In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.

We investigate the influence of the perturbed (by a solar X-ray flare) ionospheric D-region on the global navigation satellite systems (GNSS) and synthetic aperture radar (SAR) signals. We calculate a signal delay in the D-region based on the low ionospheric monitoring by very-low-frequency (VLF) radio waves. The results show that the ionospheric delay in the perturbed D-region can be important and, therefore, should be taken into account in modeling the ionospheric influence on the GNSS and SAR signal propagation and in calculations relevant for space geodesy. This conclusion is significant because numerous existing models ignore the impact of this ionospheric part on the GNSS and SAR signals due to its small electron density which is true only in quiet conditions and can result in significant errors in space geodesy during intensive ionospheric disturbances.

Recently, structural monitoring by radar remote sensing has become more necessary for both economic and security reasons. Infrastructure monitoring with no incorporated deformation sensors (e.g., old generation water dams for which regulations did not impose monitoring capabilities) is usually performed by regular in situ topographic surveys. However, these surveys cannot be performed very often, and alternative methods are desirable. A feasible nonintrusive way to monitor such a structure is with interferometric synthetic aperture radar (SAR) data that can be acquired with monostatic/bistatic sensors.

We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.

Plain Language Summary Weather forecasts will never be perfect because our models are simplified representations of nature and our observations of the atmosphere are inaccurate. In this study we show, nevertheless, that it is possible to improve such forecasts by interpreting the atmospheric signals in spaceborne radar observations of the Earth surface, indicative of the distribution of water vapor. Better and more detailed maps of water vapor are found to lead to better forecasts not just of water vapor but also of precipitation. A two and a half years assessment covering a wide range of weather conditions in a very well monitored region near the Appalachian Mountains, USA, suggests that the proposed methodology has a significant impact in the quality of the forecasts and could easily be implemented. The present study assesses the added value of high-resolution maps of precipitable water vapor, computed from synthetic aperture radar interferograms , in short-range atmospheric predictability. A large set of images, in different weather conditions, produced by Sentinel-1A in a very well monitored region near the Appalachian Mountains, are assimilated by the Weather Research and Forecast (WRF) model. Results covering more than 2 years of operation indicate a consistent improvement of the water vapor predictability up to a range comparable with the transit time of the air mass in the synthetic aperture radar interferograms footprint, an overall improvement in the forecast of different precipitation events, and better representation of the spatial distribution of precipitation. This result highlights the significant potential for increasing short-range atmospheric predictability from improved high-resolution precipitable water vapor initial data, which can be obtained from new high-resolution all-weather microwave sensors.

A passive bistatic ground-based synthetic aperture radar (PB-GB-SAR) system without a dedicated transmitter has been developed by using commercial-off-the-shelf (COTS) hardware for local-area high-resolution imaging and displacement measurement purposes. Different from the frequency-modulated or frequency-stepped continuous wave signal commonly used by GB-SAR, the continuous digital TV signal broadcast by a geostationary satellite has been adopted by PB-GB-SAR. In order to increase the coherence between the reference and surveillance channels, frequency and phase synchronization of multiple low noise blocks (LNBs) has been conducted. Then, the back-projection algorithm (BPA) and the range migration algorithm (RMA) have been modified for PB-GB-SAR to get the focused SAR image. Field experiments have been carried out to validate the designed PB-GB-SAR system and the proposed methods. It has been found that different targets within 100 m (like the fence, light pole, tree, and car) can be imaged by the PB-GB-SAR system. With a metallic plate moved on a positioner, it has been observed that the displacement of the target can be estimated by PB-GB-SAR with submillimeter accuracy.

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.

In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ASWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ASWE, showing a reasonable match with a mean accuracy of about 6 mm.

