{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:40:51Z","timestamp":1781901651471,"version":"3.54.5"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0\u20135 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016\u20132020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and <jats:italic>in situ<\/jats:italic> weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.<\/jats:p>","DOI":"10.3389\/fdata.2023.1243559","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:03:02Z","timestamp":1700218982000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations"],"prefix":"10.3389","volume":"6","author":[{"given":"Kellen","family":"Donahue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John S.","family":"Kimball","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyang","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrick","family":"Bunt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Colliander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahta","family":"Moghaddam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesse","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngwook","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Rawlins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"4085","DOI":"10.1109\/TGRS.2012.2229466","article-title":"Feasibility of characterizing snowpack and the freeze-thaw state of underlying soil using multifrequency active\/passive microwave data","volume":"51","author":"Bateni","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B2","article-title":"\u201cGlobal learning and observations to benefit the environment (globe),\u201d","volume-title":"Proceedings of the Malian Symposium of Applied Sciences","author":"Boger","year":"2002"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e0177678","DOI":"10.1371\/journal.pone.0177678","article-title":"Optimal classifier for imbalanced data using matthews correlation coefficient metric","volume":"12","author":"Boughorbel","year":"2017","journal-title":"PloS ONE"},{"key":"B4","volume-title":"SMAP Twice-Daily rSIR-Enhanced EASE-Grid 2, 0. Brightness Temperatures.","author":"Brodzik","year":"2020"},{"key":"B5","volume-title":"SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 3","author":"Chaubell","year":"2020"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.4306","article-title":"TransUNet: transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021","journal-title":"arXiv:2102.04306"},{"key":"B7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.rse.2018.10.010","article-title":"Detecting soil freeze\/thaw onsets in Alaska using SMAP and ASCAT data","volume":"220","author":"Chen","year":"2019","journal-title":"Remote Sensing Environ."},{"key":"B8","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1109\/TGRS.2011.2174368","article-title":"Application of QuikSCAT backscatter to SMAP validation planning: freeze\/thaw state over ALECTRA sites in Alaska from 2000 to 2007","volume":"50","author":"Colliander","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B9","doi-asserted-by":"crossref","DOI":"10.3133\/ofr20111073","volume-title":"Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010)","author":"Danielson","year":"2011"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/rs12010002","article-title":"Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada","volume":"12","author":"DeLancey","year":"2019","journal-title":"Remote Sens."},{"key":"B11","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.rse.2017.03.007","article-title":"Retrieving landscape freeze\/thaw state from soil moisture active passive (SMAP) radar and radiometer measurements","volume":"194","author":"Derksen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"B12","doi-asserted-by":"publisher","first-page":"5749","DOI":"10.5194\/hess-25-5749-2021","article-title":"The international soil moisture network: serving earth system science for over a decade","volume":"25","author":"Dorigo","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929v2"},{"key":"B14","doi-asserted-by":"publisher","first-page":"4871","DOI":"10.1109\/JSTARS.2023.3278686","article-title":"Assessment of surface fractional water impacts on SMAP soil moisture retrieval","volume":"16","author":"Du","year":"2023","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens"},{"key":"B15","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.rse.2016.07.029","article-title":"Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS","volume":"184","author":"Du","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.3390\/rs11161952","article-title":"Remote sensing of environmental changes in cold regions: methods, achievements and challenges","volume":"11","author":"Du","year":"2019","journal-title":"Remote Sens."},{"key":"B17","doi-asserted-by":"publisher","first-page":"2659","DOI":"10.1175\/MWR-D-20-0342.1","article-title":"Late spring and summer subseasonal forecasts in the Northern Hemisphere midlatitudes: biases and skill in the ECMWF model","volume":"149","author":"Dutra","year":"2021","journal-title":"Monthly Weather Rev."},{"key":"B18","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neunet.2018.11.005","article-title":"A comparison of deep networks with ReLU activation function and linear spline-type methods","volume":"110","author":"Eckle","year":"2019","journal-title":"Neural Netw."},{"key":"B19","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The soil moisture active passive (SMAP) mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"B20","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1175\/JHM-D-14-0065.1","article-title":"Assimilation of freeze-thaw observations into the NASA catchment land surface model","volume":"16","author":"Farhadi","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.3390\/rs10111697","article-title":"An improved algorithm for discriminating soil freezing and thawing using AMSR-E and AMSR2 soil moisture products","volume":"10","author":"Gao","year":"2018","journal-title":"Remote Sens."