High-resolution water vapor fields retrieved over Iberia during the passage of storm Barbara (October 19–20, 2020) by Sentinel-1 and assimilated by the Weanther Research & Forecasting Model (WRF) reveal a substantial positive impact on water vapor forecasting. Due to the path followed by the storm across Iberia, from its southwestern to the northeastern corners, and the geometry of Sentinel-1 data acquisition, it is possible to show, for the first time, the potential added value of precipitable water vapor (PWV) obtained by the Interferometric Synthetic-Aperture Radar (InSAR) technique, as a data source for both the forecast and validation of meteorological forecasts of synoptic-scale storms. Results indicate that data assimilated in the InSARfootprint positively impact the downstream forecasts up to the northeastern boundary, about 850km and 12 hours away, with improved skill scores of the water vapor distribution and improved forecasts of rain.

This article presents a study of the relationship among decorrelation phase in synthetic aperture radar (SAR) interferogram, soil moisture, and water content in vegetation with the aim of mitigating the contribution of decorrelation phase in SAR interferometry estimates of terrain displacements. A methodology for the mitigation of the phase bias based on the temporal variation of the vegetation water content is presented. Decorrelation phases are computed using time series of Sentinel-1 images and compared with in situ measurements of soil moisture. It is shown that soil moisture can partially explain the observed values of decorrelation phases pointing out the role of vegetation water content. A new model is proposed to compute the contribution of vegetation to the decorrelation phase based on the normalized difference water index (NDWI) index. The methodology is applied to all short temporal baseline interferograms obtained from the time series of Sentinel-1 SAR images, using the NDWI maps generated from Sentinel-2 multispectral images. The cumulative displacement is computed by integrating the short temporal baseline interferograms, corrected for the land cover and soil moisture changes. It is shown that the proposed methodology can reduce the variance of estimated cumulative displacement in areas covered by vegetation.

Accuracy of radar interferometry is often hindered by the atmospheric phase screen (APS). To address this limitation, the geostatistical approach known as Kriging has been employed to predict APS from sparse observations for compensation purposes. In this article, we propose an enhanced Kriging approach to achieve more accurate APS predictions in ground-based (GB) radar interferometry applications. Specifically, the Kriging system is augmented with a time-series measure through correlation analysis, effectively leveraging spatiotemporal information for APS prediction. The validity of the introduced Kriging method in the APS compensation framework was tested with Ku-band GB radar datasets collected over two different mountainous sites. A comparison of this method with simple Kriging reveals a noticeable improvement in APS prediction accuracy and temporal phase stability.

We study the sub-ionospheric VLF transmitter signals recorded by the Austrian Graz station in the year 2020. Those radio signals are known to propagate in the Earth-ionosphere waveguide between the ground and lower ionosphere. The Austrian Graz facility (geographic coordinates: 15.46 degrees E, 47.03 degrees N) can receive such sub-ionospheric transmitter signals, particularly those propagating above earthquake (EQ) regions in the southern part of Europe. We consider in this work the transmitter amplitude variations recorded a few weeks before the occurrence of two EQs in Croatia at a distance less than 200 km from Graz VLF facility. The selected EQs happened on 22 March 2020 and 29 December 2020, with magnitudes of Mw5.4 and Mw6.4, respectively, epicenters localized close to Zagreb (16.02 degrees E, 45.87 degrees N; 16.21 degrees E, 45.42 degrees N), and with focuses of depth smaller than 10 km. In our study we emphasize the anomaly fluctuations before/after the sunrise times, sunset times, and the cross-correlation of transmitter signals. We attempt to evaluate and to estimate the latitudinal and the longitudinal expansions of the ionospheric disturbances related to the seismic preparation areas.

The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda.

