# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 4 Discussion
### Potential of Sentinel-2 for Supporting Agricultural Management  
The recent availability of open-access data from the Sentinel-2 dual-satellite constellation provides opportunities for the development and improvement of spatial data products that can support precision agriculture. Where past Earth observation satellite sensors (including SPOT-6/7 and Landsat-7/8) have relied on observations in the visible and near-infrared wavebands, this research has demonstrated that the two Sentinel-2 red-edge bands were frequently included within the most sensitive band combinations for retrieving key crop variables. We have demonstrated the potential of the Sentinel-2 bands for providing more accurate estimates of LAI, which would be of value for improving the efficiency of crop model-data assimilation approaches, particularly when spatially upscaling model estimates from fields to regional extents [3; 58]. In this research, we have shown a high correlation (R\({}^{2}\) = 0.86) between LCC and leaf N along with a high retrieval accuracy of LCC and CCC using Sentinel-2 bands. Being able to provide reliable estimates of LAI and chlorophyll content, i.e., as a proxy for N, is particularly useful for farmers when deciding on mid-season N fertilisation applications [18]. The traditional uniform approaches to fertiliser N applications are economically and environmentally inefficient, since they inherently ignore spatial heterogeneities in topography and soil properties [59; 60]. The availability of timely and spatially explicit estimates of crop N content could be a crucial input for variable rate applications concerned with optimum N usage. Because of links between leaf N content and crop yields [50; 61], within-field estimates of N could also be used to estimate crop yields. For use on an operational basis, the overall extent to which Sentinel-2 data can reliably support precision agricultural applications is, of course, dependent on the frequency of available cloud-free imagery. For our field site and growing season, we identified 19 cloud-free images. Since the period of time between successive observations ranged from 7 to 35 days, with an average of 17 days, this would not meet the biweekly observations recommended in previous research for tracking the temporal dynamics of crop growth [22; 23]. However, these cloud-free images were within five days of each of the five growth stages targeted in this research and would be expected to be of value, particularly if the retrieved variables were used to update daily process-based crop models estimates. The traditional approaches for variable retrieval use the visible red and near-infrared bands, which correspond to Sentinel-2 bands 4 and 8 that have a spatial resolution of 10 m (Table 2). On the other hand, the Sentinel-2 red-edge bands have a spatial resolution of 20 m. Although it is clear from this research that the red-edge bands improve estimates of LAI, LCC and CCC, for practical applications, the use of these bands would be at the expense of a reduction in spatial resolution. [PERSON] and [PERSON] [62] sampled from a continuum of increasingly coarser pixel sizes in order to investigate spatial resolution requirements for the image classification of different crop types. This research demonstrated that pixel sizes of around 117 m were sufficient to identify winter wheat, but this was found to be dependent on landscape heterogeneity (i.e., field size and shape) and timing within the growing season. Further research by [PERSON] et al. [63] showed stability in an LAI-VI relationship when spatially aggregating IKONOS satellite imagery pixels from 12 to 36 m. In our research we would, therefore, not anticipate any substantial increases in uncertainty when retrieving the crop variables at 20 m; the increase in performance when using the Sentinel-2 red-edge bands are likely to outweigh any negative impacts of a reduction in spatial resolution.