{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:43:58Z","timestamp":1773081838907,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004230","name":"Korea Polar Research Institute","doi-asserted-by":"publisher","award":["PE210400"],"award-info":[{"award-number":["PE210400"]}],"id":[{"id":"10.13039\/501100004230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1A6A1A03033167"],"award-info":[{"award-number":["2019R1A6A1A03033167"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002507","name":"Kangwon National University","doi-asserted-by":"publisher","award":["520200068"],"award-info":[{"award-number":["520200068"]}],"id":[{"id":"10.13039\/501100002507","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice\/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015\u20132017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.<\/jats:p>","DOI":"10.3390\/rs13122283","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T21:34:38Z","timestamp":1623360878000},"page":"2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0414-519X","authenticated-orcid":false,"given":"Hyangsun","family":"Han","sequence":"first","affiliation":[{"name":"Department of Geophysics, Kangwon National University, Chuncheon 24341, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0926-2755","authenticated-orcid":false,"given":"Sungjae","family":"Lee","sequence":"additional","affiliation":[{"name":"Center of Remote Sensing and GIS, Korea Polar Research Institute, Incheon 21990, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-9291","authenticated-orcid":false,"given":"Hyun-Cheol","family":"Kim","sequence":"additional","affiliation":[{"name":"Center of Remote Sensing and GIS, Korea Polar Research Institute, Incheon 21990, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9805-7261","authenticated-orcid":false,"given":"Miae","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/S0034-4257(96)00220-9","article-title":"Passive microwave algorithms for sea ice concentration: A comparison of two techniques","volume":"60","author":"Comiso","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"328","DOI":"10.3402\/tellusa.v56i4.14418","article-title":"Arctic climate change: Observed and modelled temperature and sea-ice variability","volume":"56","author":"Johannessen","year":"2004","journal-title":"Tellus A"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7823","DOI":"10.1175\/JCLI-D-18-0134.1","article-title":"Evolution of the global coupled climate response to Arctic sea ice loss during 1990\u20132090 and its contribution to climate change","volume":"31","author":"Sun","year":"2018","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3255","DOI":"10.1002\/2017GL076502","article-title":"Evaluating impacts of recent Arctic sea ice loss on the northern hemisphere winter climate change","volume":"45","author":"Ogawa","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1126\/science.286.5446.1934","article-title":"Global warming and northern hemisphere sea ice extent","volume":"286","author":"Vinnikov","year":"1999","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"L15708","DOI":"10.1029\/2011GL048008","article-title":"Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world","volume":"38","author":"Kay","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1007\/s11069-020-04064-y","article-title":"Dramatic decline of Arctic sea ice linked to global warming","volume":"103","author":"Yadav","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_8","unstructured":"P\u00f6rtner, H.O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Nicolai, M., Okem, A., and Petzold, J. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC Intergovernmental Panel on Climate Change (IPCC). in press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"L19603","DOI":"10.1029\/2008GL035028","article-title":"Impact of a shrinking Arctic ice cover on marine primary production","volume":"35","author":"Arrigo","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s12526-010-0061-0","article-title":"Impacts of changing sea-ice conditions on Arctic marine mammals","volume":"41","author":"Kovacs","year":"2011","journal-title":"Mar. Biodivers."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16868","DOI":"10.1038\/srep16868","article-title":"Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route","volume":"5","author":"Inoue","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1038\/ngeo467","article-title":"September sea-ice cover in the Arctic Ocean projected to vanish by 2100","volume":"2","author":"Hall","year":"2009","journal-title":"Nat. