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

Detalhes bibliográficos
Autores: García Espriu, Aina, González-Haro, Cristina, González Gambau, Verónica, Ruiz-Sebastián, Arnaud, Olmedo, Estrella, Turiel, Antonio
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/418928
Acesso em linha:http://hdl.handle.net/10261/418928
Access Level:acceso abierto
Palavra-chave: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 Algorithms García Espriu, Aina González-Haro, Cristina González Gambau, Verónica Ruiz-Sebastián, Arnaud Olmedo, Estrella Turiel, Antonio Big data Projection algorithms Remote sensing Sea Surface Salinity (SSS) Soil Moisture and Ocean Salinity (SMOS) IEEE 2024 International Geoscience and Remote Sensing Symposium (IGARSS 2024).-- 19 pages, 20 figures, 2 tables Satellite 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 scales This 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-S Peer reviewed Institute of Electrical and Electronics Engineers http://hdl.handle.net/10261/418928
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
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
topic Big data
Projection algorithms
Remote sensing
Sea Surface Salinity (SSS)
Soil Moisture and Ocean Salinity (SMOS)
topic_facet 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
format article
status_str publishedVersion
url http://hdl.handle.net/10261/418928
eu_rights_str_mv openAccess
publisher Institute of Electrical and Electronics Engineers
institution Consejo Superior de Investigaciones Científicas (CSIC)
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
_version_ 1878431620804902912
publishDateSort 2026
author_browse García Espriu, Aina
González Gambau, Verónica
González-Haro, Cristina
Olmedo, Estrella
Ruiz-Sebastián, Arnaud
Turiel, Antonio
publisherStr Institute of Electrical and Electronics Engineers
score 6,924472

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