Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements

Autores
Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
High and low soil moisture availability is one of the main limiting factors-Affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing-exporting strategies. The aim of this work was to analyze the aptitude of temperature vegetation dryness index (TVDI) to forecast maize yield. MODIS/AQUA enhanced vegetation index and land surface temperature (LST) at 1 km were used to calculate TVDI and maize yield over a large agricultural area of Argentine Pampas. The comparison between TVDI and official yield statistics was carried out to derive regression models in two agro-climatic zones, obtaining linear and quadratic adjustments. The models account for between 73% and 83% of yield variability, with the best prediction in the humid zone. The RMSE values ranged from 14% to 19% of average yield. The bias showed a slightly higher difference between predicted and observed yield data in semi-Arid zone. The models showed aptitude to estimate yield with reasonable accuracy 8-12 weeks before harvest. In addition, the TVDI-maize yield relationship and the impact of submonthly water stress were evaluated at field scale using yield measurements to ensure the analysis on maize. The highest ext{R}^{2} (0.61) was obtained using monthly values suggesting that the entire critical stage should be taken into account for yield forecasting. Although these results would not be directly extrapolated to other agricultural regions in the world, the proposed model is promising for forecasting spatial yield in other regions with poor data coverage several weeks before harvest.
Fil: Holzman, Mauro Ezequiel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Rivas, Raúl Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Tandil. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Tandil; Argentina
Materia
Optical-Thermal
Soil Moisture
Stress Index
Temperature Vegetation Dryness Index (Tvdi)
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/58706
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index MeasurementsHolzman, Mauro EzequielRivas, Raúl EduardoOptical-ThermalSoil MoistureStress IndexTemperature Vegetation Dryness Index (Tvdi)https://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1High and low soil moisture availability is one of the main limiting factors-Affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing-exporting strategies. The aim of this work was to analyze the aptitude of temperature vegetation dryness index (TVDI) to forecast maize yield. MODIS/AQUA enhanced vegetation index and land surface temperature (LST) at 1 km were used to calculate TVDI and maize yield over a large agricultural area of Argentine Pampas. The comparison between TVDI and official yield statistics was carried out to derive regression models in two agro-climatic zones, obtaining linear and quadratic adjustments. The models account for between 73% and 83% of yield variability, with the best prediction in the humid zone. The RMSE values ranged from 14% to 19% of average yield. The bias showed a slightly higher difference between predicted and observed yield data in semi-Arid zone. The models showed aptitude to estimate yield with reasonable accuracy 8-12 weeks before harvest. In addition, the TVDI-maize yield relationship and the impact of submonthly water stress were evaluated at field scale using yield measurements to ensure the analysis on maize. The highest ext{R}^{2} (0.61) was obtained using monthly values suggesting that the entire critical stage should be taken into account for yield forecasting. Although these results would not be directly extrapolated to other agricultural regions in the world, the proposed model is promising for forecasting spatial yield in other regions with poor data coverage several weeks before harvest.Fil: Holzman, Mauro Ezequiel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Azul; ArgentinaFil: Rivas, Raúl Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Tandil. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Tandil; ArgentinaInstitute of Electrical and Electronics Engineers2016-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58706Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements; Institute of Electrical and Electronics Engineers; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing; 9; 1; 1-2016; 507-5191939-1404CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2015.2504262info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7378859/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:26:33Zoai:ri.conicet.gov.ar:11336/58706instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:26:34.181CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
title Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
spellingShingle Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
Holzman, Mauro Ezequiel
Optical-Thermal
Soil Moisture
Stress Index
Temperature Vegetation Dryness Index (Tvdi)
title_short Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
title_full Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
title_fullStr Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
title_full_unstemmed Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
title_sort Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
dc.creator.none.fl_str_mv Holzman, Mauro Ezequiel
Rivas, Raúl Eduardo
author Holzman, Mauro Ezequiel
author_facet Holzman, Mauro Ezequiel
Rivas, Raúl Eduardo
author_role author
author2 Rivas, Raúl Eduardo
author2_role author
dc.subject.none.fl_str_mv Optical-Thermal
Soil Moisture
Stress Index
Temperature Vegetation Dryness Index (Tvdi)
topic Optical-Thermal
Soil Moisture
Stress Index
Temperature Vegetation Dryness Index (Tvdi)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv High and low soil moisture availability is one of the main limiting factors-Affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing-exporting strategies. The aim of this work was to analyze the aptitude of temperature vegetation dryness index (TVDI) to forecast maize yield. MODIS/AQUA enhanced vegetation index and land surface temperature (LST) at 1 km were used to calculate TVDI and maize yield over a large agricultural area of Argentine Pampas. The comparison between TVDI and official yield statistics was carried out to derive regression models in two agro-climatic zones, obtaining linear and quadratic adjustments. The models account for between 73% and 83% of yield variability, with the best prediction in the humid zone. The RMSE values ranged from 14% to 19% of average yield. The bias showed a slightly higher difference between predicted and observed yield data in semi-Arid zone. The models showed aptitude to estimate yield with reasonable accuracy 8-12 weeks before harvest. In addition, the TVDI-maize yield relationship and the impact of submonthly water stress were evaluated at field scale using yield measurements to ensure the analysis on maize. The highest ext{R}^{2} (0.61) was obtained using monthly values suggesting that the entire critical stage should be taken into account for yield forecasting. Although these results would not be directly extrapolated to other agricultural regions in the world, the proposed model is promising for forecasting spatial yield in other regions with poor data coverage several weeks before harvest.
Fil: Holzman, Mauro Ezequiel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Rivas, Raúl Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Tandil. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Tandil; Argentina
description High and low soil moisture availability is one of the main limiting factors-Affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing-exporting strategies. The aim of this work was to analyze the aptitude of temperature vegetation dryness index (TVDI) to forecast maize yield. MODIS/AQUA enhanced vegetation index and land surface temperature (LST) at 1 km were used to calculate TVDI and maize yield over a large agricultural area of Argentine Pampas. The comparison between TVDI and official yield statistics was carried out to derive regression models in two agro-climatic zones, obtaining linear and quadratic adjustments. The models account for between 73% and 83% of yield variability, with the best prediction in the humid zone. The RMSE values ranged from 14% to 19% of average yield. The bias showed a slightly higher difference between predicted and observed yield data in semi-Arid zone. The models showed aptitude to estimate yield with reasonable accuracy 8-12 weeks before harvest. In addition, the TVDI-maize yield relationship and the impact of submonthly water stress were evaluated at field scale using yield measurements to ensure the analysis on maize. The highest ext{R}^{2} (0.61) was obtained using monthly values suggesting that the entire critical stage should be taken into account for yield forecasting. Although these results would not be directly extrapolated to other agricultural regions in the world, the proposed model is promising for forecasting spatial yield in other regions with poor data coverage several weeks before harvest.
publishDate 2016
dc.date.none.fl_str_mv 2016-01
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 http://hdl.handle.net/11336/58706
Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements; Institute of Electrical and Electronics Engineers; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing; 9; 1; 1-2016; 507-519
1939-1404
CONICET Digital
CONICET
url http://hdl.handle.net/11336/58706
identifier_str_mv Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements; Institute of Electrical and Electronics Engineers; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing; 9; 1; 1-2016; 507-519
1939-1404
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2015.2504262
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7378859/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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