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1002\/grl.50316","article-title":"When will the summer Arctic be nearly sea ice free?","volume":"40","author":"Overland","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1126\/science.aag2345","article-title":"Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission","volume":"354","author":"Notz","year":"2016","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1038\/nature18307","article-title":"Paris Agreement climate proposals need a boost to keep warming well below 2 \u00b0C","volume":"534","author":"Rogelj","year":"2016","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/TGRS.2014.2310136","article-title":"Retrieval of Arctic sea ice parameters by satellite passive microwave sensors: A comparison of eleven sea ice concentration algorithms","volume":"52","author":"Ivanova","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1109\/TGRS.2008.917213","article-title":"Special Sensor Microwave Imager Sounder (SSMIS) radiometric calibration anomalies\u2014Part I: Identification and characterization","volume":"46","author":"Kunkee","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/JPROC.2009.2036869","article-title":"Global Change Observation Mission (GCOM) for monitoring carbon, water cycles, and climate change","volume":"98","author":"Imaoka","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1109\/TGRS.2015.2402204","article-title":"Intercalibration of Advanced Microwave Scanning Radiometer-2 (AMSR2) brightness temperature","volume":"53","author":"Okuyama","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5355","DOI":"10.1029\/JD089iD04p05355","article-title":"Determination of sea ice parameters with the Nimbus 7 SMMR","volume":"89","author":"Cavalieri","year":"1984","journal-title":"J. Geophys. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1029\/JC091iC01p00975","article-title":"Characteristics of Arctic winter sea ice from satellite multispectral microwave observations","volume":"91","author":"Comiso","year":"1986","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"C02S03","DOI":"10.1029\/2005JC003384","article-title":"Sea ice remote sensing using AMSR-E 89-GHz channels","volume":"113","author":"Spreen","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.5194\/tc-9-1797-2015","article-title":"Inter-comparison and evaluation of sea ice algorithms: Towards further identification of challenges and optimal approach using passive microwave observations","volume":"9","author":"Ivanova","year":"2015","journal-title":"Cryosphere"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.5194\/tc-10-2275-2016","article-title":"The EUMETSAT sea ice concentration climate data record","volume":"10","author":"Tonboe","year":"2016","journal-title":"Cryosphere"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2006.05.013","article-title":"Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms","volume":"104","author":"Andersen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2018.02.058","article-title":"Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data","volume":"209","author":"Han","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1080\/01431160801978999","article-title":"Effects of atmospheric water and surface wind on passive microwave retrievals of sea ice concentration: A simulation study","volume":"29","author":"Shin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/TGRS.2005.846151","article-title":"Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in Arctic peripheral seas","volume":"43","author":"Meier","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Meier, W., and Notz, D. (2010). A note on the accuracy and reliability of satellite-derived passive microwave estimates of sea-ice extent. Clic Arctic Sea Ice Working Group Consensus Document, World Climate Research Program."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"455","DOI":"10.3189\/S0022143000034791","article-title":"Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM\/I","volume":"41","author":"Cavalieri","year":"1995","journal-title":"J. Glaciol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2016.2543660","article-title":"Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1109\/TGRS.2017.2655567","article-title":"Baltic sea ice concentration estimation using SENTINEL-1 SAR and AMSR2 microwave radiometer data","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111204","DOI":"10.1016\/j.rse.2019.05.023","article-title":"Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data","volume":"231","author":"Chi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e2020JC016277","DOI":"10.1029\/2020JC016277","article-title":"Assessment of high-resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application","volume":"125","author":"Fritzner","year":"2020","journal-title":"J. Geophys. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.5194\/tc-14-1083-2020","article-title":"Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks","volume":"14","author":"Kim","year":"2020","journal-title":"Cryosphere"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2017.1285501","article-title":"A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer","volume":"8","author":"Han","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"L01602","DOI":"10.1029\/2009GL041621","article-title":"The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat","volume":"37","author":"Woodgate","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1002\/2013GL058951","article-title":"Changes in Arctic melt season and implications for sea ice loss","volume":"41","author":"Stroeve","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"428","DOI":"10.3189\/172756407782871170","article-title":"Whither Arctic sea ice? A clear signal of decline regionally, seasonally and extending beyond the satellite record","volume":"46","author":"Meier","year":"2007","journal-title":"Ann. Glaciol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TGRS.2015.2465170","article-title":"GCOM-W1 AMSR2 level 1R product: Dataset of brightness temperature modified using the antenna pattern matching technique","volume":"54","author":"Maeda","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"1","article-title":"Operational Global Reanalysis: Progress, Future Directions and Synergies with NWP","volume":"27","author":"Hersbach","year":"2018","journal-title":"ECMWF Re-Anal. Proj. Rep. Ser."