# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 4 Discussion  
The first outcome of this study is that GNDVI showed the highest correlation between VI and corn grain yield. This result is in line with [PERSON] et al. (2016), who reported the same index from aircraft imagery at a small scale. In addition, [PERSON] et al. (2019) and [PERSON] et al. (2018) reported that GNDVI and NDVI reflected the potential on corn yield prediction models. The difference between the examined VIs was not large, except for EVI, but GNDVI showed a clearer trend and the highest correlations. This result is clearer in the 2017 season, while other seasons have more fluctuation in other VIs and less image availability. [PERSON] et al. (2016) reported that GNDVI is more sensitive to the chlorophyll concentration in a wide range of chlorophyll variations. Additionally, they reported that the reflectance range between 520 and 630 nm is the most sensitive for chlorophyll. This range matches with the green band B3 of Sentinel-2 data that ranges between 524 and 596 nm. Meanwhile, [PERSON] et al. (2016) reported that GNDVI was the most positively correlated vegetation index with FPAR for corn canopies which subsequently describes the strong relation to corn biomass and yield. Secondly, GNDVI derived from Sentinel-2 images in the study field could provide a clear description of the corn grain yield spatial variability within-field scale for around 1 month between the end of grain filling and physiological maturity stages. This period is around the physiological maturity stages (R4-R6) at crop ages of 105 to 135 days from planting and mostly between mid-July and mid-August in North Italy. This period was mentioned by [PERSON] et al. (2012) in a Hungarian county scale, [PERSON] et al. (2016), and [PERSON] (2016) in the USA for corn yield prediction. The consistency for this period in the USA, Hungary, and Italy and with multiple scales supports defining it as the most suitable corn yield prediction time. This period is the summer season with less probability of clouds and fog, and Sentinel-2 could provide about six images during this month, which could describe the corn grain yield variability within-field. Machine learning provides overall higher R\({}^{2}\) and more robust results than the VIs approach. Practical results from this study are clearly the application of random forests after 105 days from planting, training the model with a few ground truths sampled over the field, and then applying the trained model over other fields that must be modelled. Evidently, fields must have similar characteristics. The final target is to have a mapped spatial distribution of corn yield over the field of interest that could support farmers for site specific applications. Another result pertaining to the application of machine learning models with Sentinel-2 reflectance timeseries data for crop yield prediction is depicted in Figure 7. Figure 7 reports on the accuracy of a model that was fitted by training with imagery from a certain date (rows), applied to imagery from all other dates (columns). The diagonal row in this table represents fitting and modelling on the same image and, as expected, provided the best performance. Other combinations show how well a model that is fitted on one image date performs on another image date. Figure 7 shows that the model must be used in the same conditions of crop growing stage to replicate acceptable accuracies. Machine learning methods are nonparametric methods and have many advantages, as mentioned here and in other studies, but are not easily interpreted and, therefore, replication of a model with imagery from other dates or different conditions is not advised. Another aspect worth discussing is that correlations became worse from 2016 to 2018 for VIs. In machine learning, it is clear that compared to other models, 2016 provided the highest coefficients of determination (Figures 6 and 7). In Figure 7, the 2016 images (columns) provided better accuracy values with other models (rows) than other years, in particular, in August, as the columns show lower MAE values.