Predicting soil electro-conductivity using Sentinel-1 images

Detalhes bibliográficos
Autor(a) principal: Medeiros, Eduardo
Data de Publicação: 2021
Outros Autores: Gonçalves, Teresa, Rato, Luis, Ahmed, Sajib
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/33887
Resumo: The quality and yield of a soil can be measured by using a wide range of soil indicators. One such indicator is soil’s electro-conductivity which is an excellent indicator of the presence of soil nutrients. This work aims to create a machine learning model to predict the soil’s electro-conductivity (EC) using radar images from the satellite Sentinel-1. Using EC readings from 14 corn field parcels and Sentinel-1 readings over the course of one agriculture year, several regression models were generated. These mod- els were designed using information from the full agriculture year or only 3 months, both or only one of the VV and VH polarisations. The results show that when using a full year data VV and VH polarisations are able to generate models with similar performance (R2 of 0.888 for VH and 0.884 for VV) but when using only 3 months data, only April to June trimester using both polarisations are able to reach similar a performance (R2 of 0.867); moreover VH polarisation seems to carry out more descriptive in- formation when compared with VV (specially when using only 3 months Radar data was collected from two time windows each corresponding data). Finally, performance results seem to be independent of the yearly radar data time-window.
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spelling Predicting soil electro-conductivity using Sentinel-1 imagesSoil electro-conductivityRemote sensingSentinel-1RegressionK-nearest neighboursThe quality and yield of a soil can be measured by using a wide range of soil indicators. One such indicator is soil’s electro-conductivity which is an excellent indicator of the presence of soil nutrients. This work aims to create a machine learning model to predict the soil’s electro-conductivity (EC) using radar images from the satellite Sentinel-1. Using EC readings from 14 corn field parcels and Sentinel-1 readings over the course of one agriculture year, several regression models were generated. These mod- els were designed using information from the full agriculture year or only 3 months, both or only one of the VV and VH polarisations. The results show that when using a full year data VV and VH polarisations are able to generate models with similar performance (R2 of 0.888 for VH and 0.884 for VV) but when using only 3 months data, only April to June trimester using both polarisations are able to reach similar a performance (R2 of 0.867); moreover VH polarisation seems to carry out more descriptive in- formation when compared with VV (specially when using only 3 months Radar data was collected from two time windows each corresponding data). Finally, performance results seem to be independent of the yearly radar data time-window.2023-02-03T16:03:31Z2023-02-032021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/33887http://hdl.handle.net/10174/33887porEduardo Medeiros, Sajib Ahmed, Teresa Gonçalves, and Luı́s Rato. Predicting soil electro-conductivity using Sentinel-1 images. In Proceedings of the 27th Portuguese Con- ference on Pattern Recognition, RECPAD 2021, 2021ndtcg@uevora.ptndnd283Medeiros, EduardoGonçalves, TeresaRato, LuisAhmed, Sajibinfo: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:RCAAP2024-01-03T19:35:56Zoai:dspace.uevora.pt:10174/33887Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:22:35.186177Repositório 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 Predicting soil electro-conductivity using Sentinel-1 images
title Predicting soil electro-conductivity using Sentinel-1 images
spellingShingle Predicting soil electro-conductivity using Sentinel-1 images
Medeiros, Eduardo
Soil electro-conductivity
Remote sensing
Sentinel-1
Regression
K-nearest neighbours
title_short Predicting soil electro-conductivity using Sentinel-1 images
title_full Predicting soil electro-conductivity using Sentinel-1 images
title_fullStr Predicting soil electro-conductivity using Sentinel-1 images
title_full_unstemmed Predicting soil electro-conductivity using Sentinel-1 images
title_sort Predicting soil electro-conductivity using Sentinel-1 images
author Medeiros, Eduardo
author_facet Medeiros, Eduardo
Gonçalves, Teresa
Rato, Luis
Ahmed, Sajib
author_role author
author2 Gonçalves, Teresa
Rato, Luis
Ahmed, Sajib
author2_role author
author
author
dc.contributor.author.fl_str_mv Medeiros, Eduardo
Gonçalves, Teresa
Rato, Luis
Ahmed, Sajib
dc.subject.por.fl_str_mv Soil electro-conductivity
Remote sensing
Sentinel-1
Regression
K-nearest neighbours
topic Soil electro-conductivity
Remote sensing
Sentinel-1
Regression
K-nearest neighbours
description The quality and yield of a soil can be measured by using a wide range of soil indicators. One such indicator is soil’s electro-conductivity which is an excellent indicator of the presence of soil nutrients. This work aims to create a machine learning model to predict the soil’s electro-conductivity (EC) using radar images from the satellite Sentinel-1. Using EC readings from 14 corn field parcels and Sentinel-1 readings over the course of one agriculture year, several regression models were generated. These mod- els were designed using information from the full agriculture year or only 3 months, both or only one of the VV and VH polarisations. The results show that when using a full year data VV and VH polarisations are able to generate models with similar performance (R2 of 0.888 for VH and 0.884 for VV) but when using only 3 months data, only April to June trimester using both polarisations are able to reach similar a performance (R2 of 0.867); moreover VH polarisation seems to carry out more descriptive in- formation when compared with VV (specially when using only 3 months Radar data was collected from two time windows each corresponding data). Finally, performance results seem to be independent of the yearly radar data time-window.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01T00:00:00Z
2023-02-03T16:03:31Z
2023-02-03
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/10174/33887
http://hdl.handle.net/10174/33887
url http://hdl.handle.net/10174/33887
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Eduardo Medeiros, Sajib Ahmed, Teresa Gonçalves, and Luı́s Rato. Predicting soil electro-conductivity using Sentinel-1 images. In Proceedings of the 27th Portuguese Con- ference on Pattern Recognition, RECPAD 2021, 2021
nd
tcg@uevora.pt
nd
nd
283
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
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