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


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.

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This work was supported by the National Science Foundation under Grant #0966093, Integrative Graduate Education and Research Traineeship (IGERT) Program on Water Diplomacy at Tufts University. We thank the University of Nebraska – Lincoln’s Carbon Sequestration Program for sharing yield monitor data and the Center for Advanced Land Management Information Technologies (CALMIT) for providing AISA hyperspectral imagery.

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Correspondence to Graham R. Jeffries.

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Jeffries, G.R., Griffin, T.S., Fleisher, D.H. et al. Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning. Precision Agric 21, 678–694 (2020).

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  • Remote sensing
  • Crop simulation
  • Yield mapping
  • Machine learning
  • Yield monitor