|JOSHI, VIJAYA - University Of Minnesota|
|COULTER, JEFFREY - University Of Minnesota|
|JOHNSON, GREGG - University Of Minnesota|
|PORTER, PAUL - University Of Minnesota|
|STROCK, JEFFREY - University Of Minnesota|
|GARCIA Y GARCIA, AXEL - University Of Minnesota|
Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/4/2019
Publication Date: 11/6/2019
Citation: Joshi, V.R., Thorp, K.R., Coulter, J.A., Johnson, G.A., Porter, P.M., Strock, J.S., Garcia Y Garcia, A. 2019. Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model. Agronomy. 9(11). https://doi.org/10.3390/agronomy9110719.
Interpretive Summary: The development of satellite-based sensors that provide geospatial information on crop and soil conditions has been a primary success for precision agriculture. However, further developments are needed to integrate geospatial data into computer algorithms that spatially optimize crop production, predict crop yield, and consider potential environmental impacts of agricultural production. In this study, a computational strategy was developed to combine multispectral data from a satellite sensor with a computer simulation model. The strategy was tested using geospatial data from a maize field in Minnesota. A main outcome of the research was further testing and development of existing software tools and algorithms that synthesize data from multiple sources to estimate crop yield. The results will be useful to scientists and researchers involved in precision agriculture for maize systems in the U.S. Midwest.
Technical Abstract: 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 into 109 spatial zones. In each zone, the CERES-Maize model was run using the GeoSim plugin within QGIS. In the data integration approach, maize biomass 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 stand-alone crop modeling approach.