Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Chaparro Danon, David|||0000-0002-5545-6182; Jagdhuber, Thomas; Piles Guillem, María; Jonard, François; Flührer, Anke; Vall-Llossera Ferran, Mercedes Magdalena|||0000-0003-1357-7098; Camps Carmona, Adriano José|||0000-0002-9514-4992; López Martínez, Carlos|||0000-0002-1366-9446; Fernández Morán, Roberto; Baur, Martin J.; Feldman, Andrew F.; Fink, Anita; Entekhabi, Dara

Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly...

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Autores:	Chaparro Danon, David\|\|\|0000-0002-5545-6182, Jagdhuber, Thomas, Piles Guillem, María, Jonard, François, Flührer, Anke, Vall-Llossera Ferran, Mercedes Magdalena\|\|\|0000-0003-1357-7098, Camps Carmona, Adriano José\|\|\|0000-0002-9514-4992, López Martínez, Carlos\|\|\|0000-0002-1366-9446, Fernández Morán, Roberto, Baur, Martin J., Feldman, Andrew F., Fink, Anita, Entekhabi, Dara
Tipo de recurso:	artículo
Fecha de publicación:	2024
País:	España
Institución:	Universitat Politècnica de Catalunya (UPC)
Repositorio:	UPCommons. Portal del coneixement obert de la UPC
Idioma:	inglés
OAI Identifier:	oai:upcommons.upc.edu:2117/406666
Acceso en línea:	https://hdl.handle.net/2117/406666 https://dx.doi.org/10.1016/j.rse.2024.113993
Access Level:	acceso abierto
Palabra clave:	Soil moisture -- Measurement Vegetation monitoring Microwave remote sensing Live fuel moisture content (LFMC) Gravimetric vegetation moisture (mg) Vegetation optical depth SMAP AMSR-2 Sentinel-1 GEDI Sòls -- Humitat -- Mesurament Teledetecció per microones Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció

