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Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning

Jeffries, Graham R., Griffin, Timothy S., Fleisher, David H., Naumova, Elena N., Koch, Magaly, Wardlow, Brian D.
Precision agriculture 2020 v.21 no.3 pp. 678-694
Zea mays, artificial intelligence, calibration, corn, crop models, crop yield, data collection, growers, irrigation, prediction, remote sensing, simulation models, yield forecasting, yield mapping, Nebraska
Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324–333, 2015) was developed and tested for estimating sub-field maize (Zea mays L.) yields at 10–30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R² values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.