Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km² (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas.

Knowledge on the ionospheric total electron content (TEC) and its prediction are of great practical importance and engineering relevance in many scientific disciplines. We investigate regular ionospheric anomalies and TEC prediction by applying the least squares harmonic estimation (LS-HE) technique to a 15 year time series of the vertical TEC (VTEC) from 1998 to 2014. We first detected a few new regular and modulated signals in the TEC time series. The multivariate analysis of the time series indicates that there are diurnal, annual, 11 year, and 27 day periodic signals, as well as their higher harmonics. We also found periods matching with the global positioning system (GPS) draconitic year in the TEC time series. The results from the modulated harmonic analysis indicate that there exists a set of peaks with periods of $1\pm 0.0027j$ ( $j=1,\dots ,5$ ) and $1\pm 0.00025j$ ( $j=1,2,3$ ) days. The same situation holds also true for the harmonics higher than the diurnal signal. A model is then adopted based on the discovered periods. This model, which consists of pure and modulated harmonic functions, is shown to be appropriate for assessing the regular variations and ionospheric anomalies. There is a clear maximum TEC at around 22:00 h, which we called the “evening anomaly”. The evening anomaly occurs in the winter and autumn, and is dependent on the solar activities. Also, the Semiannual, Winter, and Equatorial anomalies were investigated. Finally, to investigate the performance of the derived model, the TEC values have been predicted monthly, and the results show that the modulated signals can significantly contribute to obtaining superior prediction results. Compared with the pure signals, the modulated signals can improve a yearly average root mean squared error (RMSE) value in the lower and higher solar activities by 20% and 15%, respectively.

Coastal sea level variation as an indicator of climate change is extremely important due to its large socioeconomic and environmental impacts. The ground-based global navigation satellite system (GNSS)-reflectometry (GNSS-R) is becoming a reliable alternative for sea surface altimetry. We investigate the impact of antenna polarization and orientation on GNSS-R altimetric performance at different carrier frequencies. A one-year dataset of ground-based observations at the Onsala Space Observatory using a dedicated reflectometry receiver is used. Interferometric patterns produced by the superposition of direct and reflected signals are analyzed using the least-squares harmonic estimation (LS-HE) method to retrieve sea surface height. The results suggest that the observations from global positioning system (GPS) L1 and L2 frequencies provide similar levels of accuracy. However, the overall performance of the height products from the GPS L1 shows slightly better performance due to more observations. The combination of L1 and L2 observations (L12) improves the accuracy up to 25% and 40% compared to the L1 and L2 heights. The impacts of antenna orientation and polarization are also evaluated. A sea-looking left-handed circular polarization (LHCP) antenna shows the best performance compared to both zenith- and sea-looking right-handed circular polarization (RHCP) antennas. The results are presented using different averaging windows ranging from 15 min to 6 h. Based on a 6-h window, the yearly root mean squared errors (RMSEs) between GNSS-R L12 sea surface heights with collocated tide gauge observations are 2.4, 3.1, and 4.1 cm with the correlation of 0.990, 0.982, and 0.969 for LHCP sea-looking, RHCP sea-looking, and RHCP up-looking antennas, respectively.

Tidal analysis and methods for estimation and prediction of ocean tidal constitutes are essential in a large area of scientific disciplines, for example, navigation, onshore and offshore engineering, and production of green energy. Ground-based Global Navigation Satellite System-Reflectometry (GNSS-R) has been proposed as an alternative method for measuring sea surface height. We use 6 years of GNSS-R observations at In-phase and Quadrature levels from July 2015 to May 2021 obtained from a dedicated receiver and sea-looking left hand circular polarization antenna for estimating sea level (SL). In the first step, the multivariate least-square harmonic estimation (LS-HE) method is applied for SL estimation. Then, final SL time series are generated by combining estimated SL from all satellites at L1 and L2 frequencies in the averaging step. The 6-year root-mean-square error between GNSS-R L12 sea surface heights and a collocated tide gauge (TG) is 5.8 cm with a correlation of 0.948 for a high temporal resolution of 5 min with 15 min averaging window. Afterward, using the univariate LS-HE, we detect tidal harmonics with periods between 30 min to 1 year. The detection results highlight a good match between GNSS-R and TG. Higher harmonics, i.e., the periods shorter than 3 h, show stronger signatures in GNSS-R data. Finally, we estimate the amplitude and phase of standard tidal harmonics from the two datasets. The results show an overall good agreement between the datasets with a few exceptions.

Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge.  We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.