# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 4 Discussion
There are possible explanations for 2016 being a better year for predicting yield values that can be brought forward, even if at this stage, they are only hypotheses. One important point to consider is the accuracy of the measured yield values. The measured yield values are used as \"truth\", but as all measures go, they are prone to a certain degree of error. The method used for measuring provides about 10 times higher accuracy than prediction models, but it can record values incorrectly, such as explained in the materials section, i.e., border effects, harvester passing twice. Then, it might be possible that seasons 2017 and 2018 yield data underwent higher errors due to unpredicted orunseen sensor or measurement distortions such as GPS and yield sensors' inaccuracies or calibration errors from year to year. This can explain the overall difference in correlation and prediction accuracy between years, as the measured yield is fixed per year. [PERSON] and [PERSON] (2017) have found R\({}^{2}\) up to 0.4 for corn yield prediction from RS data, stressing the importance of the quality of the field measures. Another possible and probably more realistic hypothesis can be related to atmospheric conditions, as the images in 2017 and 2018 might have suffered from more opaque atmospheric conditions, not so much to be detected as cloud probability by the sen2 cor algorithm used in Sentinel-2 processing. Even the relation between yield and VIs are affected by spatial and temporal constraints, as reported by many researchers (2018), but it could be used for identifying management zones ([PERSON], 2017; 2018), evaluating within-field variability and subsequently, the need for PA practices. According to the study results, Sentinel-2 could assess only 40-60% of field variability: such percentage is clearly unsatisfactory if seen at single pixel level, but is fully acceptable in common PA practices, where management zones are to be defined. Indeed, in this case, a relatively high number of values are averaged within each MZ (typically larger than 1 acre, corresponding to at least 40 pixels) eventually allowing reduction of variability and a corresponding increase of overall robustness of the method. Within-field variability is common due to variations in soil type, topography, nutrient level, and many other factors of agricultural fields. Mapping this variability, delineating to homogeneous zones, and applying the variable rate application of agricultural inputs leads to more profitable and efficient management practices. In addition, the archived Sentinel-2 images could provide an important source for monitoring fields' history and the cumulative production for each zone. Recent studies reported that the profit of PA applications ranged between -30 $/ha and 70 $/ha and about 25% of studies reported economic losses ([PERSON] et al., 2018). In view of this, evaluating field spatial variability on previous seasons from archived satellite images could be a simple approach to evaluate the actual need for site-specific management practices. [PERSON] (2018) highlighted the necessity of integrating archived satellite images for PA decision support systems and this approach could be a promising application in this field. Moreover, monitoring yield variability within fields through archived satellite images could be applied for different crops, varieties, countries, and farming management practices to widen the use of archived data.