Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Autores
Peralta, Nahuel Raúl; Assefa, Yared; Du, Juan; Barden, Charles J.; Ciampitti, Ignacio A.
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.
EEA Balcarce
Fil: Peralta, Nahuel Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Assefa, Yared. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Du, Juan. Kansas State University. Department of Statistics; Estados Unidos
Fil: Barden, Charles J. Kansas State University. Department of Horticulture and Natural Resources; Estados Unidos
Fil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
Fuente
Remote Sensing 8 (10) : 848 (2016)
Materia
Técnicas de Predicción
Imágenes por Satélites
Maíz
Rendimiento
Agricultura de Precisión
Forecasting
Satellite Imagery
Maize
Yields
Precision Agriculture
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/4937

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spelling Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn YieldPeralta, Nahuel RaúlAssefa, YaredDu, JuanBarden, Charles J.Ciampitti, Ignacio A.Técnicas de PredicciónImágenes por SatélitesMaízRendimientoAgricultura de PrecisiónForecastingSatellite ImageryMaizeYieldsPrecision AgricultureA timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.EEA BalcarceFil: Peralta, Nahuel Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Kansas State University. Department of Agronomy; Estados UnidosFil: Assefa, Yared. Kansas State University. Department of Agronomy; Estados UnidosFil: Du, Juan. Kansas State University. Department of Statistics; Estados UnidosFil: Barden, Charles J. Kansas State University. Department of Horticulture and Natural Resources; Estados UnidosFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados UnidosMDPI2019-04-22T12:05:05Z2019-04-22T12:05:05Z2016-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.mdpi.com/2072-4292/8/10/848http://hdl.handle.net/20.500.12123/49372072-4292https://doi.org/10.3390/rs8100848Remote Sensing 8 (10) : 848 (2016)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:44:38Zoai:localhost:20.500.12123/4937instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:38.465INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
title Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
spellingShingle Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
Peralta, Nahuel Raúl
Técnicas de Predicción
Imágenes por Satélites
Maíz
Rendimiento
Agricultura de Precisión
Forecasting
Satellite Imagery
Maize
Yields
Precision Agriculture
title_short Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
title_full Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
title_fullStr Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
title_full_unstemmed Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
title_sort Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
dc.creator.none.fl_str_mv Peralta, Nahuel Raúl
Assefa, Yared
Du, Juan
Barden, Charles J.
Ciampitti, Ignacio A.
author Peralta, Nahuel Raúl
author_facet Peralta, Nahuel Raúl
Assefa, Yared
Du, Juan
Barden, Charles J.
Ciampitti, Ignacio A.
author_role author
author2 Assefa, Yared
Du, Juan
Barden, Charles J.
Ciampitti, Ignacio A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Técnicas de Predicción
Imágenes por Satélites
Maíz
Rendimiento
Agricultura de Precisión
Forecasting
Satellite Imagery
Maize
Yields
Precision Agriculture
topic Técnicas de Predicción
Imágenes por Satélites
Maíz
Rendimiento
Agricultura de Precisión
Forecasting
Satellite Imagery
Maize
Yields
Precision Agriculture
dc.description.none.fl_txt_mv A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.
EEA Balcarce
Fil: Peralta, Nahuel Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Assefa, Yared. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Du, Juan. Kansas State University. Department of Statistics; Estados Unidos
Fil: Barden, Charles J. Kansas State University. Department of Horticulture and Natural Resources; Estados Unidos
Fil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
description A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
2019-04-22T12:05:05Z
2019-04-22T12:05:05Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.mdpi.com/2072-4292/8/10/848
http://hdl.handle.net/20.500.12123/4937
2072-4292
https://doi.org/10.3390/rs8100848
url https://www.mdpi.com/2072-4292/8/10/848
http://hdl.handle.net/20.500.12123/4937
https://doi.org/10.3390/rs8100848
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 8 (10) : 848 (2016)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
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instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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