Assessing field experts yield maize estimations with satellite information derived from Sentinel-2

Autores
Carcedo, Diego; Pons, Diego Hernan; Alonso, Cesar; Fiant, Silvina; Scavuzzo, Carlos Marcelo; Marinelli, María Victoria
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level.
EEA Manfredi
Fil: Carcedo, Diego. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Pons, Diego Hernan. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Alonso, Cesar. Bolsa de Cereales de Córdoba; Argentina
Fil: Fiant, Silvina. Bolsa de Cereales de Córdoba; Argentina
Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Marinelli, María Victoria. Comisión Nacional de Actividades Espaciales (CONAE). Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina
Fuente
Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 63-68
Materia
Maiz
Zea Mays
Rendimiento de Cultivos
Economía Agrícola
Maize
Crop Yield
Agricultural Economics
Agricultural Sector
Sector Agrario
Sector Agrícola
Sentinel-2
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
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spelling Assessing field experts yield maize estimations with satellite information derived from Sentinel-2Carcedo, DiegoPons, Diego HernanAlonso, CesarFiant, SilvinaScavuzzo, Carlos MarceloMarinelli, María VictoriaMaizZea MaysRendimiento de CultivosEconomía AgrícolaMaizeCrop YieldAgricultural EconomicsAgricultural SectorSector AgrarioSector AgrícolaSentinel-2Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level.EEA ManfrediFil: Carcedo, Diego. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); ArgentinaFil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Pons, Diego Hernan. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); ArgentinaFil: Alonso, Cesar. Bolsa de Cereales de Córdoba; ArgentinaFil: Fiant, Silvina. Bolsa de Cereales de Córdoba; ArgentinaFil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); ArgentinaFil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; ArgentinaFil: Marinelli, María Victoria. Comisión Nacional de Actividades Espaciales (CONAE). Instituto de Altos Estudios Espaciales "Mario Gulich"; ArgentinaUniversidad Técnica Federico Santa María, Chile2024-09-13T13:19:21Z2024-09-13T13:19:21Z2019-09-25info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/20.500.12123/19389978-956-356-095-4 (Online)Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 63-68reponame: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)2025-10-16T09:31:49Zoai:localhost:20.500.12123/19389instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:31:49.559INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
title Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
spellingShingle Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
Carcedo, Diego
Maiz
Zea Mays
Rendimiento de Cultivos
Economía Agrícola
Maize
Crop Yield
Agricultural Economics
Agricultural Sector
Sector Agrario
Sector Agrícola
Sentinel-2
title_short Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
title_full Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
title_fullStr Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
title_full_unstemmed Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
title_sort Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
dc.creator.none.fl_str_mv Carcedo, Diego
Pons, Diego Hernan
Alonso, Cesar
Fiant, Silvina
Scavuzzo, Carlos Marcelo
Marinelli, María Victoria
author Carcedo, Diego
author_facet Carcedo, Diego
Pons, Diego Hernan
Alonso, Cesar
Fiant, Silvina
Scavuzzo, Carlos Marcelo
Marinelli, María Victoria
author_role author
author2 Pons, Diego Hernan
Alonso, Cesar
Fiant, Silvina
Scavuzzo, Carlos Marcelo
Marinelli, María Victoria
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Maiz
Zea Mays
Rendimiento de Cultivos
Economía Agrícola
Maize
Crop Yield
Agricultural Economics
Agricultural Sector
Sector Agrario
Sector Agrícola
Sentinel-2
topic Maiz
Zea Mays
Rendimiento de Cultivos
Economía Agrícola
Maize
Crop Yield
Agricultural Economics
Agricultural Sector
Sector Agrario
Sector Agrícola
Sentinel-2
dc.description.none.fl_txt_mv Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level.
EEA Manfredi
Fil: Carcedo, Diego. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Pons, Diego Hernan. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Alonso, Cesar. Bolsa de Cereales de Córdoba; Argentina
Fil: Fiant, Silvina. Bolsa de Cereales de Córdoba; Argentina
Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Marinelli, María Victoria. Comisión Nacional de Actividades Espaciales (CONAE). Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina
description Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level.
publishDate 2019
dc.date.none.fl_str_mv 2019-09-25
2024-09-13T13:19:21Z
2024-09-13T13:19:21Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/19389
978-956-356-095-4 (Online)
url http://hdl.handle.net/20.500.12123/19389
identifier_str_mv 978-956-356-095-4 (Online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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 Universidad Técnica Federico Santa María, Chile
publisher.none.fl_str_mv Universidad Técnica Federico Santa María, Chile
dc.source.none.fl_str_mv Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 63-68
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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