},{"key":"B23","unstructured":"Global Learning and Observations to Benefit the Environment (GLOBE) Program2021"},{"key":"B24","year":"2015"},{"key":"B25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.agrformet.2018.01.010","article-title":"Vegetation can strongly regulate permafrost degradation and its southern edge through changing surface freeze-thaw processes","volume":"252","author":"Guo","year":"2018","journal-title":"Agric. Forest Meteorol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. Royal Meteorol. Soc."},{"key":"B27","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s11075-020-01042-0","article-title":"An algorithm for best rational approximation based on barycentric rational interpolation","volume":"88","author":"Hofreither","year":"2021","journal-title":"Num. Alg."},{"key":"B28","doi-asserted-by":"publisher","first-page":"6993","DOI":"10.1080\/01431161.2019.1597307","article-title":"A continuous global record of near-surface soil freeze\/thaw status from AMSR-E and AMSR2 data","volume":"40","author":"Hu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"B29","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1016\/j.rse.2009.08.003","article-title":"A decision tree algorithm for surface soil freeze\/thaw classification over China using SSM\/I brightness temperature","volume":"113","author":"Jin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"B30","doi-asserted-by":"publisher","first-page":"3099292","DOI":"10.1109\/TGRS.2021.3099292","article-title":"Informing improvements in freeze\/thaw state classification using subpixel temperature","volume":"60","author":"Johnston","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B31","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S0921-8181(01)00095-9","article-title":"Non-conductive heat transfer associated with frozen soils","volume":"29","author":"Kane","year":"2001","journal-title":"Global Planet. Change"},{"key":"B32","doi-asserted-by":"publisher","first-page":"133","DOI":"10.5194\/essd-9-133-2017","article-title":"An extended global earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing","volume":"9","author":"Kim","year":"2017","journal-title":"Earth Syst. Scie. Data"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.3390\/rs11111317","article-title":"Global assessment of the SMAP freeze\/thaw data record and regional applications for detecting spring onset and frost events","volume":"11","author":"Kim","year":"2019","journal-title":"Remote Sens."},{"key":"B34","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1750-0680-2-2","article-title":"Influence of freeze-thaw events on carbon dioxide emission from soils at different moisture and land use","volume":"2","author":"Kurganova","year":"2007","journal-title":"Carb. Balance Manage."},{"key":"B35","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1080\/01431160050505900","article-title":"Land surface air temperature mapping using TOVS and AVHRR","volume":"22","author":"Lakshmi","year":"2001","journal-title":"Int. J. Rem. Sens."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15481603.2022.2122117","article-title":"A machine-learning based dynamic ensemble selection algorithm for microwave retrieval of surface soil freeze\/thaw: a case study across China","volume":"59","author":"Li","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1002\/joc.6867","article-title":"How well do the ERA-Interim, ERA-5, GLDAS-2.1 and NCEP-R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau?","volume":"42","author":"Liu","year":"2020","journal-title":"Int. J. Climatol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1073765","DOI":"10.3389\/frsen.2023.1073765","article-title":"Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products","volume":"4","author":"Long","year":"2023","journal-title":"Front. Remote Sens"},{"key":"B39","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2015.03.112","article-title":"Regression and classification using extreme learning machine based on L1-norm and L2-norm","volume":"174","author":"Luo","year":"2016","journal-title":"Neurocomputing"},{"key":"B40","doi-asserted-by":"publisher","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":"2016","journal-title":"IEEE TGRS"},{"key":"B41","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.5194\/bg-20-2941-2023","article-title":"Reviews and syntheses: recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic\u2013boreal regions","volume":"20","author":"Mavrovic","year":"2023","journal-title":"Biogeosciences"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/0470848944.hsa059a","article-title":"Estimation of surface freeze-thaw states using microwave sensors","volume":"53","author":"McDonald","year":"2006","journal-title":"Encycl. Hydrol. Sci."},{"key":"B43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.rse.2015.12.025","article-title":"ESA's soil moisture and ocean salinity mission: from science to operational applications","volume":"180","author":"Mecklenburg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"B44","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1002\/2017JD027199","article-title":"Introduction to CAUSES: description of weather and climate models and their near-surface temperature errors in 5-day hindcasts near the Southern Great Plains","volume":"123","author":"Morcrette","year":"2018","journal-title":"JGR Atmosph."},{"key":"B45","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-land: a state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Mu\u00f1oz-Sabater","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6614","article-title":"In search of the real inductive bias: On the role of implicit regularization in deep learning","author":"Neyshabur","year":"2014","journal-title":"arXiv:1412.6614v4"},{"key":"B47","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.agrformet.2017.10.009","article-title":"The AmeriFlux network: a coalition of the willing","volume":"249","author":"Novick","year":"2018","journal-title":"Agric. Forest Meteorol."