This paper presents the results of an experiment aiming to measure the vibrational frequencies of the main structures of the medieval church of San Domenico (Matera, southern Italy) and relate them to the mechanical properties of geological stratigraphy and construction materials. Vibrational frequencies are measured by means of the ground-based radar inteferometry technique using a Ku-band radar. Time series of ground-based radar data are processed to measure displacements and vibration frequencies of the church structures. Data collected by a seismic triaxal station for the measurement of the H/V ratio are also processed to compare with radar-based frequencies measurements, providing information about the main vibration frequency ranges of the church structures and, more important, disentangle the site and structure vibration frequencies.

In this work we study the problem of mapping soil moisture by means of Synthetic Aperture Radar (SAR) images. A test site has been set in Companhia das Lezirias, close to Lisbon, Portugal. The main advantage of using SAR images is their capability to map soil moisture at a very high spatial resolution. This opens interesting perspectives for agricultural applications, where soil moisture can abruptly change across field boundaries depending on the agricultural practices. The study area is characterized by flat topography, large agricultural areas and sparse vegetation. Five sensors have been deployed in a test area to measure soil moisture with a sampling time of one hour for a period of seven months. In-situ measurements are compared with the results obtained by processing 33 C-band Sentinel-1 images using the SAR interferometry technique. The aim of the study is to analyze the relation between the interferometric phase and time varying soil moisture. The main advantage of SAR interferometry with respect to the use of radar cross-section is that the information about soil moisture can be recovered using a reduced number of in-situ measurements. In particular, we combine three interferograms obtained from three SAR images, acquired over the same area at different times, to derive maps of bi-coherence and phase triplet. This last quantity allows to disentangle the phase contribution due to soil moisture from those related to microwave propagation in atmosphere and terrain displacements. Results are compared to those obtained using the interferometric phase and coherence to emphasize the importance to split the effects due to propagation (e.g. atmosphere) from those related to volume scattering.

A Global Navigational Satellite System (GNSS) tomography system is implemented in the Lisbon area, Portugal, to estimate the water vapor dynamics at a local scale. A field experiment was carried out, in which a series of temporary GNSS stations were installed, increasing the network from 9 permanent stations to a total of 17 GNSS stations. A radiosonde campaign was also performed with high sampling launches, at 4-h intervals, for 1 week. A time series of hourly 3D wet refractivity solutions were obtained during the radiosonde campaign. Radiosonde and Atmospheric Infrared Sounder (AIRS) measurements were used to compute wet refractivity profiles to initialize and update the tomography solutions. The dependence of the GNSS tomography solution on the initial conditions obtained from both radiosonde and AIRS measurements, and their updating frequencies are studied. It is found that the GNSS tomography continuous measurement of the atmospheric refractivity provides solutions with an RMS mean of about 2 g/m(3).

To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.

In this work, we study the capability of the ground surface to generate Persistent Scatterers (PS) based on the lithology, slope and aspect angles. These properties affect the scattering behavior of the Synthetic Aperture Radar (SAR) signal, the interferometric phase stability and, as a consequence, the PS generation. Two-time series of interferometric SAR data acquired by two different SAR sensors in the C-band are processed to generate independent PS datasets. The region north of Lisbon, Portugal, characterized by sparse vegetation and lithology diversity, is chosen as study area. The PS frequency distribution is obtained in terms of lithology, slope and aspect angles. This relationship could be useful to estimate the expected PS density in landslide-prone areas, being lithology, slope and aspect angles important landslide predisposing factors.

In this paper we summarize the results of an experiment aiming to compare soil moisture estimates obtained by Sentinel-l interferometric data with in-situ measurements. The study area, located close to Lisbon in Companhia das Lezirias, Portugal is characterized by a flat topography, large agricultural areas and sparse vegetation. In a test site, four soil moisture sensors were deployed and soil moisture was measured (at a depth of 5 cm) for a period of 7 months in an hourly basis. For the same interval of time and with a temporal resolution of 6 days C-band Sentinel-l SAR images were interferometrically processed and coherence, phase and phase triplet images were derived. The in-situ soil moisture measurements have been used to predict the analytical interferometric phases, coherences and phase triplets and compared with the measured interferometric phases in both VV and VH polarimetric channels. As a further analysis, a regression analysis of in-situ soil moisture measurement and Sentinel-l backscattering images has been carried out.

