In this work, we investigate the assimilation of InSAR-derived water vapor into the Weather Research and Forecast Data Assimilation model (WRFDA) and evaluate the impact within the InSAR footprint and also over time and space. We ingested three sequential InSAR-derived water vapor maps, estimated from Sentinel-1 images acquired over southern Portugal and Spain during Storm Barbara in October 2020, and assessed the water vapor anomaly fields thousands of kilometers downstream and their impact on rainfall. We show that the assimilation of InSAR-derived water vapor into NWP models has the potential to enhance the understanding of mesoscale meteorological events and improve the model's capability to simulate extreme weather events. This is particularly relevant for flood forecasting and timely weather predictions.

This paper aims to contribute to clarifying the relationship between phase decorrelation and water content in vegetation and to study the impact of the temporal variability of vegetation water content on InSAR estimates of terrain deformation. We combine three interferograms obtained from three SAR images, of the same area acquired at different times, to derive maps of decorrelation phases. It was found that the magnitude and temporal variability of the decorrelation phase is low in bare soil and high in agricultural and forest areas. Furthermore, it was found that the temporal variation of vegetation water content is related to the phase bias of the cumulative displacement computed with short-interval interferograms.

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.

InSAR maps of water vapor can improve the accuracy of Numerical Weather Prediction (NWP) models, especially under extreme precipitation events. However, integrating InSAR data into NWP models is challenging due to the differential nature and absolute phase ambiguity introduced by the unwrapping algorithm. To address this, we propose a new approach that uses ERA5 reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the absolute epoch from InSAR data. The proposed method is based on constrained least-squares estimation, and ERA5 data are used as a first guess to untangle distinct atmospheric components without applying additional assumptions or external measurements. This approach will allow us to obtain absolute maps over regions without external in situ measurements (e.g., GNSS or radiosondes). The results are validated using a large GNSS dataset, improving about 29% over the field of the original ERA5.

The availability of C-band Synthetic Aperture Radar (SAR) images by the Sentinel-1 mission every six days boosts the concept of SAR interferometry (InSAR) meteorology. The general idea involves producing integrated water vapor maps using InSAR and assimilating them into high-resolution Numerical Weather Prediction (NWP) models to improve precipitation prediction. The temporal resolution is the disadvantage of the Sentinel-1 mission compared to other techniques (e.g., GNSS). Therefore, this study focuses on the minimum inter-station distance below which a dense GNSS network can catch atmospheric information similar to InSAR maps. The study uses an InSAR water vapor map to generate synthetic GNSS measurements of PWV. Results show that the WRF model's correct prediction of observed rainfall is more frequent when the inter-distance between GNSS stations becomes smaller than 20 km. A numerical instability of the WRF results is observed at shorter distances, indicating the need to handle the spatial correlation of assimilated measurements of PWV.

Results of an experiment aiming to estimate soil moisture (SM) using indexes based on hyperspectral and multispectral data, and decorrelation phase from SAR interferometry are presented. SM estimates are compared with in-situ measurements of SM. The issue of spaceborne and in-situ datasets not overlapping in time is studied. Results are obtained using PRISMA, Sentinel-1 and Sentinel-2 data.

Soil moisture is an important component in investigations of land-surface climate and hydrology. Recently several methodologies and techniques have been proposed, allowing soil moisture retrieving from SAR remote sensing techniques at large scales. In this work we investigate the relation between the closure phases and the time varying soil moisture. We combine three interferograms obtained from three SAR images of the same area acquired at different times, to derive maps of bi -coherence and phase triplet. The results show that there is a linear correlation between the modelled phases derived from soil moisture measurements and the closure phases. The correlation coefficient was R2=0.76 and R2=0.86 for the descending and ascending passes, respectively. However, a scale effect of the closure phases was found when compared with the derived model phases. The scale is about 10% for both passes, meaning that the estimated closure phases underestimate the soil moisture changes.

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 within situdata 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/m3was found over a variety of meteorological conditions.

The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.

This work presents a study on the enhancement of Numerical Weather Prediction (NWP) model simulations when assimilating SAR interferometry (InSAR) Precipitable Waver Vapor (PWV). A 3dVar assimilation scheme is used to assimilate InSAR data. The rotationally invariant spectrum of PWV maps obtained by GNSS, InSAR, and the NWP model is computed before and after the 3dVar assimilation. The average distance of GNSS stations in this region is about 100 km. The spatial resolution of NWP model simulations is 3 km. The spectra analysis shows that there are two cut frequencies, at 10−5m−1÷ 2.10−5m−1and at 2.10−4m−1÷ 3.10−4m−1. The lower and higher cut frequencies seem to be related to the average distance of GNSS stations and the spatial resolution of NWP simulations.

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.

