Assessing soil carbon stocks under pastures through orbital remote sensing
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000500010 |
Resumo: | The growing demand of world food and energy supply increases the threat of global warming due to higher greenhouse gas emissions by agricultural activity. Therefore, it is widely admitted that agriculture must establish a new paradigm in terms of environmental sustainability that incorporate techniques for mitigation of greenhouse gas emissions. This article addresses to the scientific demand to estimate in a fast and inexpensive manner current and potential soil organic carbon (SOC) stocks in degraded pastures, using remote sensing techniques. Four pastures on sandy soils under Brazilian Cerrado vegetation in São Paulo state were chosen due to their SOC sequestration potential, which was characterized for the soil depth 0-50 cm. Subsequently, a linear regression analysis was performed between SOC and Leaf Area Index (LAI) measured in the field (LAIfield) and derived by satellite (LAIsatellite) as well as SOC and pasture reflectance in six spectra from 450 nm - 2350 nm, using the Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7. A high correlation between SOC and LAIfield (R² = 0.9804) and LAIsatellite (R² = 0.9812) was verified. The suitability of satellite derived LAI for SOC determination leads to the assumption, that orbital remote sensing is a very promising SOC estimation technique from regional to global scale. |
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Scientia Agrícola (Online) |
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Assessing soil carbon stocks under pastures through orbital remote sensingBrazilleaf Area Indexsoil organic carbonpasture degradationspectral reflectanceclimate changeThe growing demand of world food and energy supply increases the threat of global warming due to higher greenhouse gas emissions by agricultural activity. Therefore, it is widely admitted that agriculture must establish a new paradigm in terms of environmental sustainability that incorporate techniques for mitigation of greenhouse gas emissions. This article addresses to the scientific demand to estimate in a fast and inexpensive manner current and potential soil organic carbon (SOC) stocks in degraded pastures, using remote sensing techniques. Four pastures on sandy soils under Brazilian Cerrado vegetation in São Paulo state were chosen due to their SOC sequestration potential, which was characterized for the soil depth 0-50 cm. Subsequently, a linear regression analysis was performed between SOC and Leaf Area Index (LAI) measured in the field (LAIfield) and derived by satellite (LAIsatellite) as well as SOC and pasture reflectance in six spectra from 450 nm - 2350 nm, using the Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7. A high correlation between SOC and LAIfield (R² = 0.9804) and LAIsatellite (R² = 0.9812) was verified. The suitability of satellite derived LAI for SOC determination leads to the assumption, that orbital remote sensing is a very promising SOC estimation technique from regional to global scale.Escola Superior de Agricultura "Luiz de Queiroz"2011-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000500010Scientia Agricola v.68 n.5 2011reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162011000500010info:eu-repo/semantics/openAccessSzakács,Gabor Gyula JuliusCerri,Carlos ClementeHerpin,UweBernoux,Martialeng2011-10-03T00:00:00Zoai:scielo:S0103-90162011000500010Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2011-10-03T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Assessing soil carbon stocks under pastures through orbital remote sensing |
title |
Assessing soil carbon stocks under pastures through orbital remote sensing |
spellingShingle |
Assessing soil carbon stocks under pastures through orbital remote sensing Szakács,Gabor Gyula Julius Brazil leaf Area Index soil organic carbon pasture degradation spectral reflectance climate change |
title_short |
Assessing soil carbon stocks under pastures through orbital remote sensing |
title_full |
Assessing soil carbon stocks under pastures through orbital remote sensing |
title_fullStr |
Assessing soil carbon stocks under pastures through orbital remote sensing |
title_full_unstemmed |
Assessing soil carbon stocks under pastures through orbital remote sensing |
title_sort |
Assessing soil carbon stocks under pastures through orbital remote sensing |
author |
Szakács,Gabor Gyula Julius |
author_facet |
Szakács,Gabor Gyula Julius Cerri,Carlos Clemente Herpin,Uwe Bernoux,Martial |
author_role |
author |
author2 |
Cerri,Carlos Clemente Herpin,Uwe Bernoux,Martial |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Szakács,Gabor Gyula Julius Cerri,Carlos Clemente Herpin,Uwe Bernoux,Martial |
dc.subject.por.fl_str_mv |
Brazil leaf Area Index soil organic carbon pasture degradation spectral reflectance climate change |
topic |
Brazil leaf Area Index soil organic carbon pasture degradation spectral reflectance climate change |
description |
The growing demand of world food and energy supply increases the threat of global warming due to higher greenhouse gas emissions by agricultural activity. Therefore, it is widely admitted that agriculture must establish a new paradigm in terms of environmental sustainability that incorporate techniques for mitigation of greenhouse gas emissions. This article addresses to the scientific demand to estimate in a fast and inexpensive manner current and potential soil organic carbon (SOC) stocks in degraded pastures, using remote sensing techniques. Four pastures on sandy soils under Brazilian Cerrado vegetation in São Paulo state were chosen due to their SOC sequestration potential, which was characterized for the soil depth 0-50 cm. Subsequently, a linear regression analysis was performed between SOC and Leaf Area Index (LAI) measured in the field (LAIfield) and derived by satellite (LAIsatellite) as well as SOC and pasture reflectance in six spectra from 450 nm - 2350 nm, using the Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7. A high correlation between SOC and LAIfield (R² = 0.9804) and LAIsatellite (R² = 0.9812) was verified. The suitability of satellite derived LAI for SOC determination leads to the assumption, that orbital remote sensing is a very promising SOC estimation technique from regional to global scale. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000500010 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000500010 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162011000500010 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.68 n.5 2011 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936462496169984 |