{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T18:23:18Z","timestamp":1777918998778,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T00:00:00Z","timestamp":1581984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC19M0103"],"award-info":[{"award-number":["80NSSC19M0103"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC19K1335"],"award-info":[{"award-number":["80NSSC19K1335"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global \u201cphenoregions\u201d using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions \u201cself-organized\u201d along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.<\/jats:p>","DOI":"10.3390\/rs12040671","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6518-4897","authenticated-orcid":false,"given":"Matthew","family":"Dannenberg","sequence":"first","affiliation":[{"name":"Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5212-6153","authenticated-orcid":false,"given":"Xian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3258-1033","authenticated-orcid":false,"given":"Dong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-6489","authenticated-orcid":false,"given":"William","family":"Smith","sequence":"additional","affiliation":[{"name":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1890\/070217","article-title":"Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century","volume":"7","author":"Morisette","year":"2009","journal-title":"Front. Ecol. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.09.012","article-title":"Climate change, phenology, and phenological control of vegetation feedbacks to the climate system","volume":"169","author":"Richardson","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.tree.2007.04.003","article-title":"Shifting plant phenology in response to global change","volume":"22","author":"Cleland","year":"2007","journal-title":"Trends Ecol. Evol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1111\/j.1365-2486.2005.00949.x","article-title":"Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects","volume":"11","author":"Henebry","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.rse.2003.11.006","article-title":"Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan","volume":"89","author":"Henebry","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1029\/2004GL021961","article-title":"A global framework for monitoring phenological responses to climate change","volume":"32","author":"White","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"044020","DOI":"10.1088\/1748-9326\/ab04d2","article-title":"Impacts of land cover and land use change on long-term trend of land surface phenology: A case study in agricultural ecosystems","volume":"14","author":"Zhang","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"054008","DOI":"10.1088\/1748-9326\/aa6ad9","article-title":"Impacts of wildfires on interannual trends in land surface phenology: An investigation of the Hayman Fire","volume":"12","author":"Wang","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2006.04.014","article-title":"Real-time monitoring and short-term forecasting of land surface phenology","volume":"104","author":"White","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"024011","DOI":"10.1088\/1748-9326\/8\/2\/024011","article-title":"Large-scale heterogeneity of Amazonian phenology revealed from 26-year long AVHRR\/NDVI time-series","volume":"8","author":"Silva","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1080\/01431161.2017.1286055","article-title":"The Dynamic-Time-Warping-based k-means++ clustering and its application in phenoregion delineation","volume":"38","author":"Zhang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111401","DOI":"10.1016\/j.rse.2019.111401","article-title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","volume":"233","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1038\/s41598-017-01260-y","article-title":"Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest","volume":"7","author":"Yang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.rse.2010.12.015","article-title":"Satellite passive microwave remote sensing for monitoring global land surface phenology","volume":"115","author":"Jones","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"096003","DOI":"10.1117\/1.JRS.9.096003","article-title":"Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation","volume":"9","author":"Li","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2012.03.025","article-title":"Satellite passive microwave detection of North America start of season","volume":"123","author":"Jones","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1111\/gcb.13464","article-title":"Mapping gains and losses in woody vegetation across global tropical drylands","volume":"23","author":"Tian","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alemu, W.G., and Henebry, G.M. (2017). Land surface phenology and seasonality using cool earthlight in croplands of Eastern Africa and the Linkages to crop production. Remote Sens., 9.","DOI":"10.3390\/rs9090914"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111307","DOI":"10.1016\/j.rse.2019.111307","article-title":"Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992\u20132012","volume":"232","author":"Tong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11640","DOI":"10.1073\/pnas.1900278116","article-title":"Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence","volume":"116","author":"Magney","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nichol, C.J., Drolet, G., Porcar-Castell, A., Wade, T., Sabater, N., Middleton, E.M., MacLellan, C., Levula, J., Mammarella, I., and Vesala, T. (2019). Diurnal and seasonal solar induced chlorophyll fluorescence and photosynthesis in a boreal scots pine canopy. Remote Sens., 11.","DOI":"10.3390\/rs11030273"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2016.06.014","article-title":"Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence","volume":"232","author":"Liu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2016.11.021","article-title":"Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests","volume":"190","author":"Jeong","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1002\/2017GL075922","article-title":"Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America","volume":"45","author":"Smith","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.1029\/2019GL082716","article-title":"Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar-Induced Chlorophyll Fluorescence","volume":"46","author":"Wang","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"37","author":"Kohonen","year":"2013","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6929","DOI":"10.3390\/rs6086929","article-title":"A non-stationary 1981\u20132012 AVHRR NDVI3g time series","volume":"6","author":"Pinzon","year":"2014","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"791","DOI":"10.5194\/essd-9-791-2017","article-title":"A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations","volume":"9","author":"Du","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.5194\/amt-6-2803-2013","article-title":"Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: Methodology, simulations, and application to GOME-2","volume":"6","author":"Joiner","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3939","DOI":"10.5194\/amt-9-3939-2016","article-title":"New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: Simulations and application to GOME-2 and SCIAMACHY","volume":"9","author":"Joiner","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1177\/0309133310397582","article-title":"The self-organizing map in synoptic climatological research","volume":"35","author":"Sheridan","year":"2011","journal-title":"Prog. Phys. Geogr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3354\/cr022013","article-title":"Self-organizing maps: Applications to synoptic climatology","volume":"22","author":"Hewitson","year":"2002","journal-title":"Clim. