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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/19389
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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) |
collection |
INTA Digital (INTA) |
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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1846143577441173504 |
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12.712165 |