Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms

IEEE 2024 International Geoscience and Remote Sensing Symposium (IGARSS 2024).-- 19 pages, 20 figures, 2 tables

Detalles Bibliográficos
Autores: García Espriu, Aina, González-Haro, Cristina, González Gambau, Verónica, Ruiz-Sebastián, Arnaud, Olmedo, Estrella, Turiel, Antonio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/418928
Acceso en línea:http://hdl.handle.net/10261/418928
Access Level:acceso abierto
Palabra clave:Big data
Projection algorithms
Remote sensing
Sea Surface Salinity (SSS)
Soil Moisture and Ocean Salinity (SMOS)
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spelling Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection AlgorithmsGarcía Espriu, AinaGonzález-Haro, CristinaGonzález Gambau, VerónicaRuiz-Sebastián, ArnaudOlmedo, EstrellaTuriel, AntonioBig dataProjection algorithmsRemote sensingSea Surface Salinity (SSS)Soil Moisture and Ocean Salinity (SMOS)IEEE 2024 International Geoscience and Remote Sensing Symposium (IGARSS 2024).-- 19 pages, 20 figures, 2 tablesSatellite datasets are growing larger due to extended mission durations and improved instrument resolutions, creating challenges in efficiently projecting measurements onto geographical grids. This requires the implementation of Big Data algorithms and specialized data management techniques, with a particular focus on optimizing interpolations and projections. These processing steps are critical as they propagate measurement errors and significantly increase computational time. This work presents a new interpolation algorithm for satellite missions where individual values for each measurement are retrieved. We conduct this study using the sea surface salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission. However, it can easily be extended to other multiangular acquisition missions. We suggest keeping the measurements within the instrument coordinate system (antenna coordinates) until the final product is generated. This allows us to avoid multiple projection-related errors during the intermediate interpolations. Additionally, we introduce a novel algorithm to project those measurements, taking into account the actual area of the acquisitions instead of considering them as points. Therefore, measurements are weighted based on the area they cover over the Earth. This method is numerically optimized to transform 2-D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. The methodology was successfully tested using the SMOS mission’s SSS processor at the Barcelona Expert Center (BEC). Final level 3 SSS maps maintain a high resolution close to the one native on the instrument, enabling the characterization of ocean dynamics at finer scalesThis workw as supported in part by the European Space Agency through the SMOS Expert Support Laboratory (ESL) for SMOS Level 1 and Level 2 over Land, Ocean, and Ice under Grant 4000130567/20/I-BG, in part by MCIN/AEI/10.13039/501100-011033 through the projects EO4TIP and INTERACT under Grant PID2023-149659OB-C21 and Grant PID2020-114623RB-C31, and in part by the CSIC Thematic Interdisciplinary Platform PTI Teledetect, through the “Severo Ochoa Centre of Excellence” accreditation underGrant CEX2019-000928-SPeer reviewedInstitute of Electrical and Electronics EngineersAgencia Estatal de Investigación (España)European Space AgencyMinisterio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/418928reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149659OB-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114623RB-C31https://doi.org/10.1109/JSTARS.2026.3652583Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4189282026-05-22T06:33:51Z
dc.title.none.fl_str_mv Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
title Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
spellingShingle Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
García Espriu, Aina
Big data
Projection algorithms
Remote sensing
Sea Surface Salinity (SSS)
Soil Moisture and Ocean Salinity (SMOS)
title_short Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
title_full Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
title_fullStr Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
title_full_unstemmed Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
title_sort Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms
dc.creator.none.fl_str_mv García Espriu, Aina
González-Haro, Cristina
González Gambau, Verónica
Ruiz-Sebastián, Arnaud
Olmedo, Estrella
Turiel, Antonio
author García Espriu, Aina
author_facet García Espriu, Aina
González-Haro, Cristina
González Gambau, Verónica
Ruiz-Sebastián, Arnaud
Olmedo, Estrella
Turiel, Antonio
author_role author
author2 González-Haro, Cristina
González Gambau, Verónica
Ruiz-Sebastián, Arnaud
Olmedo, Estrella
Turiel, Antonio
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
European Space Agency
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Big data
Projection algorithms
Remote sensing
Sea Surface Salinity (SSS)
Soil Moisture and Ocean Salinity (SMOS)
topic Big data
Projection algorithms
Remote sensing
Sea Surface Salinity (SSS)
Soil Moisture and Ocean Salinity (SMOS)
description IEEE 2024 International Geoscience and Remote Sensing Symposium (IGARSS 2024).-- 19 pages, 20 figures, 2 tables
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/418928
url http://hdl.handle.net/10261/418928
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149659OB-C21
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/JSTARS.2026.3652583
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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