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"D02104","DOI":"10.1029\/2006JD007460","article-title":"North Atlantic climate variability from a self-organizing map perspective","volume":"112","author":"Reusch","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4912","DOI":"10.1038\/ncomms5912","article-title":"Persistence of pressure patterns over North America and the North Pacific since AD 1500","volume":"5","author":"Wise","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1175\/JCLI-D-12-00649.1","article-title":"How Many ENSO Flavors Can We Distinguish?","volume":"26","author":"Johnson","year":"2013","journal-title":"J. Clim."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1002\/joc.4950","article-title":"Reanalysing the impacts of atmospheric teleconnections on cold-season weather using multivariate surface weather types and self-organizing maps","volume":"37","author":"Lee","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1109\/TGRS.2012.2223218","article-title":"Exploring spatiotemporal phenological patterns and trajectories using self-organizing maps","volume":"51","author":"Hamm","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","unstructured":"Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J. (2000, January 18). Self-organizing map in Matlab: The SOM toolbox. Proceedings of the Matlab DSP Conference, Helsinki, Finland."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/joc.3711","article-title":"Updated high-resolution grids of monthly climatic observations\u2014The CRU TS3.10 Dataset","volume":"34","author":"Harris","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ghil, M., Allen, M.R., Dettinger, M.D., Ide, K., Kondrashov, D., Mann, M.E., Robertson, A.W., Saunders, A., Tian, Y., and Varadi, F. (2002). Advanced spectral methods for climatic time series. Rev. Geophys., 40.","DOI":"10.1029\/2000RG000092"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(00)00175-9","article-title":"Land-surface phenologies from AVHRR using the discrete fourier transform","volume":"75","author":"Moody","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"E1327","DOI":"10.1073\/pnas.1320008111","article-title":"Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence","volume":"111","author":"Guanter","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2014.06.022","article-title":"The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange","volume":"152","author":"Joiner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7184","DOI":"10.1029\/2018GL077906","article-title":"Solar-Induced Fluorescence Detects Interannual Variation in Gross Primary Production of Coniferous Forests in the Western United States","volume":"45","author":"Zuromski","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2014.11.026","article-title":"Empirical evidence of El Ni\u00f1o-Southern Oscillation influence on land surface phenology and productivity in the western United States","volume":"159","author":"Dannenberg","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4896","DOI":"10.1111\/gcb.13748","article-title":"Shifting Pacific storm tracks as stressors to ecosystems of western North America","volume":"23","author":"Dannenberg","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"034029","DOI":"10.1088\/1748-9326\/aaa85a","article-title":"Atmospheric teleconnection influence on North American land surface phenology","volume":"13","author":"Dannenberg","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.rse.2010.04.005","article-title":"Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product","volume":"114","author":"Ganguly","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1029\/97GB00330","article-title":"A continental phenology model for monitoring vegetation responses to interannual climatic variability","volume":"11","author":"White","year":"1997","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2018.12.029","article-title":"Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data","volume":"223","author":"Yan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","first-page":"1","article-title":"ISS observations offer insights into plant function","volume":"1","author":"Stavros","year":"2017","journal-title":"Nat. Ecol. Evol."},{"key":"ref_54","first-page":"10456","article-title":"Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: First results and intersensor comparison to OCO-2","volume":"45","author":"Frankenberg","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global land change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s41586-018-0555-7","article-title":"Widespread seasonal compensation effects of spring warming on northern plant productivity","volume":"562","author":"Buermann","year":"2018","journal-title":"Nature"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"eaax1396","DOI":"10.1126\/sciadv.aax1396","article-title":"Increased atmospheric vapor pressure deficit reduces global vegetation growth","volume":"5","author":"Yuan","year":"2019","journal-title":"Sci. Adv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1029\/2019JG005289","article-title":"Towards a harmonized long-term spaceborne record of far-red solar-induced fluorescence","volume":"124","author":"Parazoo","year":"2019","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5779","DOI":"10.5194\/bg-15-5779-2018","article-title":"A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks","volume":"15","author":"Zhang","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Smith, W.K., Fox, A.M., MacBean, N., Moore, D.J.P., and Parazoo, N.C. (2019). Constraining estimates of terrestrial carbon uptake: New opportunities using long-term satellite observations and data assimilation. New Phytol.","DOI":"10.1111\/nph.16055"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Barnes, M.L., Breshears, D.D., Law, D.J., van Leeuwen, W.J.D., Monson, R.K., Fojtik, A.C., Barron-Gafford, G.A., and Moore, D.J.P. (2017). Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0189539"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2017.07.037","article-title":"Uncertainty in plant functional type distributions and its impact on land surface models","volume":"203","author":"Hartley","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"Dannenberg, M.P., Wang, X., Yan, D., and Smith, W.K. (2019). Global 0.5 degree phenoregions from satellite NDVI, solar-induced fluorescence, and vegetation optical depth. Mendeley Data."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/671\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:58:46Z","timestamp":1760173126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,18]]},"references-count":64,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12040671"],"URL":"https:\/\/doi.org\/10.3390\/rs12040671","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,18]]}}}