## 3 Results
### Sentinel-2 LAI Retrieval Calibration Approaches  
The two-stage LAI calibration approach for Sentinel-2 had a much better agreement with the validation dataset (R\({}^{2}\) = 0.88) than the single-stage calibration (R\({}^{2}\) = 0.29) (Figure 6). The two-stage approach could, thus, explain \(\sim\)3 times as much of the variation among 20-m pixels. The error of the single-stage calibrated model (NRMSE = 17%) was more than twice that of the two-stage calibration (NRMSE = 8%). The single-stage calibration also had a large negative bias (regression slope = 0.35) when compared to the two-stage model (0.81). The single-stage calibration, thus, has major problems in identifying changes in LAI, and picking out high and low values accurately.  
The observed-estimated LAI bias values in the distribution for the single-stage calibration were broader and slightly positively skewed when compared to that of the two-stage calibration (Figure 7) when using the same independent calibration/validation datasets. The two-stage calibration bias had a narrower and more symmetrical distribution. The single-stage calibration has a mean LAI error of 1.0, compared to 0.45 for the two-stage process. Furthermore, the single-stage approach produces very poorly constrained estimates in some cases, shown by the long tails of the error distribution.  
Figure 5: Scatter plot comparisons between LAI retrieval models calibrated with UAV multispectral data aggregated at 2 m (**left**) and aggregated to a 20 \(\times\) 20 m square grid cell (**right**). Both calibration approaches are compared to LAI ground measurements acquired at 80 locations. Plot includes a liner fit line (solid green line) with 68% confidence interval (shaded green area). The dashed gray line represents the 1:1 line.  
The Sentinel-2 two-stage calibration was capable of resolving the within-field spatial variability in LAI, whereas the single-stage calibration only captured less than half of this variability. We compared LAI maps of sample fields derived from the different calibration approaches to that from the fine-scale UAV LAI estimates (Figure 8). The variability captured by the two-stage approach was visually comparable to the fine-scale UAV LAI estimates. In particular, the two-stage approach better resolves areas of high and low LAI. The single-stage calibration, on the other hand, appeared to underestimate LAI at higher values.  
The bias and distribution of LAI estimates derived from the two calibration approaches for the field samples, in comparison with the UAV LAI data, can also be seen in the distribution plots (Figure 8). The shape of the two-stage LAI distribution matches the UAV estimates much closer when compared to that of the single-stage calibration. The single-stage calibration LAI estimates also had a much narrower distribution, which generally peaked at LAI values less than the two-stage calibration estimates, indicating a consistent and strong bias.  
Figure 6: Independent validation of Sentinel-2 LAI (S2-LAI) to UAV spatially aggregated (20 m) LAI estimates (UAV-LAI). S2-LAI include estimates from a direct single-stage LAI retrieval calibration using ground data (**left**) and a two-stage calibration using the spatially up-scaled (20 m) UAV LAI estimates (**right**). Dashed gray line represents the 1:1 line.  
Figure 7: Comparison of observed–modelled LAI bias probability distribution function plots for the single-stage (blue line and shading) and two-stage (green line and shading) calibration approaches.