{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T02:49:27Z","timestamp":1772765367727,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA","award":["NNX11AF93G"],"award-info":[{"award-number":["NNX11AF93G"]}]},{"name":"Seventh Framework Programme","award":["FP7-PEOPLE-2009-IRSES-246666"],"award-info":[{"award-number":["FP7-PEOPLE-2009-IRSES-246666"]}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["CGL2015-G9095-R"],"award-info":[{"award-number":["CGL2015-G9095-R"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009068","name":"CONICYT","doi-asserted-by":"publisher","award":["Doctoral Fellowship"],"award-info":[{"award-number":["Doctoral Fellowship"]}],"id":[{"id":"10.13039\/501100009068","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000\u20132011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.<\/jats:p>","DOI":"10.3390\/rs12111714","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6260-5791","authenticated-orcid":false,"given":"Mariano","family":"Garc\u00eda","sequence":"first","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcal\u00e1. Calle Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"},{"name":"Complutum Tecnolog\u00edas de la Informaci\u00f3n Geogr\u00e1fica S.L. (COMPLUTIG), Colegios, 2, 28801 Alcal\u00e1 de Henares, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-1424","authenticated-orcid":false,"given":"David","family":"Ria\u00f1o","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), 28037 Madrid, Spain"},{"name":"Center for Spatial Technologies and Remote Sensing (CSTARS), John Muir Institute of the Environment, University of California Davis, One Shields Drive, Davis, CA 95616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-9315","authenticated-orcid":false,"given":"Marta","family":"Yebra","sequence":"additional","affiliation":[{"name":"Fenner School of Environment &amp; Society, Colleges of Science, The Australian National University, Acton, ACT 2601, Australia"},{"name":"Bushfire and Natural Hazards Cooperative Research Centre, 340 Albert St., East Melbourne, Victoria 3002, Australia"},{"name":"Research School of Aerospace, Mechanical and Environmental Engineering, College of Engineering and Computer Science, The Australian National University, Acton, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8208-6703","authenticated-orcid":false,"given":"Javier","family":"Salas","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcal\u00e1. Calle Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0185-3959","authenticated-orcid":false,"given":"Adri\u00e1n","family":"Cardil","sequence":"additional","affiliation":[{"name":"Technosylva, 24009 Le\u00f3n, Spain"},{"name":"Technosylva, La Jolla, CA 92037-7231, USA"}]},{"given":"Santiago","family":"Monedero","sequence":"additional","affiliation":[{"name":"Technosylva, 24009 Le\u00f3n, Spain"}]},{"given":"Joaqu\u00edn","family":"Ramirez","sequence":"additional","affiliation":[{"name":"Technosylva, La Jolla, CA 92037-7231, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5563-8461","authenticated-orcid":false,"given":"M. Pilar","family":"Mart\u00edn","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), 28037 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0872-1235","authenticated-orcid":false,"given":"Lara","family":"Vilar","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), 28037 Madrid, Spain"}]},{"given":"John","family":"Gajardo","sequence":"additional","affiliation":[{"name":"Instituto de Bosques y Sociedad, Facultad de Ciencias Forestales Y Recursos Naturales, Universidad Austral de Chile, Campus Isla Teja, Valdivia 5090000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0461","authenticated-orcid":false,"given":"Susan","family":"Ustin","sequence":"additional","affiliation":[{"name":"Center for Spatial Technologies and Remote Sensing (CSTARS), John Muir Institute of the Environment, University of California Davis, One Shields Drive, Davis, CA 95616, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1080\/0143116021000021152","article-title":"Classification analyses of vegetation for delineating forest fire fuel complexes in a Mediterranean test site using satellite remote sensing and GIS","volume":"24","author":"Koutsias","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Burgan, R.E., and Rothermel, R.C. (1984). BEHAVE: Fire Behavior Prediction and Fuel Modeling System. Fuel Subsystem, GTR INT-167.","DOI":"10.2737\/INT-GTR-167"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1071\/WF09008","article-title":"Ignition and fire spread thresholds in gorse (Ulex europaeus)","volume":"19","author":"Anderson","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Finney, M.A. (1998). FARSITE: Fire Area Simulator\u2014Model Development and Evaluation, RMRS-RP-4.","DOI":"10.2737\/RMRS-RP-4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1071\/WF9920069","article-title":"Moisture content of fine forest fuels and fire occurrence in central Portugal","volume":"2","author":"Viegas","year":"1992","journal-title":"Int. J. Wildland Fire"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1071\/WF18091","article-title":"Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands?","volume":"28","author":"Pimont","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1071\/WF09038","article-title":"The initiation of fire spread in shrubland fuels recreated in the laboratory","volume":"19","author":"Plucinski","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1071\/WF08188","article-title":"Effect of drying temperature on fuel moisture content measurements","volume":"19","author":"Matthews","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_9","unstructured":"(2020, April 03). User Guide for the Glossary of Wildland Fire. PMS 937, Available online: https:\/\/www.nwcg.gov\/publications\/937."