Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning

Detalhes bibliográficos
Autor(a) principal: Erickson, Daniel Thomas
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150967
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learningSoil MoisturePolarimetryEucalyptusL-Band SARFeature SelectionMachine LearningDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSoil moisture is a critical ecological parameter because it is a primary input for all processes that involve the complex interaction between land surface and the atmosphere. Remote sensing, especially using microwaves, has shown great promise in measuring soil moisturewith several operating satellites focused on its continuous estimation and monitoring on a global scale. Portugal is predominantly characterized by Mediterranean and semi-arid climates that feature low and sporadic precipitation. Over 10% of Portugal’s land area has been planted with Eucalyptus globulus- a non-native, fast-growing tree primarily planted for industrial use. Some studies have demonstrated that eucalyptus plantations adversely affect water availability, but overall results have been inconclusive as there are numerous other confounding variables. The goals of this study were to determine, using fully polarimetric L-band SAR and machine learning, if soil moisture could be accurately predicted in eucalyptus forests, and if there is a significant difference in soil moisture inside eucalyptus forests relative to other forests. Vegetated surfaces complicate the estimation of soil moisture because their structure and water content contribute significantly to backscatter of the radar signal. Thus, four polarimetric decompositions were compared to separate vegetative versus surface backscatter. The inputs from those decompositions, as well as several additional radar indices and polarizations from the microwave images, were used as feature inputs into two different machine learning models. After a feature selection process, the soil moisture estimations were retrieved and compared using cross-validation. The best overall soil moisture retrieval for Eucalyptus forests came from Random Forest with a RMSE of 0.021, a MAE of 0.017, and a MBE of 0.001. Through a statistical t-test, predicted soil moisture values in eucalyptus forests did not differ significantly as compared to other forest types in the study area.Silva, Joel Dinis Baptista Ferreira daCosta, Hugo Alexandre Gomes daGranell-Canut, CarlosRUNErickson, Daniel Thomas2023-03-21T18:28:54Z2023-03-012023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/150967TID:203254430enginfo: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:RCAAP2024-03-11T05:33:24Zoai:run.unl.pt:10362/150967Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:23.530777Repositório 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 Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
title Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
spellingShingle Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
Erickson, Daniel Thomas
Soil Moisture
Polarimetry
Eucalyptus
L-Band SAR
Feature Selection
Machine Learning
title_short Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
title_full Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
title_fullStr Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
title_full_unstemmed Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
title_sort Soil moisture estimation of eucalyptus forests in Portugal with L-band SAR using polarimetric - Decompositions and machine learning
author Erickson, Daniel Thomas
author_facet Erickson, Daniel Thomas
author_role author
dc.contributor.none.fl_str_mv Silva, Joel Dinis Baptista Ferreira da
Costa, Hugo Alexandre Gomes da
Granell-Canut, Carlos
RUN
dc.contributor.author.fl_str_mv Erickson, Daniel Thomas
dc.subject.por.fl_str_mv Soil Moisture
Polarimetry
Eucalyptus
L-Band SAR
Feature Selection
Machine Learning
topic Soil Moisture
Polarimetry
Eucalyptus
L-Band SAR
Feature Selection
Machine Learning
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2023
dc.date.none.fl_str_mv 2023-03-21T18:28:54Z
2023-03-01
2023-03-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150967
TID:203254430
url http://hdl.handle.net/10362/150967
identifier_str_mv TID:203254430
dc.language.iso.fl_str_mv eng
language eng
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.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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