Development of a methodology to fill gaps in MODIS LST data for Antarctica

Bibliographic Details
Main Author: Alasawedah, Mohammad Hussein Mohammad
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/113760
Summary: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Development of a methodology to fill gaps in MODIS LST data for AntarcticaConvolutional Neural NetworksExtreme learning machineDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesLand Surface Temperature (LST) is an essential parameter for analyzing many environmental questions. Lack of high spatio-temporal resolution of LST data in Antarctica limits the understanding of climatological, ecological processes. The MODIS LST product is a promising source that provides daily LST data at 1 km spatial resolution, but MODIS LST data have gaps due to cloud cover. This research developed a method to fill those gaps with user-defined options to balance processing time and accuracy of MODIS LST data. The presented method combined temporal and spatial interpolation, using the nearest MODIS Aqua/Terra scene for temporal interpolation, Generalized Additive Model (GAM) using 3-dimensional spatial trend surface, elevation, and aspect as covariates. The moving window size controls the number of filled pixels and the prediction accuracy in the temporal interpolation. A large moving window filled more pixels with less accuracy but improved the overall accuracy of the method. The developed method's performance validated and compared to Local Weighted Regression (LWR) using 14 images and Thin Plate Spline (TPS) interpolation by filling different sizes of artificial gaps 3%, 10%, and 25% of valid pixels. The developed method performed better with a low percentage of cloud cover by RMSE ranged between 0.72 to 1.70 but tended to have a higher RMSE with a high percentage of cloud cover.Meyer, HannaValdes, Maite LezamaGuerrero, IgnacioRUNAlasawedah, Mohammad Hussein Mohammad2021-03-12T16:42:42Z2021-01-292021-01-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113760TID:202672328enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:51:10Zoai:run.unl.pt:10362/113760Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:22:25.051679Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Development of a methodology to fill gaps in MODIS LST data for Antarctica
title Development of a methodology to fill gaps in MODIS LST data for Antarctica
spellingShingle Development of a methodology to fill gaps in MODIS LST data for Antarctica
Alasawedah, Mohammad Hussein Mohammad
Convolutional Neural Networks
Extreme learning machine
title_short Development of a methodology to fill gaps in MODIS LST data for Antarctica
title_full Development of a methodology to fill gaps in MODIS LST data for Antarctica
title_fullStr Development of a methodology to fill gaps in MODIS LST data for Antarctica
title_full_unstemmed Development of a methodology to fill gaps in MODIS LST data for Antarctica
title_sort Development of a methodology to fill gaps in MODIS LST data for Antarctica
author Alasawedah, Mohammad Hussein Mohammad
author_facet Alasawedah, Mohammad Hussein Mohammad
author_role author
dc.contributor.none.fl_str_mv Meyer, Hanna
Valdes, Maite Lezama
Guerrero, Ignacio
RUN
dc.contributor.author.fl_str_mv Alasawedah, Mohammad Hussein Mohammad
dc.subject.por.fl_str_mv Convolutional Neural Networks
Extreme learning machine
topic Convolutional Neural Networks
Extreme learning machine
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2021
dc.date.none.fl_str_mv 2021-03-12T16:42:42Z
2021-01-29
2021-01-29T00:00:00Z
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/10362/113760
TID:202672328
url http://hdl.handle.net/10362/113760
identifier_str_mv TID:202672328
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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