{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:31:08Z","timestamp":1772253068866,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002830","name":"Centre National d'\u00c9tudes Spatiales","doi-asserted-by":"publisher","award":["TOSCA project TRISHNA"],"award-info":[{"award-number":["TOSCA project TRISHNA"]}],"id":[{"id":"10.13039\/501100002830","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005774","name":"University of Barcelona","doi-asserted-by":"publisher","award":["2017-SGR-1102"],"award-info":[{"award-number":["2017-SGR-1102"]}],"id":[{"id":"10.13039\/501100005774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating evapotranspiration at the field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Balance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the estimation of G with remote sensing (RS) data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G\/Rn. The G\/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface temperature (Ts) and albedo (Type II empirical equations). In this article, we aim to evaluate the estimation of G with RS data. Here, we compared eight G\/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method was evaluated using a wide range of climate and land cover datasets, including data from Eddy-Covariance towers that extend along the mid-latitude areas that encompass the European and African continents. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv \u2264 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the accuracy in the estimation of G with Rn, Ts or Fv decreases in densely vegetated areas (Fv \u2265 0.50). There are no significant differences between the most accurate Type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation which combines the Leaf Area Index (LAI) and Ts (E7) estimates G best. In densely vegetated areas, an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. An RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.<\/jats:p>","DOI":"10.3390\/rs14081788","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"1788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8180-9095","authenticated-orcid":false,"given":"Josep","family":"Bonsoms","sequence":"first","affiliation":[{"name":"Department of Geography, Universitat de Barcelona, 08007 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3905-7560","authenticated-orcid":false,"given":"Gilles","family":"Boulet","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re (CESBIO), Universit\u00e9 de Toulouse, CNES, CNRS, INRAE, IRD, UT3, 31500 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"L08707","DOI":"10.1029\/2006GL025734","article-title":"Climate change hot-spots","volume":"33","author":"Giorgi","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5229","DOI":"10.5194\/acp-7-5229-2007","article-title":"Observed poleward expansion of the Hadley circulation since 1979","volume":"7","author":"Hu","year":"2007","journal-title":"Atmos. Chem. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.gloplacha.2010.06.006","article-title":"Changes in seasonal precipitation in the Iberian Peninsula during 1946\u20132005","volume":"74","author":"Brunetti","year":"2010","journal-title":"Glob. Planet Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"044001","DOI":"10.1088\/1748-9326\/9\/4\/044001","article-title":"Evidence of increasing drought severity caused by temperature rise in southern Europe","volume":"9","author":"Revuelto","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s10584-011-0122-9","article-title":"Global changes in extreme events: Regional and seasonal dimension","volume":"110","author":"Orlowsky","year":"2012","journal-title":"Clim. Chang."},{"key":"ref_6","first-page":"90","article-title":"Climate change projections for the Mediterranean region, Global Planet","volume":"63","author":"Giorgi","year":"2008","journal-title":"Change"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s00382-007-0340-z","article-title":"Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations","volume":"31","author":"Sheffield","year":"2008","journal-title":"Clim. Dynam."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"W07447","DOI":"10.1029\/2006WR005653","article-title":"Mountains of the world, water towers for humanity: Typology, mapping, and global significance","volume":"43","author":"Viviroli","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_9","unstructured":"Brocca, L., Tramblay, Y., and Molle, F. (2020). Evapotranspiration in the Mediterranean region. Water, Elsevier."},{"key":"ref_10","unstructured":"Kpemlie, E. (2009). Assimilation Variation Nelle De Donnees De T\u00e9l\u00e9d\u00e9tection Dans Des Mod\u00e8les De Fonctionnements Des Couverts V\u00e9g\u00e9taux et Du Paysage Agricole. [Ph.D. Thesis, Universit\u00e9 d\u2019Avignon et des Pays de Vaucluse]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hatfield, J.L., and Baker, J.M. (2005). Soil heat flux. Micrometeorology in Agricultural Systems, American Society of Agronomy.","DOI":"10.2134\/agronmonogr47"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s00704-005-0234-0","article-title":"Evaluation of six parameterization approaches for the ground heat flux","volume":"88","author":"Liebethal","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1016\/j.agrformet.2008.04.008","article-title":"Spatial variability in soil heat flux at three Inner Mongolia steppe ecosystems","volume":"148","author":"Shao","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00704-012-0705-z","article-title":"Hourly estimation of soil heat flux density at the soil surface with three models and two field methods","volume":"112","author":"Venegas","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s00704-016-1816-8","article-title":"Estimation of ground heat flux from soil temperature over a bare soil","volume":"129","author":"An","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6934","DOI":"10.1002\/2017JD027160","article-title":"A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods","volume":"122","author":"Gao","year":"2017","journal-title":"J. Geophys. Res. Atmosph."