## 4 Discussion
The N-PLS methodology does not handle a high percentage of missing values within its cubic data structure and, it is expected that this approach would be affected in cases where cloud conditions during the target period significantly limit the number of available images. Another constraint that the model was able to deal with was the low number of ground truth samples (in this case, yield loss observations for grape fields). Note that ground truthing remains cumbersome and difficult to manage for many environmental and agricultural studies. Such limitations can influence the values of model outputs, resulting in a lower reliability of the supervised approach. However, this work showed that N-PLS gave relevant prediction results for a complex phenomenon with a very limited number of data (n = 80) in the calibration data set. These results indicated that the algorithm may be suited to other similar satellite systems, such as the Landsat products. However, it is important to keep in mind that while the spectral band specifications are similar between the Sentinel and Landsat systems (cross-calibration was actually performed between the two sensors [44]), the Landsat constellation provides imagery with a lower spatial resolution (30 m for most of the spectral bands) which may be a problem for some applications (e.g., intra-field variability in vineyards) but is not crucial for others. The combination of both Landsat and Sentinel-2 products remains a research question [45]. However, such an image combination may be interesting to explore with the N-PLS method in order to foster some limit when working at the regional scale, i.e., missing values due to weather conditions that prevent a full time-series of the objects being studied to be obtained. While this paper has focused on the monitoring of grapevine heat stress, the identification of the spectral bands that best explain the impact on plant canopy and the development of a local model, which can be applied to predict the spatial extent of the phenomenon, can be transferred to other agricultural applications related to climatic phenomena (e.g., hail or frost). Because of the type of approach, the model should only be valid for the year and the region considered. The resulting model remains specific to the learning base used for the calibration and its generalisation to other vintages and/or other agricultural regions is somewhat limited, especially when dealing with extreme climatic events. However, the results obtained can help facilitate the development of empirical models to be applied to other situations and other vintages. The type of multi-spectral and multi-temporal method presented in this work is expected to be relevant for any application that relies on different spectral domains with a clear temporal evolution. However, its application to more gradual phenomena, such as progressive changes in plant water status over the summer period, would require further testing, as it may be less relevant because changes in plant characteristics may be less obvious. In general, the methodology presented here can be effective for any environmental and agricultural application of Sentinel-2 that involves the monitoring of a sharp temporal evolution and where all spectral information may be useful.