NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures

Autores
Brieva, Carlos Alberto; Saco, Patricia M.; Sandi, Steven G.; Mora, Sebastian; Rodríguez, José F.
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza
EEA Rama Caída
Fil: Brieva, Carlos. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Brieva, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina
Fil: Saco, Patricia, M. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Sandi, Steven G. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Sandi, Steven G. Deakin University. School of Engineering; Australia
Fil: Mora, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina
Fil: Rodríguez, José F. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fuente
Remote Sensing 15 (14) : 3615 (July 2023)
Materia
Pastizales
Teledetección
Indice Normalizado Diferencial de la Vegetación
Pastures
Remote Sensing
Normalized Difference Vegetation Index
NDVI
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:20.500.12123/16440
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oai_identifier_str oai:localhost:20.500.12123/16440
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spelling NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland PasturesBrieva, Carlos AlbertoSaco, Patricia M.Sandi, Steven G.Mora, SebastianRodríguez, José F.PastizalesTeledetecciónIndice Normalizado Diferencial de la VegetaciónPasturesRemote SensingNormalized Difference Vegetation IndexNDVIPrecipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of MendozaEEA Rama CaídaFil: Brieva, Carlos. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Brieva, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; ArgentinaFil: Saco, Patricia, M. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Sandi, Steven G. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Sandi, Steven G. Deakin University. School of Engineering; AustraliaFil: Mora, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; ArgentinaFil: Rodríguez, José F. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaMDPI2024-01-03T14:11:56Z2024-01-03T14:11:56Z2023-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/16440https://www.mdpi.com/2072-4292/15/14/36152072-4292https://doi.org/10.3390/rs15143615Remote Sensing 15 (14) : 3615 (July 2023)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)2025-09-18T10:09:15Zoai:localhost:20.500.12123/16440instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-18 10:09:15.962INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
title NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
spellingShingle NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
Brieva, Carlos Alberto
Pastizales
Teledetección
Indice Normalizado Diferencial de la Vegetación
Pastures
Remote Sensing
Normalized Difference Vegetation Index
NDVI
title_short NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
title_full NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
title_fullStr NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
title_full_unstemmed NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
title_sort NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
dc.creator.none.fl_str_mv Brieva, Carlos Alberto
Saco, Patricia M.
Sandi, Steven G.
Mora, Sebastian
Rodríguez, José F.
author Brieva, Carlos Alberto
author_facet Brieva, Carlos Alberto
Saco, Patricia M.
Sandi, Steven G.
Mora, Sebastian
Rodríguez, José F.
author_role author
author2 Saco, Patricia M.
Sandi, Steven G.
Mora, Sebastian
Rodríguez, José F.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Pastizales
Teledetección
Indice Normalizado Diferencial de la Vegetación
Pastures
Remote Sensing
Normalized Difference Vegetation Index
NDVI
topic Pastizales
Teledetección
Indice Normalizado Diferencial de la Vegetación
Pastures
Remote Sensing
Normalized Difference Vegetation Index
NDVI
dc.description.none.fl_txt_mv Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza
EEA Rama Caída
Fil: Brieva, Carlos. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Brieva, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina
Fil: Saco, Patricia, M. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Sandi, Steven G. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
Fil: Sandi, Steven G. Deakin University. School of Engineering; Australia
Fil: Mora, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina
Fil: Rodríguez, José F. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia
description Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza
publishDate 2023
dc.date.none.fl_str_mv 2023-07
2024-01-03T14:11:56Z
2024-01-03T14:11:56Z
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/20.500.12123/16440
https://www.mdpi.com/2072-4292/15/14/3615
2072-4292
https://doi.org/10.3390/rs15143615
url http://hdl.handle.net/20.500.12123/16440
https://www.mdpi.com/2072-4292/15/14/3615
https://doi.org/10.3390/rs15143615
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
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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 15 (14) : 3615 (July 2023)
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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score 13.001348

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