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Open AccessFeature PaperArticle

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

1
Department TESAF, University of Padova, viale dell’Università 16, 35020 Legnaro (PD), Italy
2
Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, Giza 12619, Egypt
3
Porto Felloni srl, 44023 Lagosanto (FE), Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2873; https://doi.org/10.3390/rs11232873
Received: 15 October 2019 / Revised: 22 November 2019 / Accepted: 28 November 2019 / Published: 3 December 2019
Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6). View Full-Text
Keywords: Sentinel-2; precision agriculture; machine learning; vegetation indices; corn yield; within-field variability; digital farming Sentinel-2; precision agriculture; machine learning; vegetation indices; corn yield; within-field variability; digital farming
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MDPI and ACS Style

Kayad, A.; Sozzi, M.; Gatto, S.; Marinello, F.; Pirotti, F. Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques. Remote Sens. 2019, 11, 2873.

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