The interferometric synthetic aperture radar (InSAR) technique has demonstrated its ability to capture temporal variations in tropospheric water vapor, providing a valuable source of information for numerical weather prediction (NWP) models. Integrating InSAR data into NWP models has the potential to significantly enhance their forecasting capabilities, especially for predicting local extreme weather events. The challenge lies in extracting a single epoch from the InSAR differential observations. In this work, we introduced a method based on the least-squares approach to estimate single epochs using the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWFs) as a first guess. By leveraging ERA5 data, distinct atmospheric components can be disentangled without additional assumptions or external measurements. Since ERA5 is globally available at 1-h temporal resolution, the proposed method can be applied in remote areas without in situ data, providing improved high-resolution maps at all times (day/night) and in all weather conditions.

The health monitoring of infrastructure is vital for ensuring the safety and structural integrity of bridges. Recently, ground-based real aperture radar (GB-RAR) systems have been successfully utilized in the dynamic and static monitoring of bridges. In this study, a comprehensive and innovative approach is presented to monitor the vertical deformation of a long-span metallic railway bridge and a reinforced concrete Shinkansen bridge in Japan using a polarimetric GB-RAR system. Distinct from conventional signal processing procedures, the proposed method omits the coherent scatterer selection step. Instead, polarization analysis is employed to evaluate the properties of scatterers and identify those corresponding to bridge sections requiring monitoring, while considering the structural characteristics of the bridge. Simultaneously, the signal-to-noise ratio for monitoring is enhanced by combining co-polarization responses from scatterers. Furthermore, the radar look angle is determined by accounting for the spatial configuration of the survey and the polarization orientation angle. Lastly, vertical deformation is assessed by projecting line of sight deformation in the vertical direction. The findings reveal the dynamic responses of the two bridges under diverse loading conditions, which include the transit of a low-speed train and a high-speed Shinkansen bullet train. The results demonstrate that the polarimetric GB-RAR interferometry technique, coupled with the developed algorithms, can be effectively applied to monitor any type of bridge with unparalleled spatial and temporal resolutions.

Recent research shows reductions in the VLF signal noise amplitude that begin before particular earthquakes whose epicentres are more than 100 km away from the signal propagation path. In this paper, we extend this research to studying the noise amplitude during periods of intense seismic activity in a localized area. We analyse variations in the VLF signal noise amplitude over a period of 10 days (25 October-3 November 2016) when 981 earthquakes with the minimum magnitude of 2 occurred in Central Italy. Out of these events, 31 had the magnitude equal or greater than 4, while the strongest one had the magnitude of 6.5. We observe the VLF signal emitted by the ICV transmitter located in Sardinia (Italy) and recorded in Belgrade (Serbia). Bearing in mind that the trajectory of this signal crosses the area in which the observed earthquakes occurred, we extend the existing research to study of variations in the noise amplitude of the signal propagating at short distances from the epicentres of the considered earthquakes. In addition, we analyse the impact of a large number earthquakes on characteristics of the noise amplitude and its reductions before particular events. In order to examine the localization of the recorded changes, we additionally analysed the noise amplitude of two reference signals emitted in Germany and Norway. The obtained results show the existence of the noise amplitude reduction preceding individual strong or relatively strong earthquakes, and earthquakes followed by others that occurred in a shorter time interval. However, the additional noise amplitude reductions are either not pronounced or they do not exist before the considered events in periods of the reduced noise amplitude remain from previous earthquakes. Reductions in noise amplitudes for all observed signals indicate a larger perturbed area through which they spread or its closer location to the receiver. The analysis of daily values of parameters describing the noise amplitude reveals their variations start up to 2 weeks before the seismically active period occurs.

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared with in situ data collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m3/m3 was found over a variety of meteorological conditions.