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dc.title.none.fl_str_mv	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
title	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
spellingShingle	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy Chaparro Danon, David\|\|\|0000-0002-5545-6182 Soil moisture -- Measurement Vegetation monitoring Microwave remote sensing Live fuel moisture content (LFMC) Gravimetric vegetation moisture (mg) Vegetation optical depth SMAP AMSR-2 Sentinel-1 GEDI Sòls -- Humitat -- Mesurament Teledetecció per microones Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
title_short	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
title_full	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
title_fullStr	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
title_full_unstemmed	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
title_sort	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy
dc.creator.none.fl_str_mv	Chaparro Danon, David\|\|\|0000-0002-5545-6182 Jagdhuber, Thomas Piles Guillem, María Jonard, François Flührer, Anke Vall-Llossera Ferran, Mercedes Magdalena\|\|\|0000-0003-1357-7098 Camps Carmona, Adriano José\|\|\|0000-0002-9514-4992 López Martínez, Carlos\|\|\|0000-0002-1366-9446 Fernández Morán, Roberto Baur, Martin J. Feldman, Andrew F. Fink, Anita Entekhabi, Dara
author	Chaparro Danon, David\|\|\|0000-0002-5545-6182
author_facet	Chaparro Danon, David\|\|\|0000-0002-5545-6182 Jagdhuber, Thomas Piles Guillem, María Jonard, François Flührer, Anke Vall-Llossera Ferran, Mercedes Magdalena\|\|\|0000-0003-1357-7098 Camps Carmona, Adriano José\|\|\|0000-0002-9514-4992 López Martínez, Carlos\|\|\|0000-0002-1366-9446 Fernández Morán, Roberto Baur, Martin J. Feldman, Andrew F. Fink, Anita Entekhabi, Dara
author_role	author
author2	Jagdhuber, Thomas Piles Guillem, María Jonard, François Flührer, Anke Vall-Llossera Ferran, Mercedes Magdalena\|\|\|0000-0003-1357-7098 Camps Carmona, Adriano José\|\|\|0000-0002-9514-4992 López Martínez, Carlos\|\|\|0000-0002-1366-9446 Fernández Morán, Roberto Baur, Martin J. Feldman, Andrew F. Fink, Anita Entekhabi, Dara
author2_role	author author author author author author author author author author author author
dc.subject.none.fl_str_mv	Soil moisture -- Measurement Vegetation monitoring Microwave remote sensing Live fuel moisture content (LFMC) Gravimetric vegetation moisture (mg) Vegetation optical depth SMAP AMSR-2 Sentinel-1 GEDI Sòls -- Humitat -- Mesurament Teledetecció per microones Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
topic	Soil moisture -- Measurement Vegetation monitoring Microwave remote sensing Live fuel moisture content (LFMC) Gravimetric vegetation moisture (mg) Vegetation optical depth SMAP AMSR-2 Sentinel-1 GEDI Sòls -- Humitat -- Mesurament Teledetecció per microones Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
description	Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.
publishDate	2024
dc.date.none.fl_str_mv	2024 2024-03-15 2024 2024-04-18
dc.type.none.fl_str_mv	journal article http://purl.org/coar/resource_type/c_6501 AM http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv	info:eu-repo/semantics/article
format	article
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2117/406666 https://dx.doi.org/10.1016/j.rse.2024.113993
url	https://hdl.handle.net/2117/406666 https://dx.doi.org/10.1016/j.rse.2024.113993
dc.language.none.fl_str_mv	Inglés eng
language_invalid_str_mv	Inglés
language	eng
dc.relation.none.fl_str_mv	Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114623RB-C32 ENFOQUES SINERGICOS PARA UNA NUEVA GENERACION DE PRODUCTOS Y APLICACIONES DE OBSERVACION DE LA TIERRA. PARTE UPC
dc.rights.none.fl_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Elsevier
publisher.none.fl_str_mv	Elsevier
dc.source.none.fl_str_mv	reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC)
instname_str	Universitat Politècnica de Catalunya (UPC)
reponame_str	UPCommons. Portal del coneixement obert de la UPC
collection	UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
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spelling	Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergyChaparro Danon, David\|\|\|0000-0002-5545-6182Jagdhuber, ThomasPiles Guillem, MaríaJonard, FrançoisFlührer, AnkeVall-Llossera Ferran, Mercedes Magdalena\|\|\|0000-0003-1357-7098Camps Carmona, Adriano José\|\|\|0000-0002-9514-4992López Martínez, Carlos\|\|\|0000-0002-1366-9446Fernández Morán, RobertoBaur, Martin J.Feldman, Andrew F.Fink, AnitaEntekhabi, DaraSoil moisture -- MeasurementVegetation monitoringMicrowave remote sensingLive fuel moisture content (LFMC)Gravimetric vegetation moisture (mg)Vegetation optical depthSMAPAMSR-2Sentinel-1GEDISòls -- Humitat -- MesuramentTeledetecció per microonesÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::TeledeteccióMonitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.The work of D. Chaparro was supported by the XXXIII Ramón Areces Postdoctoral Fellowship and by MIT and the “la Caixa” Foundation (ID 100010434) under Grant LCF/ PR/MIT19/51840001 (MIT-Spain Seed Fund; D. Entekhabi, D. Chaparro). M. Piles thanks the support of Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital through the project AI4CS CIPROM/2021/56. M. Vall-llossera acknowledges funding from the Grant PID2020-114623RB-C32, funded by MCIN/AEI/10.13039/ 501100011033, and from the ERDF under Grant RTI2018-096765-A- 100. Also, the authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund (D. Entekhabi, T. Jagdhuber) and with the MIT-Belgium Seed Fund (D. Entekhabi, F. Jonard). A.F. Feldman was supported by both the ECOSTRESS science team and by a NASA Terrestrial Ecology scoping study for a dryland field campaign.Peer ReviewedElsevier20242024-03-1520242024-04-18journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/406666https://dx.doi.org/10.1016/j.rse.2024.113993reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114623RB-C32 ENFOQUES SINERGICOS PARA UNA NUEVA GENERACION DE PRODUCTOS Y APLICACIONES DE OBSERVACION DE LA TIERRA. PARTE UPCopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4066662026-05-27T15:37:01Z
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Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

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