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.agrformet.2007.07.004","article-title":"Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements. I. A universal approach to calculate thermal inertia","volume":"147","author":"Murray","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.agrformet.2007.06.009","article-title":"Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements. II. Diurnal shape of soil heat flux","volume":"147","author":"Murray","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0168-1923(90)90033-3","article-title":"Estimation of the soil heat flux\/net radiation ratio from spectral data","volume":"49","author":"Kustas","year":"1990","journal-title":"Agric. For. Meteorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0022-1694(98)00254-6","article-title":"A remote sensing surface energy balance algorithm for land (SEBAL): 2 Validation","volume":"212\u2013213","author":"Bastiaanssen","year":"1998","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1175\/1520-0450(2003)042<0851:DCISHF>2.0.CO;2","article-title":"Diurnal variation in soil heat flux and net radiation","volume":"42","author":"Santanello","year":"2003","journal-title":"J. Appl. Meteor."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0168-1923(95)02328-3","article-title":"Partition of sensible heat fluxes into bare soil and the atmosphere","volume":"82","author":"Cellier","year":"1996","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/0034-4257(93)90052-Y","article-title":"Analytical treatment of the relationship between soil heat flux\/net radiation ratio and vegetation indices","volume":"46","author":"Kustas","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/0168-1923(87)90021-9","article-title":"Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evapotranspiration by an infrared-temperature based energy balance empirical equation","volume":"39","author":"Choudhury","year":"1987","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","first-page":"D10117","article-title":"A climatological study of evapotranspiration and moisture stress across the continental U.S. based on the thermal remote sensing: I. Model formulation","volume":"112","author":"Anderson","year":"2007","journal-title":"J. Geophys. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.jhydrol.2005.03.027","article-title":"simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX Campaigns","volume":"315","author":"Sobrino","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"453","DOI":"10.5194\/hess-15-453-2011","article-title":"Global land-surface evapotranspiration estimated from satellite-based observations","volume":"15","author":"Miralles","year":"2011","journal-title":"Hydrol. Earth Sys. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1061\/(ASCE)0733-9437(2007)133:4(380)","article-title":"Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)","volume":"133","author":"Allen","year":"2007","journal-title":"Model. J. Irrig. Drain. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0022-1694(99)00202-4","article-title":"SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey","volume":"229","author":"Bastiaanssen","year":"2000","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"627","DOI":"10.2151\/jmsj.2013-505","article-title":"Evaluation of empirical remote sensing-based empirical equations for estimating soil heat flux","volume":"91","author":"Sun","year":"2013","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1002\/2016JG003591","article-title":"Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally","volume":"121","author":"Purdy","year":"2016","journal-title":"J. Geophys. Res. Biog."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.3390\/rs3081627","article-title":"Soil heat flux modeling using artificial neural networks and multispectral airborne remote sensing imagery","volume":"3","year":"2011","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"De Andrade, B.C.C., Pedrollo, O.C., Ruhoff, A., Moreira, A.A., Laipelt, L., Kayser, R.B., Biudes, M.S., dos Santos, C.A.C., Roberti, D.R., and Machado, N.G. (2021). Artificial neural network model of soil heat flux over multiple land covers in South America. Remote Sens., 13.","DOI":"10.3390\/rs13122337"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Delogu, E., Boulet, G., Olioso, A., Garrigues, S., Brut, A., Tallec, T., Demarty, J., Soudani, K., and Lagouarde, J.-P. (2018). Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates. Remote Sens., 10.","DOI":"10.3390\/rs10111806"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.apsoil.2010.06.004","article-title":"Increase in above ground fresh litter quantity over-stimulates soil respiration in a temperate deciduous forest","volume":"46","author":"Soudani","year":"2010","journal-title":"Appl. Soil Ecol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1016\/j.agrformet.2009.05.004","article-title":"Carbon balance of a three crop succession over two cropland sites in South West France","volume":"149","author":"Ceschia","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.5194\/hess-19-3109-2015","article-title":"Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: Impact of soil hydraulic and vegetation properties","volume":"19","author":"Garrigues","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.agrformet.2007.05.012","article-title":"Monitoring water stress using time series of observed to unstressed Surface temperature difference","volume":"146","author":"Boulet","year":"2007","journal-title":"Agric. For. Met."