# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 5 Conclusions  
This study has evaluated the Sentinel-2 satellite multispectral instrument spectral bands for the estimation of winter wheat variables--LAI, LCC and CCC--required for supporting precision agricultural technologies. Where past research has often used synthetic Sentinel-2 data within the growing season, here, we used data from a UAV-mounted multispectral camera with sensorsmatching the key Sentinel-2 wavebands. The acquisition of UAV multispectral data was carried out in coordination with ground measurements. These measurements, comprising destructive and non-destructive sample analysis data, were used to calibrate and validate the performance of a GPR machine-learning algorithm we applied to identify the most informative spectral bands for estimating each of the biophysical variables. The ground measurements were also used to quantify uncertainty that was propagated into the GPR model training data.  
Overall, we have demonstrated a high retrieval accuracy of the variables when using the most informative Sentinel-2 bands (mean R\({}^{2}\) = 0.86). The Sentinel-2 red-edge and near-infrared bands were identified as being the most informative, particularly for LAI and CCC. The propagation of uncertainty in the ground measurement reduced the number of most informative bands, indicating an overfitting of the GPR model when uncertainty was not properly accounted for.  
In comparison to previous satellite missions, the results we present highlight the potential of Sentinel-2 spectral data within an operational farm-scale decision support system. Future research should include testing the robustness and characterizing the uncertainty of the GPR modelling approach when applied to data acquired from the Sentinel-2 platform, including the uncertainty linked to the spatial scaling of these estimates to the resolution of Sentinel-2 multi-spectral instrument (i.e., 10 and 20 m). Furthermore, the use of GPR modelling, which provides a predictive mean and variance, would be an ideal approach for investigating the propagation of uncertainty from ground measurements to the scale of the Sentinel-2 sensor.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/17/2050/s1](http://www.mdpi.com/2072-4292/11/17/2050/s1)]([http://www.mdpi.com/2072-4292/11/17/2050/s1](http://www.mdpi.com/2072-4292/11/17/2050/s1)).  
[PERSON] and [PERSON] undertook the acquisition and processing of fieldwork data. [PERSON] carried out the analysis of results with input from all other co-authors. [PERSON] prepared the draft of this manuscript with suggested edits from all other co-authors.  
This research was funded by the joint Biotechnology and Biological Sciences Research Council (BBSRC) and National Environment Research Council (NERC) Sustainable Agricultural Research and Innovation Club (SARIC) initiative (grant numbers: BB/P004628/1 and BB/P004458/1).  
Technical support was provided by [PERSON] from the University of Edinburgh's Airborne GeoSciences Facility. We are also grateful for the support received from the NERC National Centre for Earth Observation (NCEO) Field Spectroscopy and Geophysical Equipment facilities.  
The authors declare no conflict of interest.