On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/16043 |
Resumo: | We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r 90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids = chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture management |
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On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectancegrasslandsfertilizationvegetation indiceshyperspectral reflectanceWe applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r 90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids = chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture managementEuropean Geosciences UnionRepositório da Universidade de LisboaCerasoli, SofiaCampagnolo, ManuelFaria, JoanaNogueira, CarlaCaldeira, M.Conceição2018-10-08T14:11:36Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/16043engBiogeosciences, 15, 5455–5471, 2018https://doi.org/10.5194/bg-15-5455-2018info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:45:55Zoai:www.repository.utl.pt:10400.5/16043Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:01:32.891529Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
title |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
spellingShingle |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance Cerasoli, Sofia grasslands fertilization vegetation indices hyperspectral reflectance |
title_short |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
title_full |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
title_fullStr |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
title_full_unstemmed |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
title_sort |
On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance |
author |
Cerasoli, Sofia |
author_facet |
Cerasoli, Sofia Campagnolo, Manuel Faria, Joana Nogueira, Carla Caldeira, M.Conceição |
author_role |
author |
author2 |
Campagnolo, Manuel Faria, Joana Nogueira, Carla Caldeira, M.Conceição |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Cerasoli, Sofia Campagnolo, Manuel Faria, Joana Nogueira, Carla Caldeira, M.Conceição |
dc.subject.por.fl_str_mv |
grasslands fertilization vegetation indices hyperspectral reflectance |
topic |
grasslands fertilization vegetation indices hyperspectral reflectance |
description |
We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r 90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids = chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture management |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-08T14:11:36Z 2018 2018-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.5/16043 |
url |
http://hdl.handle.net/10400.5/16043 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Biogeosciences, 15, 5455–5471, 2018 https://doi.org/10.5194/bg-15-5455-2018 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
European Geosciences Union |
publisher.none.fl_str_mv |
European Geosciences Union |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131104096026624 |