We study the imaging and interferometric performances of a MIMO radar on board of an airship as alternative to airborne and spaceborne SAR remote sensing techniques. Four different MIMO radar arrays are designed working in L, C, X and Ku frequency bands. A frequency bandwidth of 250 MHz has been considered for the MIMO radars. The spatial resolution is 0.6 m in range and 0.3 degree in azimuth. The imaging and interferometric performances of the MIMO radar are analyzed in terms of the airship stability. A synthetic raw data set is generated assuming a target deployed on a flat area at different azimuth angle. This MIMO imaging solution is intended for continuous imaging over an area of interest.

In this work, we study the problem of assimilating high resolution Precipitable Water Vapor (PWV) maps using the Weather Research and Forecast 3D Variational Data assimilation system (WRF-3DVar). The PWV maps are obtained using the Sentinel-1 Synthetic Aperture Radar (SAR) images and the SAR interferometry (InSAR) technique. The influence of the high resolution PWV data on the initial condition of WRF and during the next 12 hours is studied. We demonstrate that the assimilation of InSAR PWV maps increases both the water vapor concentration and temperature over areas affected by extreme weather events so correctly generating localized convection cells. The PWV forecast, after the assimilation of InSAR maps, are compared with the PWV estimates provided by a dense GNSS network. The precipitation pattern and amount are compared to meteorological radar measurements. The case study of the extreme weather event that affected the city of Adra, Spain, on 6 th September 2015, is used to demonstrate how the assimilation of high resolution PWV maps.

A Global Navigational Satellite System (GNSS) tomography experiment has been performed for 1 week, introducing Atmospheric Infrared Sounder (AIRS) remote sensing data to initiate and update a 3D wet refractivity hourly solution series of the troposphere. Some qualitative and quantitate studies have been performed, taking advantage of a local radiosonde campaign with a 4-hour sampling data. 3D wet refractivity maps with an accuracy close to 2 g/m 3 are obtained.

In this work we present a methodology to estimate the 3D distribution of water vapor in atmosphere based on the use of SAR interferometry (InSAR) and Sentinel-l data. Maps of propagation delay in atmosphere are assimilated in a high resolution Numerical Weather Model to enhance the forecast of atmosphere parameters. These are used to compute the atmosphere refractivity. Furthermore, 3D maps of hydrometers in atmosphere are derived after the assimilation of InSAR data. Both refractivity and hydrometeors maps are used to map 3D Water vapor patterns in atmosphere. Examples of InSAR signatures of water vapor in atmosphere are shown. We show how the 3D maps liquid refractivity and hydrometeors can be a useful tool to map moisture in atmosphere in case of convective phenomena in atmosphere.

In this paper, we describe a new methodology for the nondestructive measurement of absolute displacements of a pier during a bollard pull trial by ground-based synthetic aperture radar (GBSAR) interferometry. This technique measures displacement patterns with a submillimeter precision in any weather conditions, operating at a distance up to 4 km from the target area. Bollard pull trials are performed to study the deformation response of a pier when a static pull is applied by a tug to a bollard on the pier edge. The precise measurement of the pulling force and the corresponding displacement pattern of the pier around the bollard is a useful piece of information for the back-analysis studies during the assessment phases of recently built piers. An experiment is carried out to measure pier's displacements at 12 co-located corner reflectors (CRs) and surveying prisms, by SAR interferometry and topographic techniques during a bollard pull trial. The GBSAR results have been validated at the CR locations using the displacement measurements provided by topographic survey. The pulling force applied to the bollard is measured by a load cell specifically customized to precisely measure the pulling force during the trial. Results demonstrate that GBSAR systems can provide a useful tool for the assessment of harbor infrastructures, such as piers, measuring absolute displacements with near-real time capabilities.

The cooperation of scientists in Big-Sky-Earth COST Action creates an emergent group of researchers with relation to meteor science. Selected cases of development of novel approaches and techniques for meteor simulation and observation are presented.