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1111\/j.1654-109X.2009.01057.x","article-title":"Short-term propagation of rainfall perturbations on terrestrial ecosystems in central California","volume":"13","author":"Garcia","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.rse.2013.05.029","article-title":"A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products","volume":"136","author":"Yebra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2003.08.015","article-title":"Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index","volume":"88","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1016\/j.rse.2008.05.002","article-title":"Combining AVHRR and meteorological data for estimating live fuel moisture content","volume":"112","author":"Garcia","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Roberts, D.A., Dennison, P.E., Peterson, S., Sweeney, S., and Rechel, J. (2006). Evaluation of airborne visible\/infrared imaging spectrometer (AVIRIS) and moderate resolution imaging spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. J. Geophys. Res. Biogeosci., 111.","DOI":"10.1029\/2005JG000113"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.03.011","article-title":"Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response","volume":"148","author":"Casas","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2013.01.004","article-title":"Regional estimation of woodland moisture content by inverting Radiative Transfer Models","volume":"132","author":"Jurdao","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.agrformet.2007.12.005","article-title":"Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment","volume":"148","author":"Yebra","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2018.04.053","article-title":"A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing","volume":"212","author":"Yebra","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Ramirez, J., Monedero, S., and Buckley, D. (2011, January 9\u201313). New approaches in fire simulations analysis with Wildfire Analyst. Proceedings of the 5th International Wildland Fire Conference, Sun City, South Africa."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/j.1365-2486.2005.00930.x","article-title":"A generalized, bioclimatic index to predict foliar phenology in response to climate","volume":"11","author":"Jolly","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2004.01.019","article-title":"Combining NDVI and Surface Temperature for the estimation of live fuels moisture content in forest fire danger rating","volume":"92","author":"Chuvieco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1080\/01431160500185342","article-title":"MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content","volume":"26","author":"Stow","year":"2005","journal-title":"Int. J. Remote Sens"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/01431160110069818","article-title":"Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment","volume":"23","author":"Chuvieco","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.3390\/rs70201482","article-title":"Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Burgan, R.E., and Hartford, R.A. (1993). Monitoring Vegetation Greenness with Satellite Data, GTR INT-297.","DOI":"10.2737\/INT-GTR-297"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1071\/WF11024","article-title":"Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data","volume":"21","author":"Caccamo","year":"2012","journal-title":"Int. J. Wildland Fire"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1016\/j.rse.2011.02.005","article-title":"Relative Greenness Index for assessing curing of grassland fuel","volume":"115","author":"Newnham","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5175","DOI":"10.1080\/01431160701616129","article-title":"Stability, normalization and accuracy of MODIS-derived estimates of live fuel moisture for southern California chaparral","volume":"28","author":"Stow","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","unstructured":"Pollet, J., and Brown, A. (2007). Fuel Moisture Sampling Guide."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T.-K. (2006). A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote Sens. Lett., 3.","DOI":"10.1109\/LGRS.2005.857030"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS+C: A modified sun-canopy-sensor topographic correction in forested terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_32","first-page":"1303","article-title":"Topographic Normalization of Landsat Thematic Mapper Digital Imagery","volume":"55","author":"Civco","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/07038992.1982.10855028","article-title":"On the slope-aspect correction of multispectral scanner data","volume":"8","author":"Teillet","year":"1982","journal-title":"Can. