{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:26:59Z","timestamp":1774538819873,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models\u2019 performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from \u22120.3% to \u22127.3%. Moreover, in all of the cases examined, apart from one (Sartis\u2019 fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach.<\/jats:p>","DOI":"10.3390\/rs13214224","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"4224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece"],"prefix":"10.3390","volume":"13","author":[{"given":"Eleni","family":"Dragozi","sequence":"first","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1138-3724","authenticated-orcid":false,"given":"Theodore M.","family":"Giannaros","sequence":"additional","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1248-5490","authenticated-orcid":false,"given":"Vasiliki","family":"Kotroni","sequence":"additional","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece"}]},{"given":"Konstantinos","family":"Lagouvardos","sequence":"additional","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece"}]},{"given":"Ioannis","family":"Koletsis","sequence":"additional","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","unstructured":"Vallejo Calzada, V.R., Faivre, N., Cardoso Castro Rego, F.M., Moreno Rodr\u00edguez, J.M., and Xanthopoulos, G. (2021, June 03). Forest Fires. Sparking Firesmart Policies in the EU. Available online: https:\/\/ec.europa.eu\/info\/sites\/default\/files\/181116_booklet-forest-fire-hd.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1002\/2016GL068614","article-title":"Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia","volume":"43","author":"Nolan","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2011160118","DOI":"10.1073\/pnas.2011160118","article-title":"African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data","volume":"118","author":"Ramo","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Fan, C., and He, B. (2021). A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation. Forests, 12.","DOI":"10.3390\/f12070933"},{"key":"ref_6","unstructured":"San-Miguel-Ayanz, J., Costa, H., de Rigo, D., Libert\u00e1, G., Vivancos, T.A., Durrant, T., Nuijten, D., Loffler, P., and Moore, P. (2018). Basic criteria to assess wildfire risk at the pan-European level. JRC Technical Reports, Publications Office of the European Union. EUR 29500 EN."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1071\/WF01032","article-title":"Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns","volume":"10","author":"Morgan","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.rse.2004.03.017","article-title":"Estimating live fuel moisture content from remotely sensed reflectance","volume":"92","author":"Danson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nolan, R.H., Blackman, C.J., de Dios, V.R., Choat, B., Medlyn, B.E., Li, X., Bradstock, R.A., and Boer, M.M. (2020). Linking forest flammability and plant vulnerability to drought. Forests, 11.","DOI":"10.3390\/f11070779"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1002\/2017EF000657","article-title":"Changing weather extremes call for early warning of potential for catastrophic fire","volume":"5","author":"Boer","year":"2017","journal-title":"Earths Future"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1111\/gcb.15111","article-title":"A broader perspective on the causes and consequences of eastern Australia\u2019s 2019-2020 season of mega-fires: A response to Adams et al","volume":"26","author":"Bradstock","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Arga\u00f1araz, J.P., Landi, M.A., Scavuzzo, C.M., and Bellis, L.M. (2018). Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0204889"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Wang, L., Quan, X., He, B., Yebra, M., Xing, M., and Liu, X. (2019). Assessment of the dual polarimetric sentinel-1A data for forest fuel moisture content estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131568"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1071\/WF05063","article-title":"A process-based model of fine fuel moisture","volume":"15","author":"Matthews","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1071\/WF9910215","article-title":"A review of fine fuel moisture modelling","volume":"1","author":"Viney","year":"1991","journal-title":"Int. J. Wildland Fire"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1760","DOI":"10.1002\/eco.1760","article-title":"Responses of dead forest fuel moisture to climate change","volume":"10","author":"Liu","year":"2017","journal-title":"Ecohydrology"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Marino, E., Yebra, M., Guill\u00e9n-Climent, M., Algeet, N., Tom\u00e9, J.L., Madrigal, J., Guijarro, M., and Hernando, C. (2020). Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations. Remote Sens., 12.","DOI":"10.3390\/rs12142251"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Camia, A., Leblon, B., Cruz, M., Carlson, J., and Aguado, I. (2003). Methods used to estimate moisture content of dead wildland fuels. Wildland Fire Danger Estimation and Mapping: The Role of Remote Sensing Data, World Scientific.","DOI":"10.1142\/9789812791177_0004"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.11.020","article-title":"Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region","volume":"205","author":"Fan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1071\/WF15104","article-title":"Concurrent and antecedent soil moisture relate positively or negatively to probability of large wildfires depending on season","volume":"25","author":"Krueger","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1071\/WF14145","article-title":"Effects of curing on grassfires: I. Fuel dynamics in a senescing grassland","volume":"24","author":"Kidnie","year":"2015","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00484-002-0151-1","article-title":"Predicting live herbaceous moisture content from a seasonal drought index","volume":"47","author":"Dimitrakopoulos","year":"2003","journal-title":"Int. J. Biometeorol."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1139\/x04-101","article-title":"Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment","volume":"34","author":"Chuvieco","year":"2004","journal-title":"Can. J. For. Res."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"100300","DOI":"10.1016\/j.wace.2020.100300","article-title":"Modulating influence of drought on the synergy between heatwaves and dead fine fuel moisture content of bushfire fuels in the Southeast Australian region","volume":"31","author":"Sharples","year":"2021","journal-title":"Weather Clim. Extrem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1071\/WF08123","article-title":"Flammability descriptors of fine dead fuels resulting from two mechanical treatments in shrubland: A comparative laboratory study","volume":"19","author":"Marino","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Burton, J., Cawson, J., Noske, P., and Sheridan, G. (2019). Shifting states, altered fates: Divergent fuel moisture responses after high frequency wildfire in an obligate seeder eucalypt forest. Forests, 10.","DOI":"10.3390\/f10050436"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/00049158.2005.10674951","article-title":"Flammability of Australian forests","volume":"68","author":"Gill","year":"2005","journal-title":"Aust. For."