{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T15:23:49Z","timestamp":1775834629547,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T00:00:00Z","timestamp":1654992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012826","name":"Ministry of Energy","doi-asserted-by":"publisher","award":["219-17-005"],"award-info":[{"award-number":["219-17-005"]}],"id":[{"id":"10.13039\/100012826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012826","name":"Ministry of Energy","doi-asserted-by":"publisher","award":["1602\/19"],"award-info":[{"award-number":["1602\/19"]}],"id":[{"id":"10.13039\/100012826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Israel Science Foundation","award":["219-17-005"],"award-info":[{"award-number":["219-17-005"]}]},{"name":"Israel Science Foundation","award":["1602\/19"],"award-info":[{"award-number":["1602\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth\u2019s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (&gt;Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).<\/jats:p>","DOI":"10.3390\/rs14122822","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2822","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4826-5935","authenticated-orcid":false,"given":"Saed","family":"Asaly","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, Ariel University, Ariel 40700, Israel"}]},{"given":"Lee-Ad","family":"Gottlieb","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Ariel University, Ariel 40700, Israel"}]},{"given":"Nimrod","family":"Inbar","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Ariel University, Ariel 40700, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8902-5540","authenticated-orcid":false,"given":"Yuval","family":"Reuveni","sequence":"additional","affiliation":[{"name":"Department of Physics, Ariel University, Ariel 40700, Israel"},{"name":"Department of Geophysics, Eastern R&D Center, Ariel 40700, Israel"},{"name":"Astrophysics Geophysics and Space Science Research Center, Ariel University, Ariel 40700, Israel"},{"name":"School of Sustainability, Reichman University, IDC, Herzliya 4610101, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1038\/s41586-018-0438-y","article-title":"Deep learning of aftershock patterns following large earthquakes","volume":"560","author":"DeVries","year":"2018","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1785\/0120170313","article-title":"Can animals predict earthquakes?","volume":"108","author":"Woith","year":"2018","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_3","first-page":"88","article-title":"Earthquake\u2014a natural disaster, prediction, mitigation, laws and government policies, impact on biogeochemistry of earth crust, role of remote sensing and gis in management in india\u2014An overview","volume":"7","author":"Singh","year":"2019","journal-title":"J. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, H., Wang, P., and Jing, L. (2020). An image registration method for multisource high-resolution remote sensing images for earthquake disaster assessment. Sensors, 20.","DOI":"10.3390\/s20082286"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s11069-021-04877-5","article-title":"Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models","volume":"109","author":"Ha","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/JSTARS.2020.3044470","article-title":"Using support vector machine (SVM) and ionospheric total electron content (TEC) data for solar flare predictions","volume":"14","author":"Asaly","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1088\/0034-4885\/67\/8\/R03","article-title":"The physics of earthquakes","volume":"67","author":"Kanamori","year":"2004","journal-title":"Rep. Prog. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Scholz, C.H. (2019). The Mechanics of Earthquakes and Faulting, Cambridge University Press.","DOI":"10.1017\/9781316681473"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.epsl.2013.04.020","article-title":"Organization of the tectonic plates in the last 200 Myr","volume":"373","author":"Morra","year":"2013","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1111\/j.1365-246X.1992.tb00111.x","article-title":"Models of convection-driven tectonic plates: A comparison of methods and results","volume":"109","author":"King","year":"1992","journal-title":"Geophys. J. Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s40623-016-0400-x","article-title":"The present-day number of tectonic plates","volume":"68","author":"Harrison","year":"2016","journal-title":"Earth Planets Space"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.cageo.2018.04.007","article-title":"Global tectonic reconstructions with continuously deforming and evolving rigid plates","volume":"116","author":"Gurnis","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_13","unstructured":"Rauter, M., and Winkler, D. (2018). Predicting natural hazards with neuronal networks. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.enggeo.2018.11.011","article-title":"Mechanics of the earthquake-induced Hongshiyan landslide in the 2014 Mw 6.2 Ludian earthquake, Yunnan, China","volume":"251","author":"Luo","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lapusta, N. (2019, January 17\u201320). Mechanics of Earthquake Source Processes: Insights from Numerical Modeling. Proceedings of the International Conference on Theoretical, Applied and Experimental Mechanics, Paphos, Cyprus.","DOI":"10.1007\/978-3-030-21894-2_30"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.epsl.2005.06.010","article-title":"Directivity and apparent velocity of the coseismic ionospheric disturbances observed with a dense GPS array","volume":"236","author":"Heki","year":"2005","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Heki, K., Otsuka, Y., Choosakul, N., Hemmakorn, N., Komolmis, T., and Maruyama, T. (2006). Detection of ruptures of Andaman fault segments in the 2004 great