Synthetic Aperture Radar (SAR) interferometry is currently providing displacement measurements over large areas and recently also maps of water vapor in atmosphere. However, the interferometric phase can also capture tiny contributions due to soil moisture. In this work the results obtained in a 1-year experiment with in-situ measurements of soil moisture in agricultural area, in the flat land around the Tagus river, Lisbon, Portugal, are presented. We analyze two interferometric models for the estimation of soil moisture from SAR images. Sentinel-1 SAR images in the C-band are used due to their regular acquisition plan and the short revisiting time. We present examples of soil maps from Sentinel-1 data, characterized by a high spatial resolution and short updating time and comments on their use in precision farming.

In this work, we present a methodology to estimate 3D moisture structures in atmosphere based on the use of SAR interferometry (InSAR), and a Numerical Weather Prediction model (NWP). Maps of propagation delay in atmosphere with sub-kilometer spatial resolution are obtained by assimilating high resolution Precipitable Water Vapor (PWV) maps into the Weather Research and Forecast (WRF) model using the WRF Data Assimilation (WRFDA) System. The PWV maps are obtained by interferometrically processing of Sentinel-1 images. The comparison of WRF modeling of atmosphere parameters, before and after the assimilation is used to study the response of the model to the InSAR information and assess the physical consistency of atmosphere parameters modeled by the system. The visualization of moisture structure in atmosphere is carried out in terms of the 3D spatial distribution of atmosphere refractivity, hydrometeors, thermodynamics quantities and wind patterns. The proposed procedure is applied to the visualization of Atmospheric Gravity Waves (AGW) and Convective Systems (CS), two examples of moisture structures in atmosphere.

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/m3are obtained.

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.

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 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 6thSeptember 2015, is used to demonstrate how the assimilation of high resolution PWV maps.

Horticulture crops play an important commercial and economic role, providing employment and food security. A sustainable horticulture production requires updated and accurate statistics in terms of area and production. High temporal resolution remote sensing can be used to identify horticulture crops, especially vegetables that have shorter production cycles. Normalized Difference Vegetation Index (NDVI) bands generated from Sentinel-2A data are used to define the growth cycle of different vegetable types. NDVI time series allow the identification of several parameters, such as planting and maturation dates and crop cycle duration, that enable the characterization of each crop. A curve-matching algorithm, based on a set of NDVI curve parameters, were used to identify horticulture parcels. Two approaches were considered, one considering the total overlapping area and other considering a minimum of 1 pixel of overlap with the ancillary parcels delimitation. Results show that the latter approach allows the identification of possible horticulture crops with an accuracy higher than 80% while the former returns a lower accuracy of around 50%.

Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density.

A new methodology for the mapping of intertidal terrain morphology is presented. It is based on the use of synthetic aperture radar (SAR) images and the temporal correlation between the SAR backscatter intensity and the water level on the intertidal zone. The proposed methodology does not require manual editing, providing a set of geolocated pixels that can be used to generate a digital elevation model of the intertidal zone. The methodology is validated using TerraSAR-X SAR images acquired over Tagus estuary. This methodology can be useful for the regular updating of intertidal bathymetric models useful for both flood hazard mitigation and morphodynamics modeling.

This paper presents a methodology to generate maps of atmosphere's precipitable water vapor (PWV) over large areas with a length of hundreds of kilometers and a width of about 250 km, based on the use of interferometric Sentinel-1A/B C-band synthetic aperture radar (SAR) data with a high spatial resolution of 5 × 20 m2and the revisiting time of six days. An algorithm to calibrate and merge PWV maps from different swaths of Sentinel-1 acquired along the same track, using global navigation satellite system (GNSS) measurements, is described. The proposed methodology is tested on Sentinel-1A SAR images acquired over the Iberian Peninsula, along both descending and ascending tracks. The assessment with an independent set of GNSS measurements shows a mean difference of a fraction of millimeter and a dispersion lower than 2 mm. Both the use of Sentinel-1A/B SAR images and the proposed methodology open new perspectives on the application of SAR meteorology for the high-resolution mapping of PWV over large region-spanning areas and the assimilation of interferometric SAR data into numerical weather models.

The integration of interferometric synthetic aperture radar (InSAR) and GPS tomography techniques for the estimation of the 3-D distribution of atmosphere refractivity is discussed. A methodology to use the maps of the temporal changes of precipitable water vapor (PWV) provided by InSAR as a further constraint in the GPS tomography is described. The aim of the methodology is to increase the accuracy of the GPS tomography reconstruction of the atmosphere's refractivity. The results, which are obtained with SAR and GPS data acquired over the Lisbon area, Portugal, are presented and assessed. It has been found that the reconstruction of the atmospheric refractivity is closer to the real atmospheric state with a mitigation of the smoothing effects due to the usual geometrical constraints of the GPS tomography.

This paper studies the problem of the assimilation of precipitable water vapor (PWV), estimated by synthetic aperture radar interferometry, using the Weather Research and Forecast Data Assimilation model 3-D variational data assimilation system. The experiment is designed to assess the impact of the PWV assimilation on the hydrometers and the rainfall predictions during 12 h after the assimilation time. A methodology to obtain calibrated maps of PWV and estimated their precision is also presented. The forecasts are compared with GPS estimates of PWV and with rainfall observations from a meteorological radar. Results show that after data assimilation, there is a correction of the bias in the PWV prediction and an improvement in the prediction of the weak to moderate rainfall up to 9 h after the assimilation time.