The ionospheric D-region affects propagation of electromagnetic waves including ground-based signals and satellite signals during its intensive disturbances. Consequently, the modeling of electromagnetic propagation in the D-region is important in many technological domains. One of sources of uncertainty in the modeling of the disturbed D-region is the poor knowledge of its parameters in the quiet state at the considered location and time period. We present the Quiet Ionospheric D-Region (QIonDR) model based on data collected in the ionospheric D-region remote sensing by very low/low frequency (VLF/LF) signals and the Long-Wave Propagation Capability (LWPC) numerical model. The QIonDR model provides both Wait's parameters and the electron density in the D-region area of interest at a given daytime interval. The proposed model consists of two steps. In the first step, Wait's parameters are modeled during the quiet midday periods as a function of the daily sunspot number, related to the long-term variations during solar cycle, and the seasonal parameter, providing the seasonal variations. In the second step, the output of the first step is used to model Wait's parameters during the whole daytime. The proposed model is applied to VLF data acquired in Serbia and related to the DHO and ICV signals emitted in Germany and Italy, respectively. As a result, the proposed methodology provides a numerical tool to model the daytime Wait's parameters over the middle and low latitudes and an analytical expression valid over a part of Europe for midday parameters.

In this work, we study the impact of high-energy radiation induced by solar X-ray flares on the determination of the temporal change in precipitable water vapor (?PWV) as estimated using the synthetic aperture radar (SAR) meteorology technique. As recent research shows, this radiation can significantly affect the ionospheric D-region and induces errors in the estimation of the total electron content (TEC) by the applied models. Consequently, these errors are reflected in the determination of the phase delay and in many different types of measurements and models, including calculations of meteorological parameters based on SAR observations. The goal of this study is to quantify the impact of solar X-ray flares on the estimation of ?PWV and provide an estimate of errors induced if the vertical total electron content (VTEC) is obtained by single layer models (SLM) or multiple layer models (MLM) (these models do not include ionosphere properties below the altitude of 90 km as input parameters and cannot provide information about local disturbances in the D-region). The performed analysis is based on a known procedure for the determination of the D-region electron density (and, consequently, the vertical total electron content in the D-region (VTEC)) using ionospheric observations of very low frequency (VLF) radio waves. The main result indicates that if the D-region, perturbed by medium-sized and intense X-ray flares, is not modeled, errors occur in the determination of ?PWV. This study emphasizes the need for improved MLMs for the estimation of the TEC, including observational data at D-region altitudes during medium-sized and intense X-ray flare events.

The present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel-1 A and B products. Despite the limitations resulting from the 6-day return period of images produced by the 2-satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel-1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3-day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid-point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation.

In this paper we analyse temporal variations of the phase of a very low frequency (VLF) signal, used for the lower ionosphere monitoring, in periods around four earthquakes (EQs) with magnitude greater than 4. We provide two analyses in time and frequency domains. First, we analyse time evolution of the phase noise. And second, we examine variations of the frequency spectrum using Fast Fourier Transform (FFT) in order to detect hydrodynamic wave excitations and attenuations. This study follows a previous investigation which indicated the noise amplitude reduction, and excitations and attenuations of the hydrodynamic waves less than one hour before the considered EQ events as a new potential ionospheric precursors of earthquakes. We analyse the phase of the ICV VLF transmitter signal emitted in Italy recorded in Serbia in time periods around four earthquakes occurred on 3, 4 and 9 November 2010 which are the most intensive earthquakes analysed in the previous study. The obtained results indicate very similar changes in the noise of phase and amplitude, and show an agreement in recorded acoustic wave excitations. However, properties in the obtained wave attenuation characteristics are different for these two signal parameters.

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the k-means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

We analyse the lower ionosphere disturbances in the time period around the Mw 5.4 Kraljevo earthquake (EQ), which occurred on 3 November 2010 in Serbia. The results presented herein are based on analysis of the amplitudes of three VLF signals emitted in Italy, UK, and Germany and recorded in Serbia whose variations primarily result from changes in the electrical properties of the lower ionosphere at a distance more than 120 km from the epicentre of the EQ. The primary goals of this study are to reveal specific variations as possible EQ precursors as well as disturbances following the EQ event recorded by the observational equipment, and to investigate whether better time resolution data can affect the analysis of the lower ionosphere disturbances possibly connected to the EQ. We process two sets of data with sampling periods of 1 min and 0.1 s. As the first analysis indicates the absence of long-term disturbances, which can clearly be connected to the Kraljevo EQ, our attention is focused on the study of short-period noise amplitude and the excitation and attenuation of acoustic and gravity waves in the lower ionosphere. Processing of the amplitudes of three VLF signals during the nights of the four EQs with magnitude greater than 4 that occurred in Serbia, as well as EQs of all magnitudes during the three days of 3, 4, and 9 November, indicates that the detected ICV radio signal noise amplitude reduction starting before the Kaljevo EQ is also observed for 13 additional EQ events near the signal propagation path, and occurred during all three days (for all EQs with magnitude greater than 4 and several less intensive events). Excitation and attenuation of acoustic waves are also found for all these EQ events with a magnitude greater than 4.

