## 4 Discussion
### UAV Observations Reduced Uncertainty and Biases in Sentinel-2 LAI Retrievals  
The two-stage calibration process for Sentinel-2 LAI estimates, which incorporates the upscaled ground measurements via UAV, demonstrated a clear improvement over a single-stage process without UAV data. In the two-stage process, the error was reduced by \(>\)50% in the accuracy of LAI retrievals when validated using independent ground data. As a key variable for quantifying vegetation canopy structure, accurate and consistent LAI estimates can be used directly to determine crop phenology and status (e.g., [12; 57]). Past research has also demonstrated the value of LAI data for updating state variables in process-based crop models in order to improve the spatial and temporal simulation of crop dynamics, including yield and net carbon land-atmosphere exchanges (e.g., [14; 15; 16; 17; 18]).  
The enhancement in LAI retrieval achieved with the two-stage GPR model calibration approach can be attributed to both an increase in the spectral purity and to the volume of samples used to train the GPR LAI retrieval models. With regards to the spectral sample purity, when training the model directly based on ground measurements (i.e., the single-stage calibration), due to accessibility with equipment and efforts to minimise crop damage, LAI measurement points would typically be constrained to the vicinity of the field tramlines (i.e., access tracks), which would bias the training dataset. Indeed, a negative bias in LAI was clear with the single-stage calibration results (Figures 6 and 7). With the two-stage approach, however, the fine-scale UAV observations can resolve the spectral reflectance around and beyond the measurement points more precisely. UAV sampling reduced the influence of tramlines, by sampling the broader field and avoiding access problems for field sampling. These more representative spectral samples can be used to estimate LAI across the spatial extents of the UAV images, including areas that would not otherwise be ground-sampled, and then aggregated to match the resolution of the co-registered Sentinel-2 pixel grid.  
In comparison to alternative approaches for retrieving crop variables from EO spectral reflectance, the performance of machine learning methods, including GPR, can be more sensitive to the number of ground measurements used in the training dataset [28; 29]. Furthermore, unless prior knowledge of the field is available, for example through the identification of management zones [58; 59; 60], the within-field scale variability in in-situ LAI is typically insufficiently sampled. Our two-stage approach addresses these limitations in machine learning LAI retrieval techniques by producing accurate and high-resolution maps of the upscaling ground measurements. These upscaled measurements, which cover multiple overlapping Sentinel-2 pixels, can then be sampled and used to produce a larger training dataset when calibrating the Sentinel-2 retrieval algorithm. For instance, the number of Sentinel-2 pixels that overlapped UAV imagery on Farm 1 and 2 were 80 and 161, respectively, each of which could be used to train the LAI retrieval model.