},{"key":"B48","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1111\/gcb.14283","article-title":"Spring photosynthetic onset and net CO2 uptake in Alaska triggered by landscape thawing","volume":"24","author":"Parazoo","year":"2018","journal-title":"Global Change Biol."},{"key":"B49","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1029\/2019WR026020","article-title":"In situ estimates of freezing\/melting point depression in agricultural soils using permittivity and temperature measurements","volume":"56","author":"Pardo","year":"2020","journal-title":"Water Resour. Res."},{"key":"B50","doi-asserted-by":"publisher","first-page":"6818","DOI":"10.1109\/TGRS.2014.2303635","article-title":"Multisensor microwave sensitivity to freeze\/thaw dynamics across a complex boreal landscape","volume":"52","author":"Podest","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B51","doi-asserted-by":"publisher","first-page":"2055","DOI":"10.5194\/essd-10-2055-2018","article-title":"Northern Hemisphere surface freeze-thaw product from Aquarius L-band radiometers","volume":"10","author":"Prince","year":"2018","journal-title":"Earth Syst. Sci. Data"},{"key":"B52","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.rse.2016.01.012","article-title":"SMOS prototype algorithm for detecting autumn soil freezing","volume":"180","author":"Rautiainen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"B53","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"Ronneberger","year":"2015"},{"key":"B54","doi-asserted-by":"publisher","first-page":"3179","DOI":"10.1029\/96GL03076","article-title":"Regional trends of surface and tropospheric temperature and evening-morning temperature difference in northern latitudes: 1973-93","volume":"23","author":"Ross","year":"1996","journal-title":"Geophys. Res. Lett"},{"key":"B55","doi-asserted-by":"publisher","first-page":"111542","DOI":"10.1016\/j.rse.2019.111542","article-title":"L-band response to freeze\/thaw in a boreal forest stand from ground- and tower-based radiometer observations","volume":"237","author":"Roy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"B56","doi-asserted-by":"publisher","first-page":"4","DOI":"10.30534\/ijatcse\/2020\/175942020","article-title":"Binary cross entropy with deep learning technique for image classification","volume":"9","author":"Ruby","year":"2020","journal-title":"Int. J. Adv. Trends Comp. Sci. Eng."},{"key":"B57","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"B58","first-page":"7262","article-title":"\u201cSegmenter: transformer for semantic segmentation,\u201d","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision.","author":"Strudel","year":"2021"},{"key":"B59","first-page":"648","article-title":"\u201cEfficient object localization using convolutional networks,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Tompson","year":"2015"},{"key":"B60","volume-title":"National Water and Climate Center Interactive Map","year":"2017"},{"key":"B61","doi-asserted-by":"publisher","first-page":"4411211","DOI":"10.1109\/TGRS.2022.3174807","article-title":"Satellite retrievals of probabilistic freeze-thaw conditions from SMAP and AMSR brightness temperatures","volume":"60","author":"Walker","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B62","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.jhydrol.2009.06.046","article-title":"The influence of freeze-thaw cycles of active soil layer on surface runoff in a permafrost watershed","volume":"375","author":"Wang","year":"2009","journal-title":"J. Hydrol."},{"key":"B63","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/w13081010","article-title":"The influence mechanism of freeze-thaw on soil erosion: a review","volume":"13","author":"Zhang","year":"2021","journal-title":"Water"},{"key":"B64","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1080\/789610186","article-title":"Application of satellite remote sensing techniques to frozen ground studies","volume":"28","author":"Zhang","year":"2004","journal-title":"Polar Geograph."},{"key":"B65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10889370802175895","article-title":"Statistics and characteristics of permafrost and ground-ice distribution in the Northern Hemisphere","volume":"31","author":"Zhang","year":"2008","journal-title":"Polar Geography"},{"key":"B66","doi-asserted-by":"publisher","first-page":"044012","DOI":"10.1088\/1748-9326\/aab1e7","article-title":"Impacts of snow on soil temperature observed across the circumpolar north","volume":"13","author":"Zhang","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"B67","doi-asserted-by":"publisher","first-page":"127354","DOI":"10.1016\/j.jhydrol.2021.127354","article-title":"Freeze\/thaw onset detection combining SMAP and ASCAT data over Alaska: a machine learning approach","volume":"605","author":"Zhong","year":"2022","journal-title":"J. Hydrol."},{"key":"B68","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1109\/TGRS.2011.2169076","article-title":"Probabilistic fusion of Ku- and C-band scatterometer data for determining the freeze\/thaw state","volume":"50","author":"Zwieback","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2023.1243559\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:03:18Z","timestamp":1700218998000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2023.1243559\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":68,"alternative-id":["10.3389\/fdata.2023.1243559"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2023.1243559","relation":{},"ISSN":["2624-909X"],"issn-type":[{"value":"2624-909X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,17]]},"article-number":"1243559"}}