Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , , , , , , , , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Institucional da UFLA |
Download full: | http://repositorio.ufla.br/jspui/handle/1/49215 |
Summary: | Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data. |
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Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time seriesEnvironmental monitoringLand use and cover historyRandom forestRemote sensingSoil depthCarbon stocksMonitoramento ambientalHistórico de uso e cobertura da terraSensoriamento remotoProfundidade do soloEstoques de carbonoSoil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.MDPI2022-02-08T19:04:46Z2022-02-08T19:04:46Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfTAYEBI, M. et al. Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series. Remote Sensing, [S. l.], v. 13, n. 11, 2021. DOI: 10.3390/rs13112223.http://repositorio.ufla.br/jspui/handle/1/49215Remote Sensingreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessTayebi, MahboobehRosas, Jorge Tadeu FimMendes, Wanderson de SousaPoppiel, Raul RobertoOstovari, YaserRuiz, Luis Fernando ChimeloSantos, Natasha Valadares dosCerri, Carlos Eduardo PellegrinoSilva, Sérgio Henrique GodinhoCuri, NiltonSilvero, Nélida Elizabet QuiñonezDemattê, José A. M.eng2022-02-08T19:04:46Zoai:localhost:1/49215Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-02-08T19:04:46Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
title |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
spellingShingle |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series Tayebi, Mahboobeh Environmental monitoring Land use and cover history Random forest Remote sensing Soil depth Carbon stocks Monitoramento ambiental Histórico de uso e cobertura da terra Sensoriamento remoto Profundidade do solo Estoques de carbono |
title_short |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
title_full |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
title_fullStr |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
title_full_unstemmed |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
title_sort |
Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series |
author |
Tayebi, Mahboobeh |
author_facet |
Tayebi, Mahboobeh Rosas, Jorge Tadeu Fim Mendes, Wanderson de Sousa Poppiel, Raul Roberto Ostovari, Yaser Ruiz, Luis Fernando Chimelo Santos, Natasha Valadares dos Cerri, Carlos Eduardo Pellegrino Silva, Sérgio Henrique Godinho Curi, Nilton Silvero, Nélida Elizabet Quiñonez Demattê, José A. M. |
author_role |
author |
author2 |
Rosas, Jorge Tadeu Fim Mendes, Wanderson de Sousa Poppiel, Raul Roberto Ostovari, Yaser Ruiz, Luis Fernando Chimelo Santos, Natasha Valadares dos Cerri, Carlos Eduardo Pellegrino Silva, Sérgio Henrique Godinho Curi, Nilton Silvero, Nélida Elizabet Quiñonez Demattê, José A. M. |
author2_role |
author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Tayebi, Mahboobeh Rosas, Jorge Tadeu Fim Mendes, Wanderson de Sousa Poppiel, Raul Roberto Ostovari, Yaser Ruiz, Luis Fernando Chimelo Santos, Natasha Valadares dos Cerri, Carlos Eduardo Pellegrino Silva, Sérgio Henrique Godinho Curi, Nilton Silvero, Nélida Elizabet Quiñonez Demattê, José A. M. |
dc.subject.por.fl_str_mv |
Environmental monitoring Land use and cover history Random forest Remote sensing Soil depth Carbon stocks Monitoramento ambiental Histórico de uso e cobertura da terra Sensoriamento remoto Profundidade do solo Estoques de carbono |
topic |
Environmental monitoring Land use and cover history Random forest Remote sensing Soil depth Carbon stocks Monitoramento ambiental Histórico de uso e cobertura da terra Sensoriamento remoto Profundidade do solo Estoques de carbono |
description |
Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-02-08T19:04:46Z 2022-02-08T19:04:46Z |
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 |
TAYEBI, M. et al. Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series. Remote Sensing, [S. l.], v. 13, n. 11, 2021. DOI: 10.3390/rs13112223. http://repositorio.ufla.br/jspui/handle/1/49215 |
identifier_str_mv |
TAYEBI, M. et al. Drivers of organic carbon stocks in different LULC history and along soil depth for a 30 years image time series. Remote Sensing, [S. l.], v. 13, n. 11, 2021. DOI: 10.3390/rs13112223. |
url |
http://repositorio.ufla.br/jspui/handle/1/49215 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
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 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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1784550175056855040 |