a robust characterisation of error. In conjunction with LAI ground measurements at multiple field sites and growth stages, we deployed a UAV-mounted multispectral sensor which has the same red-edge and near-infrared central wavelengths as Sentinel-2. These UAV sensor data, once calibrated, allow upscaling of ground LAI measurements over larger spatial extents at very high spatial resolutions (around 5 cm). We spatially aggregated these LAI data to allow a direct spatial match to observations retrieved from Sentinel-2 at 20 m. Using a machine learning approach, we further evaluated the performance of a novel two-stage Sentinel-2 LAI retrieval calibration that incorporates these up-scaled UAV LAI estimates. Based on UAV multispectral observations, [PERSON] et al. [26] performed a robust analysis of Sentinel-2 bands for retrieving wheat canopy variables, identifying the sensor's red-edge 705-nm and near-infrared 865-nm bands as being the most informative for estimating wheat LAI. This present study extends these findings by investigating the error associated with the retrieval of wheat LAI from Sentinel-2 at the native resolution of these spectral bands (i.e., 20 m). We performed a series of experiments in order to answer the following research questions:  
1. How do maps of red-edge VIs compare between high-resolution UAV imagery and coarser-spatial-resolution Sentinel-2 data throughout the winter wheat season? 2. How do observation scale and scale mismatch (i.e., between in-situ data and satellite resolution) influence the accuracy of wheat LAI estimates? 3. To what extent can models calibrated with high-spatial-resolution UAV-derived winter wheat LAI estimates reduce uncertainty and bias in the retrieval of LAI from Sentinel-2 data? We discuss the potential for UAVs to support the improved calibration of vegetation parameters on the basis of this research.