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, David, Jagdhuber, Thomas, Piles, María, Jonard, François, Fluhrer, Anke, Vall-llossera, Mercè, Camps, Adriano, López-Martínez, Carlos, Fernández-Morán, Roberto, Baur, Martín J., Feldman, Andrew F., Fink, Anita, Entekhabi, Dara
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/357930
Acesso em linha:http://hdl.handle.net/10261/357930
Access Level:acceso abierto
Palavra-chave:Live fuel moisture content (LFMC)
Gravimetric vegetation moisture (mg)
Vegetation optical depth
SMAP
AMSR-2
Sentinel-1
GEDI
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repository_id_str
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, David
Live fuel moisture content (LFMC)
Gravimetric vegetation moisture (mg)
Vegetation optical depth
SMAP
AMSR-2
Sentinel-1
GEDI
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, David
Jagdhuber, Thomas
Piles, María
Jonard, François
Fluhrer, Anke
Vall-llossera, Mercè
Camps, Adriano
López-Martínez, Carlos
Fernández-Morán, Roberto
Baur, Martín J.
Feldman, Andrew F.
Fink, Anita
Entekhabi, Dara
author Chaparro, David
author_facet Chaparro, David
Jagdhuber, Thomas
Piles, María
Jonard, François
Fluhrer, Anke
Vall-llossera, Mercè
Camps, Adriano
López-Martínez, Carlos
Fernández-Morán, Roberto
Baur, Martín J.
Feldman, Andrew F.
Fink, Anita
Entekhabi, Dara
author_role author
author2 Jagdhuber, Thomas
Piles, María
Jonard, François
Fluhrer, Anke
Vall-llossera, Mercè
Camps, Adriano
López-Martínez, Carlos
Fernández-Morán, Roberto
Baur, Martín J.
Feldman, Andrew F.
Fink, Anita
Entekhabi, Dara
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Fundación Ramón Areces
Fundación la Caixa
Generalitat Valenciana
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Ministerio de Ciencia e Innovación (España)
NASA
European Commission
dc.subject.none.fl_str_mv Live fuel moisture content (LFMC)
Gravimetric vegetation moisture (mg)
Vegetation optical depth
SMAP
AMSR-2
Sentinel-1
GEDI
topic Live fuel moisture content (LFMC)
Gravimetric vegetation moisture (mg)
Vegetation optical depth
SMAP
AMSR-2
Sentinel-1
GEDI
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
2024
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/10261/357930
url http://hdl.handle.net/10261/357930
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 2017-2020/PID2020-114623RB-C32
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096765-A-I00
https://doi.org/10.1016/j.rse.2024.113993
No
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
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spelling Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergyChaparro, DavidJagdhuber, ThomasPiles, MaríaJonard, FrançoisFluhrer, AnkeVall-llossera, MercèCamps, AdrianoLópez-Martínez, CarlosFernández-Morán, RobertoBaur, Martín J.Feldman, Andrew F.Fink, AnitaEntekhabi, DaraLive fuel moisture content (LFMC)Gravimetric vegetation moisture (mg)Vegetation optical depthSMAPAMSR-2Sentinel-1GEDIMonitoring 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 reviewedElsevierFundación Ramón ArecesFundación la CaixaGeneralitat ValencianaAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Ministerio de Ciencia e Innovación (España)NASAEuropean Commission202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/357930reponame: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 2017-2020/PID2020-114623RB-C32info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096765-A-I00https://doi.org/10.1016/j.rse.2024.113993Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3579302026-05-22T06:33:51Z
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