Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.
Bibliographical noteFunding Information:
This research received no external funding. We are grateful to Farmers EdgeTM for providing in-season RapidEye satellite imagery. We appreciate the help from Lindsey Englar, Nathan Dalman, summer interns, and staff at the University of Minnesota Southwest Research and Outreach Center. We also appreciate the help from Emily Ann Evans, Communications Coordinator of the University of Minnesota Research and Outreach Centers, for proofreading the manuscript.
- Crop modeling
- Precision agriculture
- Remote sensing
- Site-specific calibration