{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:34:15Z","timestamp":1770831255196,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The low-latitude ionosphere has an active behavior causing the total electron content (TEC) to vary spatially and temporally very dynamically. The solar activity and the geomagnetic field have a strong influence over the spatiotemporal distribution of TEC. These facts make it a challenge to attempt modeling the ionization response. Single frequency GNSS users are particularly vulnerable due to these ionospheric variations that cause degradation of positioning performance. Motivated by recent applications of machine learning, temporal series of TEC available in map formats were employed to build an independent TEC estimator model for low-latitude environments. A TEC dataset was applied along with geophysical indices of solar flux and magnetic activity to train a feedforward artificial neural network based on a multilayer perceptron (MLP) approach. The forecast for the next 24 h was made relying on TEC maps over the Brazilian region using data collected on the previous 5 days. The performance of this approach was evaluated and compared with real data. The accuracy of the model was evaluated taking into account seasonality, spatial coverage and dependence on solar flux and geomagnetic activity indices. The results of the analysis show that the developed model has a superior capacity describing the TEC behavior across Brazil, when compared to global ionosphere maps and the NeQuick G model. TEC predictions were applied in single point positioning. The achieved errors were 27% and 33% lower when compared to the results obtained using the NeQuick G and global ionosphere maps, respectively, showing success in estimating TEC with small recent datasets using MLP.<\/jats:p>","DOI":"10.3390\/rs15020412","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T01:57:48Z","timestamp":1673315868000},"page":"412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Using Deep Learning to Map Ionospheric Total Electron Content over Brazil"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6034-3991","authenticated-orcid":false,"given":"Andre","family":"Silva","sequence":"first","affiliation":[{"name":"Instituto Tecnol\u00f3gico de Aeron\u00e1utica, S\u00e3o Jos\u00e9 dos Campos 12228-900, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6493-1694","authenticated-orcid":false,"given":"Alison","family":"Moraes","sequence":"additional","affiliation":[{"name":"Instituto de Aeron\u00e1utica e Espa\u00e7o, S\u00e3o Jos\u00e9 dos Campos 12228-904, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-8009","authenticated-orcid":false,"given":"Jonas","family":"Sousasantos","sequence":"additional","affiliation":[{"name":"William B. Hanson Center for Space Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-4476","authenticated-orcid":false,"given":"Marcos","family":"Maximo","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico de Aeron\u00e1utica, S\u00e3o Jos\u00e9 dos Campos 12228-900, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-9227","authenticated-orcid":false,"given":"Bruno","family":"Vani","sequence":"additional","affiliation":[{"name":"Instituto Federal de Educa\u00e7\u00e3o, Ci\u00eancia e Tecnologia de S\u00e3o Paulo, Presidente Epit\u00e1cio 19470-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-5495","authenticated-orcid":false,"suffix":"Jr.","given":"Clodoaldo","family":"Faria","sequence":"additional","affiliation":[{"name":"Instituto Federal de Educa\u00e7\u00e3o, Ci\u00eancia e Tecnologia de S\u00e3o Paulo, Presidente Epit\u00e1cio 19470-000, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/S1364-6826(00)00201-7","article-title":"Outstanding problems in the equatorial ionosphere\u2013thermosphere electrodynamics relevant to spread F","volume":"63","author":"Abdu","year":"2001","journal-title":"J. Atmos. Solar-Terr. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vankadara, R.K., Panda, S.K., Amory-Mazaudier, C., Fleury, R., Devanaboyina, V.R., Pant, T.K., Jamjareegulgarn, P., Haq, M.A., Okoh, D., and Seemala, G.K. (2022). Signatures of Equatorial Plasma Bubbles and Ionospheric Scintillations from Magnetometer and GNSS Observations in the Indian Longitudes during the Space Weather Events of Early September 2017. Remote Sens., 14.","DOI":"10.3390\/rs14030652"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40562-016-0043-6","article-title":"Electrodynamics of ionospheric weather over low latitudes","volume":"3","author":"Abdu","year":"2016","journal-title":"Geosci. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/jatm.v13.1237","article-title":"Ground-based augmentation system operation in low latitudes-part 2: Space weather, ionospheric behavior and challenges","volume":"13","author":"Sousasantos","year":"2021","journal-title":"J. Aerosp. Technol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TAES.1987.310829","article-title":"Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users","volume":"AES-23","author":"Klobuchar","year":"1987","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1002\/2016SW001593","article-title":"International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions","volume":"15","author":"Bilitza","year":"2017","journal-title":"Space Weather."},{"key":"ref_7","unstructured":"European Commission\u2014European GNSS (Galileo) (2022, December 14). Open Service Ionospheric Correction Algorithm for Galileo Single Frequency Users. Available online: https:\/\/www.gsc-europa.eu\/sites\/default\/files\/sites\/all\/files\/Galileo_Ionospheric_Model.pdf."},{"key":"ref_8","unstructured":"Schaer, S., Gurtner, W., and Feltens, J. (1998, January 9\u201311). IONEX: The ionosphere map exchange format version 1.1. Proceedings of the IGS AC Workshop 1998, Darmstadt, Germany. Available online: http:\/\/ftp.aiub.unibe.ch\/ionex\/draft\/ionex11.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Reddybattula, K.D., Nelapudi, L.S., Moses, M., Devanaboyina, V.R., Ali, M.A., Jamjareegulgarn, P., and Panda, S.K. (2022). Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network. Universe, 8.","DOI":"10.3390\/universe8110562"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.actaastro.2020.08.034","article-title":"Modeling and analysis of ionospheric TEC variability from GPS\u2013TEC measurements using SSA model during 24th solar cycle","volume":"178","author":"Dabbakuti","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1016\/j.asr.2020.12.015","article-title":"Evaluation of the dusk and early nighttime Total Electron Content modeling over the eastern Brazilian region during a solar maximum period","volume":"67","author":"Silva","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.asr.2003.07.008","article-title":"Plasmaspheric electron content derived from GPS TEC and digisonde ionograms","volume":"33","author":"Belehaki","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_13","first-page":"99","article-title":"Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Re-construction over Iran","volume":"44","author":"Voosoghi","year":"2018","journal-title":"J. Earth Sp. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/JSTARS.2018.2877445","article-title":"Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method","volume":"12","author":"Mallika","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2020SW002501","DOI":"10.1029\/2020SW002501","article-title":"Forecasting Global Ionospheric TEC Using Deep Learning Approach","volume":"18","author":"Liu","year":"2020","journal-title":"Space Weather"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1051\/swsc\/2020013","article-title":"Neural network based model for global Total Electron Content forecasting","volume":"10","author":"Cesaroni","year":"2020","journal-title":"J. Space Weather Space Clim."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/JSTARS.2021.3132049","article-title":"Machine Learning-Based Short-Term GPS TEC Forecasting during High Solar Activity and Magnetic Storm Periods","volume":"15","author":"Han","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/jatm.v14.1249","article-title":"The GNSS NavAer INCT Project Overview and Main Results","volume":"14","author":"Monico","year":"2022","journal-title":"J. Aerosp. Technol. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e0123","DOI":"10.1590\/jatm.v15.1288","article-title":"A Retrospective of Global Navigation Satellite System Ionospheric Irregularities Monitoring Networks in Brazil","volume":"15","author":"Monico","year":"2023","journal-title":"J. Aerosp. Technol. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2020RS007158","DOI":"10.1029\/2020RS007158","article-title":"Regional Ionospheric Delay Mapping for Low-Latitude Environments","volume":"55","author":"Sousasantos","year":"2020","journal-title":"Radio Sci."},{"key":"ref_21","unstructured":"Misra, P., and Enge, P. (2006). GPS Measurements and Error Sources. Global Positioning System: Signals. Measurements and Performance, Ganga-Jamuna Press. [2nd ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s00190-006-0093-1","article-title":"Calibration errors on experimental slant total electron content (TEC) determined with GPS","volume":"81","author":"Ciraolo","year":"2006","journal-title":"J. Geodesy"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Teunissen, P.J., and Montenbruck, O. (2017). Springer Handbook of Global Navigation Satellite Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-42928-1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TGRS.2008.916642","article-title":"An evaluation of interpolation techniques for reconstructing ionospheric TEC maps","volume":"46","author":"Foster","year":"2008","journal-title":"IEEE Trans Geosci Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Morton, Y.J., van Diggelen, F., Spilker, J.J., Parkinson, B.W., Lo, S., and Gao, G. (2021). Position, Navigation, and Timing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications, John Wiley & Sons. [1st ed.].","DOI":"10.1002\/9781119458449"},{"key":"ref_26","unstructured":"G\u00e9ron, A. (2019). Hands-On Machine Learning with Sckit-Learn, Keras and TensorFlow, O\u2019Reilly Media, Inc.. [2nd ed.]."},{"key":"ref_27","unstructured":"Jason, B. (2018). Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery. [1st ed.]."},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. [1st ed.]."},{"key":"ref_29","unstructured":"Haykin, S. (2008). Neural Netwoks and Learning Machines, Pearson Education. [3rd ed.]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1016\/S1364-6826(02)00089-5","article-title":"Ionospheric plasma bubble climatology over Brazil based on 22 years (1977\u20131998) of 630nm airglow observations","volume":"64","author":"Sobral","year":"2002","journal-title":"J. Atm. Solar-Terr. Phy."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1002\/jgra.50378","article-title":"Comparative analysis of spread-F signature and GPS scintillation occurrences at Tucum\u00e1n, Argentina","volume":"118","author":"Alfonsi","year":"2013","journal-title":"J. Geophy. Res. Spc. Phy."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.asr.2007.09.043","article-title":"Solar flux effects on equatorial ionization anomaly and total electron content over Brazil: Observational results versus IRI representations","volume":"42","author":"Abdu","year":"2008","journal-title":"Adv. Space Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1029\/RG010i004p00935","article-title":"Geomagnetic indices","volume":"10","author":"Rostoker","year":"1972","journal-title":"Rev. Geophys."},{"key":"ref_34","unstructured":"Kaplan, E., and Hegarty, C. (2006). Understanding GPS: Principles and Applications, Artech House. [2nd ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4487","DOI":"10.1029\/JC074i018p04487","article-title":"Two-quartic tropospheric refractivity profile for correcting satellite data","volume":"74","author":"Hopfield","year":"1969","journal-title":"J. Geophys. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13723","DOI":"10.1029\/JA091iA12p13723","article-title":"The prereversal enhancement of the zonal electric field in the equatorial ionosphere","volume":"91","author":"Farley","year":"1986","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s11200-007-0015-6","article-title":"A neural network approach for regional vertical total electron content modelling","volume":"51","author":"Leandro","year":"2007","journal-title":"Stud. Geophys. Geod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1016\/j.asr.2017.06.001","article-title":"Short-term estimation of GNSS TEC using a neural network model in Brazil","volume":"60","author":"Ferreira","year":"2017","journal-title":"Adv. Space Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:01Z","timestamp":1760119501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020412"],"URL":"https:\/\/doi.org\/10.3390\/rs15020412","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]}}}