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5161","DOI":"10.1080\/01431160802036417","article-title":"An integrated modelling and remote sensing approach for hydrological study in arid and semi-arid regions: The SUDMED programme","volume":"29","author":"Chehbouni","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jhydrol.2009.06.021","article-title":"The AMMA-CATCH experiment in the cultivated Sahelian area of south-west Niger\u2013Investigating water cycle response to a fluctuating climate and changing environment","volume":"375","author":"Cappelaere","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5001","DOI":"10.5194\/hess-18-5001-2014","article-title":"Building a field- and model-based climatology of surface energy and water cycles for dominant land cover types in the cultivated Sahel. Annual budgets and seasonality","volume":"18","author":"Velluet","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0034-4257(89)90076-X","article-title":"The Application of a Weighted Infrared-Red Vegetation Index for Estimating Leaf-Area Index by Correcting for soil moisture","volume":"29","author":"Clevers","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.5194\/hess-18-1165-2014","article-title":"Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate","volume":"18","author":"Chirouze","year":"2014","journal-title":"Hydrol. Earth Sys. Sci."},{"key":"ref_44","first-page":"4150","article-title":"Global mapping of vegetation parameters from POLDER multi angular measurements for studies of surface atmosphere interactions: A pragmatic method and validation","volume":"107","author":"Roujean","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1080\/01431160310001647984","article-title":"Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: Application to Denmark","volume":"25","author":"Boegh","year":"2004","journal-title":"Intern. J. Remote Sens."},{"key":"ref_46","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"IEEE Mach. Learn."},{"key":"ref_48","unstructured":"Haykin, S. (1998). Neural Networks: A Comprehensive Foundation, Prentice Hall."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_51","unstructured":"Kuhn, M. (2015). Caret: Classification and Regression Training, Astrophysics Source Code Library. R Package Version 6.0\u201330."},{"key":"ref_52","unstructured":"Team, R.C. (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1175\/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2","article-title":"Some comments on the Evaluation of Model Performance","volume":"63","author":"Willmott","year":"1982","journal-title":"Bull. Amer. Meteorol. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.jhydrol.2012.06.002","article-title":"A new parameterisation scheme of ground heat flux for land surface flux retrieval from remote sensing information","volume":"454","author":"Tanguy","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5194\/hess-6-85-2002","article-title":"The Surface Energy Balance Systems (SEBS) for estimation of turbulent heat fluxes. Hydrol","volume":"6","author":"Su","year":"2002","journal-title":"Earth Sys. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"747225","DOI":"10.1117\/12.830289","article-title":"Critical analysis of empirical ground heat flux empirical equations on a cereal field using micrometeorological data","volume":"Volume 7472","author":"Cammelleri","year":"2009","journal-title":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XI"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Meyer, H., Katurji, M., Appelhans, T., M\u00fcller, M.U., Nauss, T., Roudier, P., and Zawar-Reza, P. (2016). Mapping Daily Air Temperature for Antarctica Based on MODIS LST. Remote Sens., 8.","DOI":"10.3390\/rs8090732"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2013.10.026","article-title":"Improving the accuracy of rainfall rates from optical satellite sensors with machine learning: A random forests-based approach applied to MSG SEVIRI","volume":"141","author":"Kuhnlein","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.5194\/bg-13-4291-2016","article-title":"Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms","volume":"13","author":"Tramontana","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4101","DOI":"10.5194\/bg-14-4101-2017","article-title":"Water, Energy and Carbon with Artificial Neural Networks (WECANN): A statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence","volume":"14","author":"Alemohammad","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"14496","DOI":"10.1029\/2019GL085291","article-title":"Physics-constrained machine learning of evapotranspiration","volume":"46","author":"Zhao","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_63","first-page":"86","article-title":"Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing","volume":"78","author":"Carter","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.agwat.2019.03.015","article-title":"Evapotranspiration evaluation models based on machine learning algorithms\u2014A comparative study","volume":"217","author":"Granata","year":"2019","journal-title":"Agricul. Water Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1788\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:05Z","timestamp":1760136605000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":64,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081788"],"URL":"https:\/\/doi.org\/10.3390\/rs14081788","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202202.0199.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]}}}