Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes

Autores
Caballero, Gabriel; Pezzola, Nestor Alejandro; Winschel, Cristina Ines; Sanchez Angonova, Paolo Andres; Casella, Alejandra An; Orden, Luciano; Salinero-Delgado, Matías; Reyes-Muñoz, Pablo; Berger, Katja; Delegido, Jesús; Verrelst, Jochem
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯¯¯¯wheat−2020 = 0.95, R2¯¯¯¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.
EEA Hilario Ascasubi
Fil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; España
Fil: Salinero-Delgado, Matías. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Reyes-Muñoz, Pablo. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Berger, Katja. Mantle Labs GmbH; Austria
Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España
Fil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); España
Fuente
Remote Sensing 15 (7) : 1822 (March 2023)
Materia
Imágenes por Satélites
Indice de Vegetación
Contenido de Humedad
Teledetección
Nubes
Satellite Imagery
Vegetation Index
Moisture Content
Remote Sensing
Clouds
Sentinel-1
Sentinel-2
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/14389
Acceder
id INTADig_da17aa5c031462af7be7c6d245fc42e4
oai_identifier_str oai:localhost:20.500.12123/14389
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian ProcessesCaballero, GabrielPezzola, Nestor AlejandroWinschel, Cristina InesSanchez Angonova, Paolo AndresCasella, Alejandra AnOrden, LucianoSalinero-Delgado, MatíasReyes-Muñoz, PabloBerger, KatjaDelegido, JesúsVerrelst, JochemImágenes por SatélitesIndice de VegetaciónContenido de HumedadTeledetecciónNubesSatellite ImageryVegetation IndexMoisture ContentRemote SensingCloudsSentinel-1Sentinel-2Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯¯¯¯wheat−2020 = 0.95, R2¯¯¯¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.EEA Hilario AscasubiFil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; EspañaFil: Salinero-Delgado, Matías. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Reyes-Muñoz, Pablo. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Berger, Katja. Mantle Labs GmbH; AustriaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); EspañaFil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); EspañaMDPI2023-04-03T12:14:52Z2023-04-03T12:14:52Z2023-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/14389https://www.mdpi.com/2072-4292/15/7/18222072-4292https://doi.org/10.3390/rs15071822Remote Sensing 15 (7) : 1822 (March 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:45:56Zoai:localhost:20.500.12123/14389instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:45:57.093INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
title Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
spellingShingle Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
Caballero, Gabriel
Imágenes por Satélites
Indice de Vegetación
Contenido de Humedad
Teledetección
Nubes
Satellite Imagery
Vegetation Index
Moisture Content
Remote Sensing
Clouds
Sentinel-1
Sentinel-2
title_short Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
title_full Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
title_fullStr Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
title_full_unstemmed Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
title_sort Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
dc.creator.none.fl_str_mv Caballero, Gabriel
Pezzola, Nestor Alejandro
Winschel, Cristina Ines
Sanchez Angonova, Paolo Andres
Casella, Alejandra An
Orden, Luciano
Salinero-Delgado, Matías
Reyes-Muñoz, Pablo
Berger, Katja
Delegido, Jesús
Verrelst, Jochem
author Caballero, Gabriel
author_facet Caballero, Gabriel
Pezzola, Nestor Alejandro
Winschel, Cristina Ines
Sanchez Angonova, Paolo Andres
Casella, Alejandra An
Orden, Luciano
Salinero-Delgado, Matías
Reyes-Muñoz, Pablo
Berger, Katja
Delegido, Jesús
Verrelst, Jochem
author_role author
author2 Pezzola, Nestor Alejandro
Winschel, Cristina Ines
Sanchez Angonova, Paolo Andres
Casella, Alejandra An
Orden, Luciano
Salinero-Delgado, Matías
Reyes-Muñoz, Pablo
Berger, Katja
Delegido, Jesús
Verrelst, Jochem
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Imágenes por Satélites
Indice de Vegetación
Contenido de Humedad
Teledetección
Nubes
Satellite Imagery
Vegetation Index
Moisture Content
Remote Sensing
Clouds
Sentinel-1
Sentinel-2
topic Imágenes por Satélites
Indice de Vegetación
Contenido de Humedad
Teledetección
Nubes
Satellite Imagery
Vegetation Index
Moisture Content
Remote Sensing
Clouds
Sentinel-1
Sentinel-2
dc.description.none.fl_txt_mv Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯¯¯¯wheat−2020 = 0.95, R2¯¯¯¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.
EEA Hilario Ascasubi
Fil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; España
Fil: Salinero-Delgado, Matías. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Reyes-Muñoz, Pablo. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Berger, Katja. Mantle Labs GmbH; Austria
Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España
Fil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); España
description Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯¯¯¯wheat−2020 = 0.95, R2¯¯¯¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-03T12:14:52Z
2023-04-03T12:14:52Z
2023-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/14389
https://www.mdpi.com/2072-4292/15/7/1822
2072-4292
https://doi.org/10.3390/rs15071822
url http://hdl.handle.net/20.500.12123/14389
https://www.mdpi.com/2072-4292/15/7/1822
https://doi.org/10.3390/rs15071822
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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 Remote Sensing 15 (7) : 1822 (March 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.559606

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