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TGRS.2003.811693","article-title":"Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types","volume":"41","author":"Chuvieco","year":"2003","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_35","first-page":"858","article-title":"Completion of the 2006 national land cover database for the conterminous united states","volume":"77","author":"Fry","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/01431169208904049","article-title":"High-espectral resolution data for determining leaf water content","volume":"13","author":"Danson","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5523","DOI":"10.5194\/bg-12-5523-2015","article-title":"Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site","volume":"12","author":"Mendiguren","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Scott, J.H., and Burgan, R.E. (2005). Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel\u2019s Surface fire Spread Model.","DOI":"10.2737\/RMRS-GTR-153"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.envsoft.2017.02.023","article-title":"Simulating wildfires backwards in time from the final fire perimeter in point-functional fire models","volume":"92","author":"Monedero","year":"2017","journal-title":"Environ. Modell. Softw."},{"key":"ref_44","unstructured":"Rothermel, R.C. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels, Research Paper INT-115."},{"key":"ref_45","unstructured":"Finney, M. (2006, January 28\u201330). An overview of FlamMap fire modeling capabilities. Proceedings of the Fuels Management\u2014How to Measure Success: Conference Proceedings, Portland, OR, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rothermel, R.C. (1983). How to Predict the Spread and Intensity of Forest and Range Fires, GTR INT-143.","DOI":"10.2737\/INT-GTR-143"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2012.2235447","article-title":"Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity","volume":"51","author":"Gonsamo","year":"2013","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3390\/rs6010310","article-title":"Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM plus) and Landsat-8 Operational Land Imager (OLI) Sensors","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2014.05.004","article-title":"Spectroscopic analysis of seasonal changes in live fuel moisture content and leaf dry mass","volume":"150","author":"Qi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1071\/WF15156","article-title":"Seasonal relationships between foliar moisture content, heat content and biochemistry of lodgepole line and big sagebrush foliage","volume":"25","author":"Qi","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1071\/WF07017","article-title":"Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California","volume":"17","author":"Dennison","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_53","unstructured":"Yebra, M., Ria\u00f1o, D., Quan, X., Mouillot, F., Paget, E., Di Bella, C.M., Garc\u00eda, M., Mart\u00edn, P., van Dijk, A., and Cary, G.J. (2017, January 18\u201322). Global validation of Live Fuel Moisture Content (LFMC) products from satellite MODIS. Proceedings of the 5th International Symposium Recent Advances in Quantitative Remote Sensing Torrent, Valencia, Spain."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1111\/2041-210X.12554","article-title":"A simple and effective method to collect leaves and seeds from tall trees","volume":"7","author":"Youngentob","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2013.11.018","article-title":"Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis","volume":"143","author":"Cheng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.12.024","article-title":"Detection of diurnal variation in orchard canopy water content using MODIS\/ASTER airborne simulator (MASTER) data","volume":"132","author":"Cheng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1560\/IJPS.60.1-2.9","article-title":"Estimating Canopy Water Content from Spectroscopy","volume":"60","author":"Ustin","year":"2012","journal-title":"Isr. J. Plant Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1071\/WF12008","article-title":"Assessing the effect of foliar moisture on the spread rate of crown fires","volume":"22","author":"Alexander","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/TGRS.2010.2050488","article-title":"Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band","volume":"49","author":"Calvet","year":"2011","journal-title":"IEEE Trans. Geosci. Remote"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1714\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:58Z","timestamp":1760175178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,27]]},"references-count":59,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111714"],"URL":"https:\/\/doi.org\/10.3390\/rs12111714","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,27]]}}}