# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 5 Conclusions  
A field study was conducted to investigate the possibility of monitoring corn grain yield within a 22-ha field using Sentinel-2 satellite images. Actual corn yield maps for three seasons were correlated with different vegetation indices and crop ages through the implementation of machine learning techniques. Monitoring the within-field variability of corn yield was possible and the R\({}^{2}\) value reached more than 0.5 in some cases. The main findings can be summarized as follows:  
* GNDVI was the most correlated VI with corn grain yield at the field scale;
* the most suitable time for corn yield forecasting was during the physiological maturity stages (R4-R6) between 105 and 135 days from the planting date, i.e., between mid-July and mid-August;
* this period is not only the most correlated, but also is provides a higher number of satellite images due to reduced cloud events in the area considered;
* Random Forests was the most accurate machine learning technique in corn yield monitoring with R\({}^{2}\) values almost reaching 0.6 in an independent validation set;
* the Random Forests method works best when trained with imagery close to the above-mentioned most suitable time for forecasting; applying it to images at different crop stages is not advised.  
The overall question \"can Sentinel-2 imagery be used to predict corn yield values?\" can be answered by saying that in a scenario with expected yields between 9 and 18 ton/ha, a likely prediction with a 10% error can be expected using a Random Forests model trained with a few ground samples taken between 105 and 135 days of crop age. Without ground samples of yield values, thus, with less effort on the ground, one can validly use GNDVI at the same crop age range for understanding the spatial variation of yield. Future research will use more tests to understand the ideal combination of volume of training data and the spatial distribution of training data for understanding the best economical combination for an accurate prediction of corn yield with the least effort in terms of surveying training data for the model.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/23/2873/s1](http://www.mdpi.com/2072-4292/11/23/2873/s1)]([http://www.mdpi.com/2072-4292/11/23/2873/s1](http://www.mdpi.com/2072-4292/11/23/2873/s1)), S1: All correlations between VIs and corn grain yield for all satellite image dates.  
Conceptualization, A.K., F.M. and F.P.; formal analysis, A.K. and F.P.; data curation, A.K., M.S. and S.G.; writing--review and editing, A.K. and F.P.; supervision, F.M. and F.P.  
This research was financially supported by the Land Environment Resources and Health (L.E.R.H.) doctoral course ([[http://www.tesaf.unipd.it/en/research/doctoral-degrees-phd-lerh-program](http://www.tesaf.unipd.it/en/research/doctoral-degrees-phd-lerh-program)]([http://www.tesaf.unipd.it/en/research/doctoral-degrees-phd-lerh-program](http://www.tesaf.unipd.it/en/research/doctoral-degrees-phd-lerh-program))) and by the CARIPARO foundation (AGRIGNSSVeneto--Precision positioning for precision agriculture project).  
The authors declare no conflict of interest.