Sumatra earthquake with coseismic ionospheric disturbances. J. Geophys. Res. Solid Earth, 111.","DOI":"10.1029\/2005JB004202"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Astafyeva, E., Heki, K., Kiryushkin, V., Afraimovich, E., and Shalimov, S. (2009). Two-mode long-distance propagation of coseismic ionosphere disturbances. J. Geophys. Res. Space Phys., 114.","DOI":"10.1029\/2008JA013853"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.5194\/nhess-11-1019-2011","article-title":"Investigation of TEC and VLF space measurements associated to L\u2019Aquila (Italy) earthquakes","volume":"11","author":"Stangl","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kuo, C., Huba, J., Joyce, G., and Lee, L. (2011). Ionosphere plasma bubbles and density variations induced by pre-earthquake rock currents and associated surface charges. J. Geophys. Res. Space Phys., 116.","DOI":"10.1029\/2011JA016628"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1002\/2013JA019392","article-title":"An improved coupling model for the lithosphere-atmosphere-ionosphere system","volume":"119","author":"Kuo","year":"2014","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hayakawa, M., Hobara, Y., Yasuda, Y., Yamaguchi, H., Ohta, K., Izutsu, J., and Nakamura, T. (2012). Possible precursor to the March 11, 2011, Japan earthquake: Ionospheric perturbations as seen by subionospheric very low frequency\/low frequency propagation. Ann. Geophys., 55.","DOI":"10.4401\/ag-5357"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cohen, M.B., and Marshall, R. (2012). ELF\/VLF recordings during the 11 March 2011 Japanese Tohoku earthquake. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL052123"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1038\/176795a0","article-title":"Magnitude and energy of earthquakes","volume":"176","author":"Gutenberg","year":"1955","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.5047\/eps.2012.08.003","article-title":"Detecting ionospheric TEC perturbations caused by natural hazards using a global network of GPS receivers: The Tohoku case study","volume":"64","author":"Komjathy","year":"2012","journal-title":"Earth Planets Space"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009RS004336","article-title":"Natural atmospheric noise statistics from VLF measurements in the eastern Mediterranean","volume":"45","author":"Reuveni","year":"2010","journal-title":"Radio Sci."},{"key":"ref_27","first-page":"23","article-title":"The connection between meteor showers and VLF atmospheric noise signals","volume":"31","author":"Reuveni","year":"2011","journal-title":"J. Atmos. Electr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geog.2015.12.009","article-title":"Performance of GPS slant total electron content and IRI-Plas-STEC for days with ionospheric disturbance","volume":"7","author":"Arikan","year":"2016","journal-title":"Geod. Geodyn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3847\/1538-4365\/ac37bc","article-title":"Low-dimensional Convolutional Neural Network for Solar Flares GOES Time-series Classification","volume":"258","author":"Landa","year":"2022","journal-title":"Astrophys. J. Suppl. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/wics.1198","article-title":"Linear regression","volume":"4","author":"Su","year":"2012","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Wang, L. (2005). Support Vector Machines: Theory and Applications, Springer Science & Business Media.","DOI":"10.1007\/b95439"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nbt1386","article-title":"What are artificial neural networks?","volume":"26","author":"Krogh","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Reuveni, Y., and Price, C. (2009). A new approach for monitoring the 27-day solar rotation using VLF radio signals on the Earth\u2019s surface. J. Geophys. Res. Space Phys., 114.","DOI":"10.1029\/2009JA014364"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hargreaves, J.K. (1992). The Solar-Terrestrial Environment: An Introduction to Geospace-the Science of the Terrestrial Upper Atmosphere, Ionosphere, and Magnetosphere, Cambridge University Press.","DOI":"10.1017\/CBO9780511628924"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1093\/gji\/ggv253","article-title":"Calibrating interferometric synthetic aperture radar (InSAR) images with regional GPS network atmosphere models","volume":"202","author":"Reuveni","year":"2015","journal-title":"Geophys. J. Int."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Reuveni, Y., Kedar, S., Owen, S.E., Moore, A.W., and Webb, F.H. (2012). Improving sub-daily strain estimates using GPS measurements. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL051927"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/gji\/ggu208","article-title":"Analyzing slip events along the Cascadia margin using an improved subdaily GPS analysis strategy","volume":"198","author":"Reuveni","year":"2014","journal-title":"Geophys. J. Int."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1016\/j.jastp.2009.05.014","article-title":"Trends in the F2 ionospheric layer due to long-term variations in the Earth\u2019s magnetic field","volume":"71","author":"Elias","year":"2009","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0021-9169(83)80080-4","article-title":"The accuracy of simple methods for determining the height of the maximum electron concentration of the F2-layer from scaled ionospheric characteristics","volume":"45","author":"Dudeney","year":"1983","journal-title":"J. Atmos. Terr. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s00190-008-0217-x","article-title":"GPS observations of the ionospheric F2-layer behavior during the 20th November 2003 geomagnetic storm over South Korea","volume":"82","author":"Jin","year":"2008","journal-title":"J. Geod."