## 5 Conclusions  
This study used six months of data from the NASA CYGNSS GNSS-R mission from March to August 2019 to estimate ionospheric amplitude scintillation to try to analyze possible correlations between the occurrence of earthquakes and the associated changes in the ionospheric amplitude scintillation index \(S_{4}\), and also try to determine if these changes are produced with higher probabilities before (as a precursor) or after the earthquakes. The technique used computes the confusion matrix (CM), a usual technique in diagnostic tests, e.g., in medical sciences or machine learning algorithms, to quantify the goodness of a diagnostic test. The results presented in this work show a small, but detectable, positive correlation for all earthquakes with magnitudes above 4, with better results as the magnitude increases. A slightly better correlation was also observed when the positive increments in the \(S_{4}\) index are observed between 6 and 3 days before the earthquakes, with respect to the ones observed after them. These results have been extracted from the analysis of ROC curves for different magnitude intervals showing the relation between the probability of finding a correct prediction vs. a false alarm. In the best cases, the correct prediction probability is around 32% and the false alarm probability is 16%. It should be also pointed out, that the probability of detection is small for all the cases. In our opinion, despite these results showing possible evidence of ionospheric scintillation increments as precursors of earthquakes, mostly for the largest magnitude ones, the signature is still small, and it should not be regarded as an early warning system for earthquakes. Moreover, the study was made using earthquakes during a 6 month period, when only a small number of large earthquakes (\(M\geq 6\)) took place, specifically 68. From them, only 47 were in oceanic regions. Moreover, the region covered by CYGNSS is always close to the geomagnetic equator, where ionospheric scintillation is also more probable to appear. The novelty of this study lies in the use of GNSS-R global oceanic data to measure amplitude scintillation \(S_{4}\) positive increments as a possible precursor for earthquakes. Other studies detected anomalies in the ionosphere, but they were mostly related to changes in the Total Electron Content (TEC) after the earthquakes, which can be produced by the mechanical energy transmission from the sudden crust movement through the atmosphere to the ionosphere. Furthermore, few of the previous studies used satellite data, allowing the possibility to perform global studies such as this one, and they were focused on particular cases using local GNSS ground stations. Future work will extend this study using longer periods of time, and larger Earth coverage. CYGNSS constellation used to obtain the GNSS-R data used here only covers from 40\({}^{\circ}\)N to 40\({}^{\circ}\)S, which is good to have a better revisit time, but it is missing earthquakes occurring at higher latitudes. Furthermore, this technique, using GNSS-R is only valid for calm oceanic regions, which loses around 30% of the earthquakes. So, a possible way to study ionospheric scintillation on land would be to use GNSS Radio-Occultation (GNSS-RO) data as well. Using this technique the overall performance may be even better as it does not suffer from the ocean surface conditions. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14112555/s1](https://www.mdpi.com/article/10.3390/rs14112555/s1)]([https://www.mdpi.com/article/10.3390/rs14112555/s1](https://www.mdpi.com/article/10.3390/rs14112555/s1)), Video S1: anomaly_s4_180 days_fullCoverage.m4v, Video S2: case_study.m4v. Conceptualization, C.M. and A.C.; methodology, C.M. and A.C.; software, C.M. ; validation, C.M., B.-E.B.S., H.P., and A.C.; formal analysis, C.M. ; investigation, C.M., B.-E.B.S., H.P., and A.C.; resources, A.C.; data curation, C.M. ; writing--original draft preparation, C.M.