###### Abstract  
Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency's Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10-20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates--coinciding with different wheat growth stages--that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CI\({}_{\text{red-edge}}\)) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CI\({}_{\text{red-edge}}\) values increased with growth stage--R\({}^{2}\) ranged from 0.32 (stem elongation) to 0.75 (milk development). The CI\({}_{\text{red-edge}}\) variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R\({}^{2}\) = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R\({}^{2}\) = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by \(>\)50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types. S 202021218430.3390/rs121118430.3390/rs121118430.390/rs121118430. can be addressed through precision agriculture, defined as a set of technologies that integrate sensors, information systems, machinery and informed management to improve production by accounting for spatiotemporal variability and uncertainty in agricultural systems [3,4]. Remote sensing, from satellite Earth observation (EO) and Unmanned Aerial Vehicle (UAV) platforms, can provide frequent and instantaneous information on crop growth at sub-field scales, which could be used to support precision agriculture [5]. Leaf area index (LAI, defined as half of the total green leaf area per unit horizontal ground surface area [6]) is one of the most important crop variables that can be retrieved from remotely sensed data [7,8]. LAI characterises crop canopy structure and is, thus, linked to canopy-scale processes, including evapotranspiration, photosynthesis, respiration and the interception of precipitation and solar radiation [9,10]. Accurate estimates of LAI, which typically range from 2 to 6 m\({}^{2}\) m\({}^{-2}\) across the spring and summer months for wheat [11], can support precision agriculture, for instance by informing variable fertiliser applications [12]. Being a key state variable within most process-based crop models [13], EO-derived LAI observations have also been used within model-data assimilation frameworks, including those used for estimating yields and net carbon land-atmosphere exchanges (e.g., [14, 15, 16, 17, 18, 19]).