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/S1364-6826(02)00034-2","article-title":"GPS\/GLONASS-based TEC measurements as a contributor for space weather forecast","volume":"64","author":"Jakowski","year":"2002","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"263","DOI":"10.5194\/angeo-35-263-2017","article-title":"Near real-time estimation of ionosphere vertical total electron content from GNSS satellites using B-splines in a Kalman filter","volume":"Volume 35","author":"Erdogan","year":"2017","journal-title":"Annales Geophysicae"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"537","DOI":"10.5194\/amt-10-537-2017","article-title":"Combining Meteosat-10 satellite image data with GPS tropospheric path delays to estimate regional integrated water vapor (IWV) distribution","volume":"10","author":"Leontiev","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"14785","DOI":"10.1038\/s41598-018-33163-x","article-title":"Augmenting GPS IWV estimations using spatio-temporal cloud distribution extracted from satellite data","volume":"8","author":"Leontiev","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6433","DOI":"10.1002\/joc.7205","article-title":"Long-term variability and trends of precipitable water vapour derived from GPS tropospheric path delays over the Eastern Mediterranean","volume":"41","author":"Alpert","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105307","DOI":"10.1016\/j.atmosres.2020.105307","article-title":"The diurnal variability of precipitable water vapor derived from GPS tropospheric path delays over the Eastern Mediterranean","volume":"249","author":"Ziv","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lynn, B., Yair, Y., Levi, Y., Ziv, S.Z., Reuveni, Y., and Khain, A. (2021). Impacts of Non-Local versus Local Moisture Sources on a Heavy (and Deadly) Rain Event in Israel. Atmosphere, 12.","DOI":"10.3390\/atmos12070855"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Leontiev, A., Rostkier-Edelstein, D., and Reuveni, Y. (2020). On the potential of improving WRF model forecasts by assimilation of high-resolution GPS-derived water-vapor maps augmented with METEOSAT-11 data. Remote Sens., 13.","DOI":"10.3390\/rs13010096"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1002\/2015RS005916","article-title":"Three methods to retrieve slant total electron content measurements from ground-based GPS receivers and performance assessment","volume":"51","author":"Zhang","year":"2016","journal-title":"Radio Sci."},{"key":"ref_51","unstructured":"Van Dierendonck, A., Hua, Q., Fenton, P., and Klobuchar, J. (1996, January 19\u201321). Commercial ionospheric scintillation monitoring receiver development and test results. Proceedings of the 52nd Annual Meeting of The Institute of Navigation (1996), Cambridge, MA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"719","DOI":"10.2478\/s11600-009-0066-x","article-title":"Toward a unified solid state theory for pre-earthquake signals","volume":"58","author":"Freund","year":"2010","journal-title":"Acta Geophys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1002\/2017JA024012","article-title":"Ionospheric anomalies immediately before Mw7.0\u20138.0 earthquakes","volume":"122","author":"He","year":"2017","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6689","DOI":"10.1002\/2016JA023601","article-title":"Apparent ionospheric total electron content variations prior to major earthquakes due to electric fields created by tectonic stresses","volume":"122","author":"Kelley","year":"2017","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7006","DOI":"10.1002\/2015JA021353","article-title":"Mw dependence of the preseismic ionospheric electron enhancements","volume":"120","author":"Heki","year":"2015","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.inffus.2018.03.006","article-title":"Visual and textual information fusion using Kernel method for content based image retrieval","volume":"44","author":"Unar","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11704-018-8148-z","article-title":"A primal perspective for indefinite kernel SVM problem","volume":"14","author":"Xue","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10845-015-1089-6","article-title":"Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function","volume":"29","author":"Zhou","year":"2018","journal-title":"J. Intell. Manuf."},{"key":"ref_60","first-page":"26","article-title":"Hyperparameter optimization for machine learning models based on Bayesian optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_61","first-page":"2960","article-title":"Practical bayesian optimization of machine learning algorithms","volume":"25","author":"Snoek","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_62","unstructured":"Acerbi, L., and Ma, W.J. (2017). Practical Bayesian optimization for model fitting with Bayesian adaptive direct search. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6618","DOI":"10.1002\/jgra.50578","article-title":"Preseismic ionospheric electron enhancements revisited","volume":"118","author":"Heki","year":"2013","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"403","DOI":"10.5194\/angeo-35-403-2017","article-title":"High-order ionospheric effects on electron density estimation from Fengyun-3C GPS radio occultation","volume":"Volume 35","author":"Li","year":"2017","journal-title":"Annales Geophysicae"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s00190-020-01360-0","article-title":"IGS real-time service for global ionospheric total electron content modeling","volume":"94","author":"Li","year":"2020","journal-title":"J. Geod."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2822\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:28:29Z","timestamp":1760138909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,12]]},"references-count":65,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122822"],"URL":"https:\/\/doi.org\/10.3390\/rs14122822","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,12]]}}}