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.agrformet.2015.01.002","article-title":"A semi-mechanistic model for predicting the moisture content of fine litter","volume":"203","author":"Fellows","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lee, H., Won, M., Yoon, S., and Jang, K. (2020). Estimation of 10-Hour Fuel Moisture Content Using Meteorological Data: A Model Inter-Comparison Study. Forests, 11.","DOI":"10.3390\/f11090982"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.agrformet.2017.04.014","article-title":"Hourly fine fuel moisture model for Pinus halepensis (Mill.) litter","volume":"243","author":"Jazbec","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108311","DOI":"10.1016\/j.agrformet.2020.108311","article-title":"Darker, cooler, wetter: Forest understories influence surface fuel moisture","volume":"300","author":"Pickering","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119379","DOI":"10.1016\/j.foreco.2021.119379","article-title":"Soil moisture influences on Sierra Nevada dead fuel moisture content and fire risks","volume":"496","author":"Rakhmatulina","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108602","DOI":"10.1016\/j.agrformet.2021.108602","article-title":"Stand conditions alter seasonal microclimate and dead fuel moisture in a Northwestern California oak woodland","volume":"308\u2013309","author":"Kane","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_41","unstructured":"Bovill, W., Hawthorne, S., Radic, J., Baillie, C., Ashton, A., Noske, P., Lane, P., and Sheridan, G. (December, January 29). Effectiveness of automated fuelsticks for predicting the moisture content of dead fuels in Eucalyptus forests. Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, Australia."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.agrformet.2018.11.038","article-title":"Fine dead fuel moisture shows complex lagged responses to environmental conditions in a saw palmetto (Serenoa repens) flatwoods","volume":"266","author":"Hiers","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1007\/s11676-020-01280-x","article-title":"Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain, Northeast China","volume":"32","author":"Masinda","year":"2021","journal-title":"J. For. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1071\/WF19061_CO","article-title":"Corrigendum to: Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide","volume":"29","author":"Cawson","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1071\/WF06136","article-title":"Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment","volume":"16","author":"Aguado","year":"2007","journal-title":"Int. J. Wildland Fire"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.rse.2015.12.010","article-title":"Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data","volume":"174","author":"Nolan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.agrformet.2010.02.007","article-title":"Dead fuel moisture estimation with MSG\u2013SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content","volume":"150","author":"Nieto","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2007.04.016","article-title":"Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data","volume":"112","author":"Hashimoto","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1071\/WF05060","article-title":"Time series of chaparral live fuel moisture maps derived from MODIS satellite data","volume":"15","author":"Stow","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_50","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_51","first-page":"17","article-title":"Dead fuel moisture content estimation using remote sensing","volume":"8","author":"Zormpas","year":"2017","journal-title":"Eur. J. Geogr."},{"key":"ref_52","unstructured":"Keetch, J.J., and Byram, G.M. (1968). A Drought Index for Forest Fire Control."},{"key":"ref_53","first-page":"37","article-title":"Development and Structure of the Canadian Forest Fireweather Index System","volume":"35","author":"Forest","year":"1987","journal-title":"Can. For. Serv. Forestry Tech. Rep."},{"key":"ref_54","unstructured":"McArthur, A.G. (1966). Weather and Grassland Fire Behaviour."},{"key":"ref_55","unstructured":"McArthur, A.G. (1967). Fire Behaviour in Eucalypt Forests, Commonwealth of Austalia Forest and Timber Bureau. Available online: https:\/\/vgls.sdp.sirsidynix.net.au\/client\/search\/asset\/1299701\/0."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.envsoft.2008.10.012","article-title":"A simple index for assessing fuel moisture content","volume":"24","author":"Sharples","year":"2009","journal-title":"Environ. Model. Softw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1175\/BAMS-D-18-0231.1","article-title":"Meteorological conditions conducive to the rapid spread of the deadly wildfire in eastern Attica, Greece","volume":"100","author":"Lagouvardos","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lu, L., Zhang, T., Wang, T., and Zhou, X. (2018). Evaluation of collection-6 MODIS land surface temperature product using multi-year ground measurements in an arid area of Northwest China. Remote Sens., 10.","DOI":"10.3390\/rs10111852"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/gdj3.44","article-title":"The automatic weather stations NOANN network of the National Observatory of Athens: Operation and database","volume":"4","author":"Lagouvardos","year":"2017","journal-title":"Geosci. Data J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ejrh.2018.02.002","article-title":"Spatial interpolation of climate variables in Northern Germany\u2014Influence of temporal resolution and network density","volume":"15","author":"Berndt","year":"2018","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1139\/x26-211","article-title":"Synoptic climatology of lightning-caused forest fires in subalpine and boreal forests","volume":"26","author":"Nash","year":"1996","journal-title":"Can. J. For. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0034-4257(02)00093-7","article-title":"Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data","volume":"83","author":"Wan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1139\/x06-207","article-title":"Testing a process-based fine fuel moisture model in two forest types","volume":"37","author":"Matthews","year":"2006","journal-title":"Can. J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4224\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:20:11Z","timestamp":1760167211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,21]]},"references-count":63,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214224"],"URL":"https:\/\/doi.org\/10.3390\/rs13214224","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,21]]}}}