This study describes a semi-empirical model developed to estimate volumetric soil moisture (theta v) in bare soils during the dry season (March-May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (sigma degrees dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients sigma VV0 and sigma VH0 were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0-10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March-May) of 2017 and 2018. A linear equation was proposed by combining sigma VV0 and sigma VH0 to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization (VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of sigma VV0 and sigma VH0. The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of sigma VV0 + sigma VH0 showed a significantly higher R-2 than the individual polarimetric channels.

This work presents a methodology to monitor the dynamic behaviour of tall metallic towers based on ground-based radar interferometry, and apply it to the case of telecommunication towers. Ground-based radar displacement measurements of metallic towers are acquired without installing any Corner Reflector (CR) on the structure. Each structural element of the tower is identified based on its range distance with respect to the radar. The interferometric processing of a time series of radar profiles is used to measure the vibration frequencies of each structural element and estimate the amplitude of its oscillation. A methodology is described to visualize the results and provide a useful tool for the real-time analysis of the dynamic behaviour of metallic towers.

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth's atmosphere's global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

This article presents a methodology for the monitoring of tall structures based on the joint use of a terrestrial laser scanner (TLS), configured in line scanner mode, and a ground-based real aperture radar (GB-RAR) interferometer. The methodology provides both natural frequencies and oscillation amplitudes of tall structures. Acquisitions of the surface of the tall structure are performed by the TLS with a high sampling rate: each line scan provides an instantaneous longitudinal section. By interpolating the points of each line, oscillation profiles are estimated with a much better precision than each single point. The amplitude and frequency of the main oscillation mode of the whole structure are derived from the TLS profiles. GB-RAR measurements are used to measure the vibration frequencies of higher oscillation modes which are not caught by the TLS due its lower precision in the measurement of displacements. In contrast, the high spatial resolution of TLS measurements provides an accurate description of oscillation amplitude along the tower, which cannot be caught by the GB-RAR, due to its poorer spatial resolution. TLS and GB-RAR acquisitions are simultaneous. The comparison with the analytical solution for oscillation modes demonstrates that the proposed methodology can provide useful information for structural health monitoring (SHM). The methodology does not require the use of targets on the structure and it can be applied during its normal use, even in presence of dynamic loads (wind, traffic vibrations, etc.). A test was carried out on a wind tower where the synergistic use of TLS and GB-RAR made it possible to fully describe the spectral properties of the tower and at the same time measure the amplitude of the first oscillation mode along the tower with a high spatial resolution.

Global mean sea level rise associated with global warming has a major impact on coastal areas and represents one of the significant natural hazards. The Asia-Pacific region, which has the highest concentration of human population in the world, represents one of the larger areas on Earth being threatened by the rise of sea level. Recent studies indicate a global sea level of 3.2 mm/yr as measured from 20 years of satellite altimetry. The combined effect of sea level rise and local land subsidence, can be overwhelming for coastal areas. The Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of TerraSAR-X images and estimate the land subsidence in the urban area of Singapore. Interferometric SAR (InSAR) measurements are merged to the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 sea-level rise scenarios to identify projected inundated areas and provide a map of flood vulnerability. Subsiding rates larger than 5 mm/year are found near the shore on the low flat land, associated to areas recently reclaimed or built. The projected flooded map of Singapore are provided for different sea-level rise scenarios. In this study, we show that local land subsidence can increase the flood vulnerability caused by sea level rise by 2100 projections. This can represent an increase of 25% in the flood area in the central area of Singapore for the RCP4.5 scenario.