Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets

IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 6 figures.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any c...

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Detalles Bibliográficos
Autores: García Espriu, Aina, González-Haro, Cristina, González Gambau, Verónica, Olmedo, Estrella, Turiel, Antonio
Tipo de recurso: otro
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/369551
Acceso en línea:http://hdl.handle.net/10261/369551
Access Level:acceso abierto
Palabra clave:Sea Surface Salinity
Remote Sensing
SMOS
Big Data
Projection Algorithms
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spelling Improved Projection Algorithms for Long-Term and High-Resolution Satellite DatasetsGarcía Espriu, AinaGonzález-Haro, CristinaGonzález Gambau, VerónicaOlmedo, EstrellaTuriel, AntonioSea Surface SalinityRemote SensingSMOSBig DataProjection AlgorithmsIEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 6 figures.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksSatellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining reasonable computational time and costs can be challenging. It is thus necessary to include big data algorithms and dedicated management techniques in the processing of such datasets.In this regard, the optimization of the number of interpolations and projections is one of the key aspects, as the native errors of the measurements propagate at each processing step. Besides each interpolation and projection implies a non-negligible increase in total computational time.This work is based on the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission, but it could be easily extended to any other satellite mission where individual values for each measurement are retrieved. We propose a redefinition of the complete processor chain so it can work with the measurements within the instrument coordinate system. This allows us to avoid projection-related errors during the generation of the final product.Additionally, we introduce a novel algorithm to project those measurements taking into account the actual spatial extent of the acquisitions instead of taking them as points, so measures are averaged weighted by the area they cover on the Earth-based grid. This method is optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. Our algorithm has demonstrated its potential when incorporated into the SMOS SSS processor at the Barcelona Expert Center (BEC), allowing us to keep a final resolution very close to the one attained at the antenna coordinate systemThe work has been funded by the European Space Agency through the SMOS Expert Support Laboratory (ESL) for SMOS Level 1 and Level 2 over Land, Ocean, and Ice (4000130567/20/I-BG) and by MCIN/AEI/10.13039/501100- 011033 through the project INTERACT (PID2020-114623RB-C31). This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI Teledetect, with the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewedInstitute of Electrical and Electronics EngineersEuropean Space AgencyMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_3248Postprintinfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/bookParthttp://hdl.handle.net/10261/369551reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114623RB-C31https://doi.org/10.1109/IGARSS53475.2024.10641832Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3695512026-05-22T06:33:51Z
dc.title.none.fl_str_mv Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
title Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
spellingShingle Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
García Espriu, Aina
Sea Surface Salinity
Remote Sensing
SMOS
Big Data
Projection Algorithms
title_short Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
title_full Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
title_fullStr Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
title_full_unstemmed Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
title_sort Improved Projection Algorithms for Long-Term and High-Resolution Satellite Datasets
dc.creator.none.fl_str_mv García Espriu, Aina
González-Haro, Cristina
González Gambau, Verónica
Olmedo, Estrella
Turiel, Antonio
author García Espriu, Aina
author_facet García Espriu, Aina
González-Haro, Cristina
González Gambau, Verónica
Olmedo, Estrella
Turiel, Antonio
author_role author
author2 González-Haro, Cristina
González Gambau, Verónica
Olmedo, Estrella
Turiel, Antonio
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Space Agency
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Sea Surface Salinity
Remote Sensing
SMOS
Big Data
Projection Algorithms
topic Sea Surface Salinity
Remote Sensing
SMOS
Big Data
Projection Algorithms
description IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 6 figures.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/other
http://purl.org/coar/resource_type/c_3248
Postprint
info:eu-repo/semantics/acceptedVersion
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format other
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/369551
url http://hdl.handle.net/10261/369551
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114623RB-C31
https://doi.org/10.1109/IGARSS53475.2024.10641832
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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