# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 1 Introduction
In developing a generic model for estimating canopy chlorophyll content (CCC), [PERSON] et al. [11] demonstrated that the performance of widely used VIs--those that combine near-infrared with red reflectance--are dependent on crop phenology. On the other hand, VIs utilising the Sentinel-2 red-edge and near-infrared bands were less affected by crop phenology and could provide accurate estimates of CCC without re-calibration. [PERSON] et al. [11] estimated CCC based on top-of-canopy reflectance data recorded using ground-based spectral radiometer measurements. However, if applied to top-of-canopy reflectance derived from the Sentinel-2 multi-spectral instrument, it is likely that the validity of this generic retrieval calibration would be dependent on the atmospheric correction procedure applied [24]. Past research evaluating Sentinel-2 for the retrieval of crop variables was often based on the use of VIs calculated from simulated Sentinel-2 data. For instance, [PERSON] et al. [32] investigated the use of the Sentinel-2 red-edge bands for estimating LAI and CCC using airborne hyperspectral data. Research by [PERSON] and [PERSON] [33] and [PERSON] et al. [11] simulated Sentinel-2 data using ground-based narrow-band spectroradiometer measurements, which were then used to estimate crop CCC. Thus,with the exception of [PERSON] et al. (2019) where potato leaf chlorophyll content (LCC) and CCC were estimated from a VI derived from real Sentinel-2 observations, studies involving the use of measurements that matched the spectral characteristics of Sentinel-2 are sparse. Whilst the potential of Sentinel-2 data for supporting agricultural applications has been investigated, a thorough sensitivity analysis of each Sentinel-2 band for deriving crop variables across important crop growth stages is not well documented. This research addresses this knowledge gap by evaluating the characteristics of Sentinel-2 spectral data for retrieving key winter wheat variables--LAI, LCC and CCC. We used multi-temporal data acquired from field campaigns, which included non-destructive direct measurements of LAI and LCC at experimental winter wheat N trial plots. These measurements were also compared to data derived from analysing destructive samples. In conjunction with these ground measurements, we acquired data from an Unmanned Aerial Vehicle (UAV)-mounted multispectral camera comprising nine sensors measuring the same key central wavelengths, ranging from 443 to 865 nm, and with the same bandwidths (full width at half maximum response (FWHM)) as that of the Sentinel-2 multi-spectral instrument. Where cloud cover often limits the availability of optical Earth observation data, our use of a UAV platform ensures observations and allows us to thoroughly explore the sensitivity of the Sentinel-2 bands on the ground data. Our research objectives were, firstly, to characterise the uncertainty and correct biases in the non-destructive ground measurements based on the destructive sample data. Second, to investigate the impact of propagating the quantified uncertainty when training the Gaussian processes regression (GPR) machine learning algorithm, which was used to determine the most informative Sentinel-2 bands for retrieving LAI, LCC and CCC. We further compared the performance of the GPR model to a simpler multivariate linear fit derived from the most informative Sentinel-2 bands for estimating each crop variable.