Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

Even within managed crop systems, there is considerable and important within-field variation in LAI at scales finer than the resolution of current satellite imagers.
In this scientific research it is demonstrated that UAV multispectral observations at the cm scale, acquired from a sensor designed to match Sentinel-2 spectral bands, improve interpretation of the satellite signal.
Furthermore, the fine-scale resolution of the UAV sensor provides a tool for accurately upscaling LAI ground measurements, which were collected in coordination with the UAV flights, to satellite resolution. The within-field variance in spectral data resolved from the UAV observations was linked to wheat growth stage. Consequently, the Sentinel-2 and UAV platform data were more comparable at the later growth stages, when the vegetation canopy appeared more homogeneous due to a reduced influence of bare soil.
Calibrating models used to retrieve LAI from Sentinel-2 observations directly from ground measurements performed poorly and were unable to explain the variance in LAI throughout the growing season. On the other hand, our novel two-stage model calibration, involving the use of upscaled UAV LAI estimates, demonstrated a clear improvement in the accuracy of LAI retrievals from Sentinel-2 data, reducing bias strongly.
This study has highlighted the value of UAV observations for eectively providing a link between point measurements on the ground and 20-m resolution multispectral observations made from the Sentinel-2 satellite.
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