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7308","DOI":"10.1002\/2016JC011977","article-title":"Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea","volume":"121","author":"Serreze","year":"2016","journal-title":"J. Geophys. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/36.992795","article-title":"Evaluation of late summer passive microwave Arctic sea ice retrievals","volume":"40","author":"Markus","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Han, H., Im, J., Kim, M., Sim, S., Kim, J., Kim, D.-J., and Kang, S.-H. (2016). Retrieval of melt ponds on arctic multiyear sea ice in summer from terrasar-x dual-polarization data using machine learning approaches: A case study in the Chukchi Sea with mid-incidence angle data. Remote Sens., 8.","DOI":"10.3390\/rs8010057"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/15481603.2015.1026050","article-title":"Landfast sea ice monitoring using multisensor fusion in the Antarctic","volume":"52","author":"Kim","year":"2015","journal-title":"GIScience Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2017.2743339","article-title":"Sea ice classification using Cryosat-2 altimeter data by optimal classifier\u2013feature assembly","volume":"14","author":"Shen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111782","DOI":"10.1016\/j.rse.2020.111782","article-title":"Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data","volume":"242","author":"Kim","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lee, S., Im, J., Kim, J., Kim, M., Shin, M., Kim, H.-C., and Quackenbush, L.J. (2016). Arctic sea ice thickness estimation from CryoSat-2 satellite data using machine learning-based lead detection. Remote Sens., 8.","DOI":"10.3390\/rs8090698"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1017\/aog.2018.6","article-title":"Method for detection of leads from Sentinel-1 SAR images","volume":"59","author":"Murashkin","year":"2018","journal-title":"Ann. Glaciol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TGRS.2006.878445","article-title":"Assessment of EOS Aqua AMSR-E Arctic sea ice concentrations using Landsat-7 and airborne microwave imagery","volume":"44","author":"Cavalieri","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1109\/TGRS.2010.2046495","article-title":"Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS","volume":"48","author":"Cavalieri","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","unstructured":"Liang, S., Strahler, A., and Walthall, C. (1998, January 6\u201310). Retrieval of land surface albedo from satellite observations: A simulation study. Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium IGARSS \u201898, Seattle, WA, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1175\/JCLI3489.1","article-title":"Surface albedo of the Antarctic sea ice zone","volume":"18","author":"Brandt","year":"2005","journal-title":"J. Clim."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Radhakrishnan, R., Scott, A., and Clausi, D.A. (2021). Sea ice concentration estimation: Using passive microwave and SAR data with a U-net and curriculum learning. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.","DOI":"10.1109\/JSTARS.2021.3076109"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.5194\/tc-10-2217-2016","article-title":"The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations","volume":"10","author":"Kern","year":"2016","journal-title":"Cryosphere"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.1109\/JSTARS.2017.2719624","article-title":"Intercalibration of AMSR2 NASA Team 2 algorithm sea ice concentrations with AMSR-E slow rotation data","volume":"10","author":"Meier","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.5194\/tc-13-1661-2019","article-title":"Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: Effects on sea ice thermodynamics and evolution","volume":"13","author":"Wang","year":"2019","journal-title":"Cryosphere"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Di Napoli, C., Barnard, C., Prudhomme, C., Cloke, H.L., and Pappenberger, F. (2020). ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis. Geosci. Data J., 1\u20139.","DOI":"10.1002\/gdj3.102"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:03Z","timestamp":1760163183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":61,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122283"],"URL":"https:\/\/doi.org\/10.3390\/rs13122283","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]}}}