Classifying Soil Type Using Radar Satellite Images

Detalhes bibliográficos
Autor(a) principal: Ahmed, MD Sajib
Data de Publicação: 2020
Outros Autores: Gonçalves, Teresa, Rato, Luís, Marques da Silva, José Rafael, Vieira, Filipe, Paixão, Luís, Salgueiro, Pedro
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/31998
Resumo: The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.
id RCAP_8179c099009b2b0e8aa1a535c39eebc2
oai_identifier_str oai:dspace.uevora.pt:10174/31998
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Classifying Soil Type Using Radar Satellite ImagesRemote SensingSoil Electrical ConductivitySentinel-1, Machine LearningRandom ForestThe growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.2022-05-03T14:45:05Z2022-05-032020-10-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/31998http://hdl.handle.net/10174/31998porhttps://recpad2020.uevora.pt/wp-content/uploads/2020/11/proceedings_recpad2020.pdfndndndndndndndAhmed, MD SajibGonçalves, TeresaRato, LuísMarques da Silva, José RafaelVieira, FilipePaixão, LuísSalgueiro, Pedroinfo: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:RCAAP2024-01-03T19:32:09Zoai:dspace.uevora.pt:10174/31998Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:21:03.863958Repositório 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 Classifying Soil Type Using Radar Satellite Images
title Classifying Soil Type Using Radar Satellite Images
spellingShingle Classifying Soil Type Using Radar Satellite Images
Ahmed, MD Sajib
Remote Sensing
Soil Electrical Conductivity
Sentinel-1, Machine Learning
Random Forest
title_short Classifying Soil Type Using Radar Satellite Images
title_full Classifying Soil Type Using Radar Satellite Images
title_fullStr Classifying Soil Type Using Radar Satellite Images
title_full_unstemmed Classifying Soil Type Using Radar Satellite Images
title_sort Classifying Soil Type Using Radar Satellite Images
author Ahmed, MD Sajib
author_facet Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luís
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
author_role author
author2 Gonçalves, Teresa
Rato, Luís
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luís
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
dc.subject.por.fl_str_mv Remote Sensing
Soil Electrical Conductivity
Sentinel-1, Machine Learning
Random Forest
topic Remote Sensing
Soil Electrical Conductivity
Sentinel-1, Machine Learning
Random Forest
description The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-30T00:00:00Z
2022-05-03T14:45:05Z
2022-05-03
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/10174/31998
http://hdl.handle.net/10174/31998
url http://hdl.handle.net/10174/31998
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://recpad2020.uevora.pt/wp-content/uploads/2020/11/proceedings_recpad2020.pdf
nd
nd
nd
nd
nd
nd
nd
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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
repository.mail.fl_str_mv
_version_ 1799136692199751680

Registros relacionados