Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model
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 unf...
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MDPI AG
2019-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/9/11/719 |
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| author | Vijaya R. Joshi Kelly R. Thorp Jeffrey A. Coulter Gregg A. Johnson Paul M. Porter Jeffrey S. Strock Axel Garcia y Garcia |
| author_facet | Vijaya R. Joshi Kelly R. Thorp Jeffrey A. Coulter Gregg A. Johnson Paul M. Porter Jeffrey S. Strock Axel Garcia y Garcia |
| author_sort | Vijaya R. Joshi |
| collection | DOAJ |
| description | 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<sup>−1</sup>. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha<sup>−1</sup>) as compared to V5 (RMSE 1158 kg ha<sup>−1</sup>). 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. |
| first_indexed | 2024-12-14T00:35:54Z |
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| id | doaj.art-9713ae71ec494662a7a9c5ea18941f11 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-12-14T00:35:54Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-9713ae71ec494662a7a9c5ea18941f112022-12-21T23:24:39ZengMDPI AGAgronomy2073-43952019-11-0191171910.3390/agronomy9110719agronomy9110719Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop ModelVijaya R. Joshi0Kelly R. Thorp1Jeffrey A. Coulter2Gregg A. Johnson3Paul M. Porter4Jeffrey S. Strock5Axel Garcia y Garcia6Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USAU.S. Arid Land Agricultural Research Center, United States Department of Agriculture-Agricultural Research Service, Maricopa, AZ 85138, USADepartment of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USADepartment of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USADepartment of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USADepartment of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USADepartment of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USAIntegrating 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<sup>−1</sup>. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha<sup>−1</sup>) as compared to V5 (RMSE 1158 kg ha<sup>−1</sup>). 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.https://www.mdpi.com/2073-4395/9/11/719spatial-variabilityprecision agriculturesite-specific calibrationcrop modelingremote sensing |
| spellingShingle | Vijaya R. Joshi Kelly R. Thorp Jeffrey A. Coulter Gregg A. Johnson Paul M. Porter Jeffrey S. Strock Axel Garcia y Garcia Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model Agronomy spatial-variability precision agriculture site-specific calibration crop modeling remote sensing |
| title | Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model |
| title_full | Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model |
| title_fullStr | Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model |
| title_full_unstemmed | Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model |
| title_short | Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model |
| title_sort | improving site specific maize yield estimation by integrating satellite multispectral data into a crop model |
| topic | spatial-variability precision agriculture site-specific calibration crop modeling remote sensing |
| url | https://www.mdpi.com/2073-4395/9/11/719 |
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