On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation

[EN]While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign pe...

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Autores: Sánchez Martín, Nilda, Alonso-Arroyo, Alberto, Martínez Fernández, José, Piles, María, González Zamora, Ángel, Camps, Adriano, Vall-llosera, Mercè
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Recursos:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/167262
Acesso em linha:http://hdl.handle.net/10366/167262
Access Level:acceso abierto
Palavra-chave:GNSS-R
Landsat 8
Airborne
Soil Moisture
Reflectivity
Temperature
Synergy
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spelling On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture EstimationSánchez Martín, NildaAlonso-Arroyo, AlbertoMartínez Fernández, JoséPiles, MaríaGonzález Zamora, ÁngelCamps, AdrianoVall-llosera, MercèGNSS-RLandsat 8AirborneSoil MoistureReflectivityTemperatureSynergy[EN]While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politècnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data).MDPI202520252015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10366/167262reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésE-GEM-ID 607126AYA2011-29183- C02-01AYA2012-39356-C05info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1672622026-06-07T06:28:51Z
dc.title.none.fl_str_mv On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
title On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
spellingShingle On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
Sánchez Martín, Nilda
GNSS-R
Landsat 8
Airborne
Soil Moisture
Reflectivity
Temperature
Synergy
title_short On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
title_full On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
title_fullStr On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
title_full_unstemmed On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
title_sort On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation
dc.creator.none.fl_str_mv Sánchez Martín, Nilda
Alonso-Arroyo, Alberto
Martínez Fernández, José
Piles, María
González Zamora, Ángel
Camps, Adriano
Vall-llosera, Mercè
author Sánchez Martín, Nilda
author_facet Sánchez Martín, Nilda
Alonso-Arroyo, Alberto
Martínez Fernández, José
Piles, María
González Zamora, Ángel
Camps, Adriano
Vall-llosera, Mercè
author_role author
author2 Alonso-Arroyo, Alberto
Martínez Fernández, José
Piles, María
González Zamora, Ángel
Camps, Adriano
Vall-llosera, Mercè
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv GNSS-R
Landsat 8
Airborne
Soil Moisture
Reflectivity
Temperature
Synergy
topic GNSS-R
Landsat 8
Airborne
Soil Moisture
Reflectivity
Temperature
Synergy
description [EN]While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politècnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data).
publishDate 2015
dc.date.none.fl_str_mv 2015
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/167262
url http://hdl.handle.net/10366/167262
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv E-GEM-ID 607126
AYA2011-29183- C02-01
AYA2012-39356-C05
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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