Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model

Vijaya R. Joshi; Kelly R. Thorp; Jeffrey A. Coulter; Gregg A. Johnson; Paul M. Porter; Jeffrey S. Strock; Axel Garcia y Garcia

doi:10.3390/agronomy9110719

Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model

Vijaya R. Joshi, Kelly R. Thorp, Jeffrey A. Coulter, Gregg A. Johnson, Paul M. Porter, Jeffrey S. Strock, Axel Garcia y Garcia

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

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 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.

Original language	English (US)
Article number	719
Journal	Agronomy
Volume	9
Issue number	11
DOIs	https://doi.org/10.3390/agronomy9110719
State	Published - Nov 6 2019

Bibliographical note

Funding 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.

Publisher Copyright:
© 2019 by the authors.

Keywords

Crop modeling
Precision agriculture
Remote sensing
Site-specific calibration
Spatial-variability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access

10.3390/agronomy9110719

OpenUrl availability

Full text

Cite this

@article{527ea20a4a094268ab5dffbbf53cf260,

title = "Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model",

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 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.",

keywords = "Crop modeling, Precision agriculture, Remote sensing, Site-specific calibration, Spatial-variability",

author = "Joshi, {Vijaya R.} and Thorp, {Kelly R.} and Coulter, {Jeffrey A.} and Johnson, {Gregg A.} and Porter, {Paul M.} and Strock, {Jeffrey S.} and {y Garcia}, {Axel Garcia}",

note = "Funding 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. Publisher Copyright: {\textcopyright} 2019 by the authors.",

year = "2019",

month = nov,

day = "6",

doi = "10.3390/agronomy9110719",

language = "English (US)",

volume = "9",

journal = "Agronomy",

issn = "2073-4395",

publisher = "MDPI AG",

number = "11",

}

TY - JOUR

T1 - Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model

AU - Joshi, Vijaya R.

AU - Thorp, Kelly R.

AU - Coulter, Jeffrey A.

AU - Johnson, Gregg A.

AU - Porter, Paul M.

AU - Strock, Jeffrey S.

AU - y Garcia, Axel Garcia

N1 - Funding 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. Publisher Copyright: © 2019 by the authors.

PY - 2019/11/6

Y1 - 2019/11/6

N2 - 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.

AB - 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.

KW - Crop modeling

KW - Precision agriculture

KW - Remote sensing

KW - Site-specific calibration

KW - Spatial-variability

UR - http://www.scopus.com/inward/record.url?scp=85075142535&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075142535&partnerID=8YFLogxK

U2 - 10.3390/agronomy9110719

DO - 10.3390/agronomy9110719

M3 - Article

AN - SCOPUS:85075142535

SN - 2073-4395

VL - 9

JO - Agronomy

JF - Agronomy

IS - 11

M1 - 719

ER -

Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model

Abstract

Bibliographical note

Keywords

UN SDGs

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this