{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T11:07:06Z","timestamp":1778324826629,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["CNME19\/0304\/42."],"award-info":[{"award-number":["CNME19\/0304\/42."]}],"id":[{"id":"10.13039\/501100003359","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) is an input factor in fire behavior simulation models highly contributing to fire ignition and propagation. Developing models capable of accurately estimating spatio-temporal changes of LFMC in different forest species is needed for wildfire risk assessment. In this paper, an empirical model based on multivariate linear regression was constructed for the forest cover classified as shrublands in the central part of the Valencian region in the Eastern Mediterranean of Spain in the fire season. A sample of 15 non-monospecific shrubland sites was used to obtain a spatial representation of this type of forest cover in that area. A prediction model was created by combining spectral indices and meteorological variables. This study demonstrates that the Normalized Difference Moisture Index (NDMI) extracted from Sentinel-2 images and meteorological variables (mean surface temperature and mean wind speed) are a promising combination to derive cost-effective LFMC estimation models. The relationships between LFMC and spectral indices for all sites improved after using an additive site-specific index based on satellite information, reaching a R2adj = 0.70, RMSE = 8.13%, and MAE = 6.33% when predicting the average of LFMC weighted by the canopy cover fraction of each species of all shrub species present in each sampling plot.<\/jats:p>","DOI":"10.3390\/rs13183726","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"3726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-6111","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Costa-Saura","sequence":"first","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy"},{"name":"Euro-Mediterranean Center on Climate Change (CMCC), IAFES Division, Via De Nicola 9, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0039-2641","authenticated-orcid":false,"given":"\u00c1ngel","family":"Balaguer-Beser","sequence":"additional","affiliation":[{"name":"Geo-Environmental Cartography and Remote Sensing Group (CGAT-UPV), Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0073-7259","authenticated-orcid":false,"given":"Luis A.","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Geo-Environmental Cartography and Remote Sensing Group (CGAT-UPV), Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0471-9795","authenticated-orcid":false,"given":"Josep E.","family":"Pardo-Pascual","sequence":"additional","affiliation":[{"name":"Geo-Environmental Cartography and Remote Sensing Group (CGAT-UPV), Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 L.","family":"Soriano-Sancho","sequence":"additional","affiliation":[{"name":"Technical Unit for Analysis and Prevention of Forest Fires, (VAERSA), Direcci\u00f3 General de Prevenci\u00f3 d\u2019Incendis Forestals, Generalitat Valenciana, Calle de la Democracia, 77 Torre I, 46018 Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"ref_1","unstructured":"Dimitriou, A., Mantakas, G., and Kouvelis, S. (2021, August 31). An Analysis of Key Issues that Underlie Forest Fires and Shape Subsequent Fire Management Strategies in 12 Countries in the Mediterranean Basin; Final report prepared by Alcyon for WWF Mediterranean Programme Office and IUCN. Available online: https:\/\/ec.europa.eu\/environment\/forests\/pdf\/meeting140504_wwfsecondocument.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1071\/WF07151","article-title":"Are wildfires a disaster in the Mediterranean basin?\u2014A review","volume":"17","author":"Pausas","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1111\/brv.12544","article-title":"Fire as a key driver of Earth\u2019s biodiversity","volume":"94","author":"He","year":"2019","journal-title":"Biol. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, M.R., Delogu, G.M., Fernandes, P.M., Ferreira, C., McCaffrey, S., and McGee, T.K. (2018). Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts. Fire, 1.","DOI":"10.3390\/fire1010009"},{"key":"ref_5","unstructured":"San-Miguel-Ayanz, J., Durrant, T.H., Boca, R., Liberta, G., Branco, A., de Rigo, G., Ferrari, D., Maianti, P., Vivancos, T., and Oom, D. (2019). Forest Fires in Europe, Middle East and North Africa 2018, Technical Report EUR 29856 EN; Publications Office of the European Union."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ribeiro, L., Viegas, D., Almeida, M., McGee, T.K., Pereira, M.G., Parente, J., Xanthopoulos, G., Leone, V., Delogu, G.M., and Hardin, H. (2020). Extreme wildfires and disasters around the world. Extreme Wildfire Events and Disasters, Root Causes and New Management Strategies, Elsevier.","DOI":"10.1016\/B978-0-12-815721-3.00002-3"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13595-020-00933-5","article-title":"Climate change impact on future wildfire danger and activity in southern Europe: A review","volume":"77","author":"Dupuy","year":"2020","journal-title":"Ann. For. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s13595-019-0829-8","article-title":"The 10% wind speed rule of thumb for estimating a wildfire\u2019s forward rate of spread in forests and shrublands","volume":"76","author":"Cruz","year":"2019","journal-title":"Ann. For. Sci."},{"key":"ref_9","unstructured":"Brown, A.A., and Davis, K.P. (1973). Fire Danger Rating. Forest Fire: Control and Use, Mac Graw Hill. [2nd ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1071\/WF19193","article-title":"Soil moisture as an indicator of growing-season herbaceous fuel moisture and curing rate in grasslands","volume":"30","author":"Sharma","year":"2021","journal-title":"Int. J. Wildland Fire"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecolmodel.2008.11.017","article-title":"Development of a framework for fire risk assessment using remote sensing and geographic information system technologies","volume":"221","author":"Chuvieco","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13595-016-0599-5","article-title":"Characterizing potential wildland fire fuel in live vegetation in the Mediterranean region","volume":"74","author":"Fares","year":"2017","journal-title":"Ann. For. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s13595-018-0729-3","article-title":"Live fuel moisture content (LFMC) time series for multiple sites and species in the French Mediterranean area since 1996","volume":"75","author":"Pimont","year":"2018","journal-title":"Ann. For. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/s41597-019-0164-9","article-title":"Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications","volume":"6","author":"Yebra","year":"2019","journal-title":"Sci. Data"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13595-021-01057-0","article-title":"Live fuel moisture content time series in Catalonia since 1998","volume":"78","author":"Gabriel","year":"2021","journal-title":"Ann. For. Sci."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Luo, K., Quan, X., He, B., and Yebra, M. (2019). Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests, 10.","DOI":"10.3390\/f10100887"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1109\/JSTARS.2016.2575366","article-title":"Estimation of Live Fuel Moisture Content From MODIS Images for Fire Danger Assessment in Southern Gran Chaco","volume":"9","author":"Arganaraz","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/d41586-020-02306-4","article-title":"Wildfires: Australia needs national monitoring agency","volume":"584","author":"Bowman","year":"2020","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1071\/WF13127","article-title":"De-coupling seasonal changes in water content and dry matter to predict live conifer foliar moisture content","volume":"23","author":"Jolly","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1071\/WF01022","article-title":"Estimating live fine fuels moisture content using meteorologically-based indices","volume":"10","author":"Viegas","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12314","DOI":"10.3390\/rs70912314","article-title":"A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series","volume":"7","author":"Helman","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.agrformet.2018.07.031","article-title":"How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems","volume":"262","author":"Ruffault","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0168-1923(02)00248-4","article-title":"Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain)","volume":"116","author":"Castro","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1071\/WF06081","article-title":"Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species","volume":"16","author":"Pellizzaro","year":"2007","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/0034-4257(95)00039-4","article-title":"Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data","volume":"52","author":"Gao","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/0143116042000274131","article-title":"Inter-comparison of AVHRR-based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy","volume":"26","author":"Lasaponara","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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":"Chuvieco","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","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 fuel moisture content in forest fire danger rating","volume":"92","author":"Chuvieco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/0143116042000273998","article-title":"Use of Normalized Difference Water Index for monitoring live fuel moisture","volume":"26","author":"Dennison","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.envsoft.2017.06.006","article-title":"Retrieval of forest fuel moisture content using a coupled radiative transfer model","volume":"95","author":"Quan","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.3390\/rs12142251","article-title":"Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations","volume":"12","author":"Marino","year":"2020","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Myoung, B., Kim, S.H., Nghiem, S.V., Jia, S., Whitney, K., and Kafatos, M.C. (2018). Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA. Remote Sens., 10.","DOI":"10.3390\/rs10010087"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1016\/j.rse.2008.07.012","article-title":"Mapping live fuel moisture with MODIS data: A multiple regression approach","volume":"112","author":"Peterson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"71","DOI":"10.4996\/fireecology.0803071","article-title":"Monitoring Live Fuel Moisture Using Soil Moisture and Remote Sensing Proxies","volume":"8","author":"Qi","year":"2012","journal-title":"Fire Ecol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jia, S., Kim, S.H., Nghiem, S.V., Cho, W., and Kafatos, M.C. (2018, January 22\u201327). Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519392"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s40725-020-00116-5","article-title":"Satellite Remote Sensing Contributions to Wildland Fire Science and Management","volume":"6","author":"Chuvieco","year":"2020","journal-title":"Curr. For. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shu, Q., Quan, X., Yebra, M., Liu, X., Wang, L., and Zhang, Y. (August, January 28). Evaluating the Sentinel-2a Satellite Data for Fuel Moisture Content Retrieval. Proceedings of the IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900104"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, M., Ria\u00f1o, D., Yebra, M., Salas, J., Cardil, A., Monedero, S., Ramirez, J., Mart\u00edn, M.P., Vilar, L., and Gajardo, J. (2020). A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models. Remote Sens., 12.","DOI":"10.3390\/rs12111714"},{"key":"ref_44","unstructured":"Costa, M. (1986). La Vegetaci\u00f3 al Pa\u00eds Valenci\u00e0, Universitat de Val\u00e8ncia."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.H., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation, Type III Final Report; NASA\/GSFC."},{"key":"ref_50","unstructured":"Pearson, R.L., and Miller, L.D. (1972, January 2\u20136). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado. Proceedings of the 8th International Symposium on Remote Sensing of the Environment II, Ann Arbor, MI, USA."},{"key":"ref_51","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_52","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":"Huntjr","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, L., and Qu, J.J. (2007). NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett., 34.","DOI":"10.1029\/2007GL031021"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.5194\/isprs-archives-XLII-3-1381-2018","article-title":"Comparison of vegetation indices from rpas and sentinel-2 imagery for detecting permanent pastures","volume":"XLII-3","author":"Piragnolo","year":"2018","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1046\/j.0269-8463.2001.00563.x","article-title":"A standardized protocol for the determination of specific leaf area and leaf dry matter content","volume":"15","author":"Garnier","year":"2001","journal-title":"Funct. Ecol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, Y., Luo, J., Huang, Q., Wu, W., and Sun, Y. (2019). Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set. Remote Sens., 11.","DOI":"10.3390\/rs11202342"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.envsoft.2018.08.003","article-title":"Estimating daily meteorological data and downscaling climate models over landscapes","volume":"108","author":"Turco","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_60","unstructured":"Milliken, G.A., and Johnson, D.E. (1992). Analysis of Messy Data, Chapman & Hall\/CRC."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1037\/a0027127","article-title":"Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)","volume":"17","author":"Vrieze","year":"2012","journal-title":"Psychol. Methods"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"495","DOI":"10.32614\/RJ-2016-062","article-title":"mctest: An R Package for Detection of Collinearity among Regressors","volume":"8","author":"Imdadullah","year":"2016","journal-title":"R J."},{"key":"ref_63","unstructured":"Viegas, D.X. (2018). Estimation of live fuel moisture content of shrubland using MODIS and Sentinel-2 images. Advances in Forest Fire Research 2018; Chapter 2\u2014Fuel Management; Proceedings of the VIII Inter-National Conference on Forest Fire Research, Coimbra, Portugal, 10\u201316 November 2018, Imprensa da Universidade de Coimbra."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.foreco.2016.04.005","article-title":"Seasonal variations in red pine (Pinus resinosa) and jack pine (Pinus banksiana) foliar physio-chemistry and their potential influence on stand-scale wildland fire behavior","volume":"373","author":"Jolly","year":"2016","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:19Z","timestamp":1760166079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,17]]},"references-count":64,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183726"],"URL":"https:\/\/doi.org\/10.3390\/rs13183726","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,17]]}}}