We investigate the influence of the perturbed (by a 1 solar X-ray flare) ionospheric D-region on the global navigation 2 satellite systems (GNSS) and synthetic aperture radar (SAR) 3 signals. We calculate a signal delay in the D-region based on 4 the low ionospheric monitoring by very-low-frequency (VLF) 5 radio waves. The results show that the ionospheric delay in the 6 perturbed D-region can be important and, therefore, should be 7 taken into account in modeling the ionospheric influence on the 8 GNSS and SAR signal propagation and in calculations relevant 9 for space geodesy. This conclusion is significant because numerous 10 existing models ignore the impact of this ionospheric part on the 11 GNSS and SAR signals due to its small electron density which is 12 true only in quiet conditions and can result in significant errors 13 in space geodesy during intensive ionospheric disturbances. 14 Index Terms-Global navigation satellite systems (GNSS), 15 ionosphere, synthetic aperture radar (SAR) interferometry 16 (InSAR), very-low-frequency (VLF) radio signals. 17 I. INTRODUCTION 18 N OWADAYS spaceborne measurements of positioning, 19 displacements, navigation, and timing play an important 20 and critical role in telecommunications, geodesy, all forms 21 of transportation and other human activities. These mea-22 surements are primarily provided by the global navigation

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This paper is dedicated to modeling extreme TEC (Total Electron Content) values at the territory of Serbia. For the extreme TEC values, we consider the maximum values from the peak of the 11-year cycle of solar activity in the years 2013, 2014 and 2015 for the days of the winter and summer solstice and autumnal and vernal equinox. The average TEC values between 10 and 12 UT (Universal Time) were treated. As the basic data for all processing, we used GNSS (Global Navigation Satellite System) observation obtained by three permanent stations located in the territory of Serbia. Those data, we accept as actual, i.e. as a "true TEC values". The main objectives of this research were to examine the possibility to use two machine learning techniques: neural networks and support vector machine. In order to emphasize the quality of applied techniques, all results are adequately compared to the TEC values obtained by using International Reference Ionosphere global model. In addition, we separately analyzed the quality of techniques throughout temporal and spatial-temporal approach.