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Global fuel moisture content mapping from MODIS

hal.structure.identifierUniversity of Electronic Science and Technology of China [Chengdu] [UESTC]
dc.contributor.authorQUAN, Xingwen
hal.structure.identifierFenner School of Environment and Society
dc.contributor.authorYEBRA, Marta
hal.structure.identifierCenter for Spatial Technologies and Remote Sensing [CSTARS]
dc.contributor.authorRIAÑO, David
hal.structure.identifierUniversity of Electronic Science and Technology of China [Chengdu] [UESTC]
dc.contributor.authorHE, Binbin
hal.structure.identifierUniversity of Electronic Science and Technology of China [Chengdu] [UESTC]
dc.contributor.authorLAI, Gengke
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLIU, Xiangzhuo
dc.date.accessioned2024-04-08T11:51:00Z
dc.date.available2024-04-08T11:51:00Z
dc.date.issued2021-05-19
dc.identifier.issn1569-8432
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195412
dc.description.abstractEnFuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE = 32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.subject.enFire Danger
dc.subject.enFuel Moisture Content
dc.subject.enGlobal Scale
dc.subject.enModel Inversion
dc.subject.enMODIS
dc.subject.enRadiative Transfer Model
dc.title.enGlobal fuel moisture content mapping from MODIS
dc.typeArticle de revue
dc.identifier.doi10.1016/j.jag.2021.102354
dc.subject.halSciences de l'environnement
bordeaux.journalInternational Journal of Applied Earth Observation and Geoinformation
bordeaux.page1-15
bordeaux.volume101
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03288016
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03288016v1
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.jtitle=International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation&amp;rft.date=2021-05-19&amp;rft.volume=101&amp;rft.spage=1-15&amp;rft.epage=1-15&amp;rft.eissn=1569-8432&amp;rft.issn=1569-8432&amp;rft.au=QUAN,%20Xingwen&amp;YEBRA,%20Marta&amp;RIA%C3%91O,%20David&amp;HE,%20Binbin&amp;LAI,%20Gengke&amp;rft.genre=article


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