# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 4 Discussion
### Sentinel-2 Bands and GPR Modelling for Parameter Retrievals  
Using UAV-based Sentinel-2 band observations and ground measurements, we assessed the capacity of the Sentinel-2 spectral bands for the retrieval of winter wheat LAI, LCC and CCC. We acknowledge that our GPR models for retrieving each of these variables were limited to one winter wheat season at our experimental field site. The calibration of the models was, however, carried out across multiple dates and N treatments; thus, covering a range of developmental stages and nutrient stresses. We would, therefore, expect the models to be broadly applicable when applied to retrieve the key variables from similar winter wheat varieties at alternative sites. Figure 5: Independent GPR model evaluation for estimating **(a)** LAI, **(b)** LCC and **(c)** CCC. Note: Fit line (black line) is defined using reduced major axis where \(y\)-axis error bars are derived from the standard deviations of the GPR model estimates and \(x\)-axis error bars represent the standard deviations of the corresponding non-destructive ground measurements (available for LAI and LCC only). Figure 6: Independent evaluation of a multivariate linear regression model for **(a)** LAI, **(b)** LCC and **(c)** CCC. We exploited the band-ranking capabilities of a GPR machine learning algorithm and, in doing so, we objectively identified the most sensitive Sentinel-2 bands for retrieving each of these wheat variables. When we included the uncertainty of the ground measurements used in the training of the GPR models, it was found that two to three bands were sufficient for estimating the variables (Figure 3). Specifically, for the estimation of LAI and CCC without propagating the uncertainty, the optimum GPR model included both the red-edge bands (705 and 740 nm) and two of the near infra-red bands (783 and 865 nm). When propagating the uncertainty, the most informative bands included the red-edge band at 705 nm and near infra-red band at 865 nm only. This result suggests overfitting of the GPR model when variance in the training data is not accounted for and, thus, highlights the importance of propagating uncertainty in ground measurements when developing remote sensing retrieval algorithms. Being able to provide reliable estimates of the variables from a small number of well-defined Sentinel-2 bands also has the advantage of a reduction in processing time when generating high-resolution empirical retrieval maps of the variables over large areas [20]. More broadly, the ability to estimate multiple variables of a specific crop type using the essential spectral bands also has implications for the development of compact and lightweight UAV multispectral cameras [52]. Where the red-edge and near infra-red bands were favoured for the estimation of LAI and CCC, for the retrieval of LCC when the GPR model was developed with the propagated uncertainty, the optimum bands were composed of the visible blue (490 nm), green (560 nm) and near-infrared bands (783 nm). The sensitivity of the green and infrared bands for the estimation of LCC has previously been demonstrated [9; 27]. Specifically, the Green Chlorophyll Vegetation Index (near infra-red/green; [31]) has successfully been applied in several studies for deriving crop chlorophyll content [53; 24; 54; 55]. Research by [PERSON] et al. [56] involved the analysis of winter wheat spectral reflectance under different N applications and demonstrated that bands centred around the green and near-infrared spectral regions were sensitive to the treatments, whereas the blue band was comparatively less sensitive. In our analysis, the inclusion of the blue band in the most informative band combination for LCC was, therefore, unexpected, but did appear to improve the performance of the GPR model when compared to using the green and near-infrared bands only. Our analysis also demonstrated a relationship between blue reflectance and LCC measurements (Figure 4).