Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring

Climate variables data derived from satellite imagery or products available in the cloud have wide coverage in space and time, good accuracy, and are generally freely accessible. However, obtaining and downloading climate variables at different spatial and temporal scales is limited by the lack of s...

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Autores principales: Paccioretti, Pablo, Giannini-Kurina, Franca, Suarez, Franco, Scavuzzo, Marcelo, Balzarini , Mónica, Alemandri, Vanina, Gómez Montenegro, Brenda E.
Formato: Artículo revista
Lenguaje:Español
Publicado: Facultad de Ciencias Agropecuarias 2023
Materias:
Google Earth Engine
R software
ERA5
cloud computing
software R
computación en la nube
Acceso en línea:https://revistas.unc.edu.ar/index.php/agris/article/view/39619
Aporte de:
AgriScientia de Universidad Nacional de Córdoba
id I10-R352-article-39619
record_format ojs
spelling I10-R352-article-396192024-06-21T17:33:29Z Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring Protocolo para automatizar la descarga de datos climáticos desde la nube y generar indicadores biometeorológicos para el monitoreo epidemiológico de cultivos Paccioretti, Pablo Giannini-Kurina, Franca Suarez, Franco Scavuzzo, Marcelo Balzarini , Mónica Alemandri, Vanina Gómez Montenegro, Brenda E. Google Earth Engine R software ERA5 cloud computing Google Earth Engine software R ERA5 computación en la nube Climate variables data derived from satellite imagery or products available in the cloud have wide coverage in space and time, good accuracy, and are generally freely accessible. However, obtaining and downloading climate variables at different spatial and temporal scales is limited by the lack of standardized computational procedures. The objective of this study was to develop a computational code to facilitate handling satellite images in order to derive climatic variables for a given spatiotemporal domain. The climate data was obtained from ERA5, a Copernicus Climate Change Service product. The protocol includes data download from the Google Earth Engine platform with a code developed in R language. The protocol developed includes statistical preprocessing of climatic data at fortnightly and/or monthly scale. By combining satellite-derived products with agronomic knowledge about a crop, climate datacan be converted into biometeorological variables and used for spatiotemporal monitoring of crops. The process developed was validated by joint data from biometeorological variables at each site of an epidemiological study which has been monitoring two viruses for 15 years. The protocol may be applied to other satellite products using spatial data. Los datos climáticos derivados de imágenes o productos satelitales disponibles en la nube tienen gran cobertura en espacio y tiempo, buena precisión y, en general, son de libre acceso. Sin embargo, la obtención y descarga de variables climáticas a diferentes escalas se encuentra limitada por la falta de procedimientos computacionales estandarizados. El objetivo de este estudio fue desarrollar un código computacional que facilite el manejo de imágenes satelitales para obtención de variables climáticas en un dominio espaciotemporal. El producto ERA5 del servicio de Cambio Climático Copernicus fue usado como fuente de datos climáticos. El protocolo incluye la descarga desde la plataforma Google Earth Engine, con un código desarrollado en lenguaje R. Adiciona el preprocesamiento estadístico de los datos climáticos a escala quincenal y/o mensual. Combinando productos derivados de satélites con conocimiento agronómico sobre un cultivo, los datos climáticos pueden convertirse en variables biometeorológicas y usarse para el monitoreo espaciotemporal de cultivos. El proceso generado se validó superponiendo datos de variables biometeorológicas, en cada sitio de un estudio epidemiológico, sobre dos virus monitoreados por 15 años. El procedimiento puede aplicarse a otros productos satelitales que generan datos espaciales. Facultad de Ciencias Agropecuarias 2023-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion text/html application/pdf https://revistas.unc.edu.ar/index.php/agris/article/view/39619 10.31047/1668.298x.v40.n1.39619 AgriScientia; Vol. 40 No. 1 (2023) AgriScientia; Vol. 40 Núm. 1 (2023) 1668-298X 10.31047/1668.298x.v1.n40 spa https://revistas.unc.edu.ar/index.php/agris/article/view/39619/45366 https://revistas.unc.edu.ar/index.php/agris/article/view/39619/44708 Derechos de autor 2023 Pablo Paccioretti, Franca Giannini-Kurina, Franco Suarez, Marcelo Scavuzzo https://creativecommons.org/licenses/by-sa/4.0
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-352
container_title_str AgriScientia
language Español
format Artículo revista
topic Google Earth Engine
R software
ERA5
cloud computing
Google Earth Engine
software R
ERA5
computación en la nube
spellingShingle Google Earth Engine
R software
ERA5
cloud computing
Google Earth Engine
software R
ERA5
computación en la nube
Paccioretti, Pablo
Giannini-Kurina, Franca
Suarez, Franco
Scavuzzo, Marcelo
Balzarini , Mónica
Alemandri, Vanina
Gómez Montenegro, Brenda E.
Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
topic_facet Google Earth Engine
R software
ERA5
cloud computing
Google Earth Engine
software R
ERA5
computación en la nube
author Paccioretti, Pablo
Giannini-Kurina, Franca
Suarez, Franco
Scavuzzo, Marcelo
Balzarini , Mónica
Alemandri, Vanina
Gómez Montenegro, Brenda E.
author_facet Paccioretti, Pablo
Giannini-Kurina, Franca
Suarez, Franco
Scavuzzo, Marcelo
Balzarini , Mónica
Alemandri, Vanina
Gómez Montenegro, Brenda E.
author_sort Paccioretti, Pablo
title Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
title_short Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
title_full Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
title_fullStr Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
title_full_unstemmed Protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
title_sort protocol for automating climatic data download from the cloud and generating biometeorological indicators for crop epidemiological monitoring
description Climate variables data derived from satellite imagery or products available in the cloud have wide coverage in space and time, good accuracy, and are generally freely accessible. However, obtaining and downloading climate variables at different spatial and temporal scales is limited by the lack of standardized computational procedures. The objective of this study was to develop a computational code to facilitate handling satellite images in order to derive climatic variables for a given spatiotemporal domain. The climate data was obtained from ERA5, a Copernicus Climate Change Service product. The protocol includes data download from the Google Earth Engine platform with a code developed in R language. The protocol developed includes statistical preprocessing of climatic data at fortnightly and/or monthly scale. By combining satellite-derived products with agronomic knowledge about a crop, climate datacan be converted into biometeorological variables and used for spatiotemporal monitoring of crops. The process developed was validated by joint data from biometeorological variables at each site of an epidemiological study which has been monitoring two viruses for 15 years. The protocol may be applied to other satellite products using spatial data.
publisher Facultad de Ciencias Agropecuarias
publishDate 2023
url https://revistas.unc.edu.ar/index.php/agris/article/view/39619
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first_indexed 2024-09-03T22:16:39Z
last_indexed 2024-09-03T22:16:39Z
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