GPS tomography provides a unique opportunity to sense the 3D state of the atmosphere. However, the setting of the model grid can affect the water vapor solution since GPS data can be insufficient to cover all the domain, leading to an ill-posed conditioning, usually solved by constraints. In this work we present the result of a simulation analysis to study the impact of Galileo data on the reconstruction of 3D atmospheric water vapor. GPS tomography results are compared with those provided by merging GPS and Galileo data in order to ascertain the enhancement of the 3D water vapor refractivity reconstruction.

Microwave sensing of the atmosphere with GPS data, particularly using GPS tomography, provides a unique opportunity to measure the 3D state of the atmospheric water vapor, since it acquires data in a high temporal sampling. However, this technique requires a full domain coverage which is not fulfilled by GPS data, leading to an ill-posed conditioning only solved by constraints. SAR interferometry can be used to get integrated water vapor maps with high spatial resolution, but with a temporal frequency depending on the SAR acquisitions. In this work we describe a methodology to include integrated water vapor maps provided by SAR interferometry into the GPS tomography processing scheme. The differential water vapor spatial distribution occurred between the acquisition time of master and slave images is used to constrain the GPS tomography solution. Results are obtained over Lisbon area using GPS and ENVISAT-ASAR data and validated with regional radiosonde data.

In this study we compare the results obtained using three different atmospheric datasets for the mitigation of atmospheric effects in TerraSAR-X imagery. The used datasets are: ERA-interim re-analysis model, MODIS sensor precipitation water vapour data and GPS derived precipitable water vapour (PWV). The PWV maps were converted to atmospheric path delay and projected into the SAR interferograms geometry. Subsequently the PWV contribution was removed from the interferograms. The Persistent Scatterers technique was applied to the atmospherically corrected interferograms and the obtained displacement rate compared with GPS surface displacement. It was observed that the mitigation of atmospheric effects influences the estimated displacement rate.

In this paper, the uncertainty of atmospheric delay maps obtained by a numerical weather model (NWM) is assessed. These models can simulate 3-D fields of meteorological parameters at coarse scales. A few approaches, which are mainly based on zenith wet delay estimates derived by NWM data, have been recently developed to mitigate atmospheric propagation artifacts in synthetic aperture radar interferometry applications. However, an assessment of these estimates is still missing. We present a methodology based on radiosonde data to assess the uncertainty of the meteorological parameters derived from an NWM and the dry and wet delays calculated using these parameters. The methodology is applied to the data generated by the Weather Research and Forecasting model that currently is the model with the higher accuracy relative to its predecessors.

Recently, synthetic aperture radar interferometry (InSAR) has been recognized as a promising tool to generate high-resolution maps of atmospherical precipitable water vapor temporal changes (ΔPWV) from the propagation delay of radar signal in atmosphere. The relationship between ΔPWV and propagation delay mainly depends on the vertical profiles of temperature and water vapor pressure. In this letter, we present a methodology to study the spatial and temporal variations of the temperature's vertical profile and generate more accurate high-resolution ΔPWV maps by means of InSAR.

In this paper, we present the results of an experiment aiming to compare measurements of atmospheric delay by synthetic aperture radar (SAR) interferometry and GPS techniques to estimates by numerical weather prediction. Maps of the differential atmospheric delay are generated by processing a set of interferometric SAR images acquired by the ENVISAT-ASAR mission over the Lisbon region from April to November 2009. GPS measurements of the wet zenith delay are carried out over the same area, covering the time interval between the first and the last SAR acquisition. The Weather Research and Forecasting (WRF) model is used to model the atmospheric delay over the study area at about the same time of SAR acquisitions. The analysis of results gives hints to devise mitigation approaches of atmospheric artifacts in SAR interferometry applications.

In this work we investigate the use of Synthetic Aperture Radar (SAR) images acquired by the TerraSAR-X satellite operating a very high resolution X-band SAR sensor to generate maps of temporal and spatial variations of the water vapour distribution with a horizontal resolution of 27×27 m over the Lisbon region, Portugal. We present the result of a time series of delay maps obtained by processing a set of interferometric SAR images acquired by the TerraSAR-X mission. The maps are calibrated by means of GPS estimations of the delay over the same area and covering the time interval between the first and the last SAR acquisition. The availability of maps with a high spatial resolution could increase the quality of quantitative precipitation forecasting and open interesting perspectives for nowcasting applications.

Tidal flats are considered an invaluable natural resource. Generally, they show dynamic morphologic changes that arise from high tidal energy and sediment transportation. The study of sediment budget processes is important in many ecological systems. The sediment budget can be estimated only if maps of morphological changes are available. Remote sensing, combined with in situ surveying, is an effective tool for monitoring tidal flats. The aim of this work is to precisely map tidal flats changes using multi-temporal Synthetic Aperture Radar (SAR) images acquired by the TerraSAR-X sensor. For that purpose, is proposed the application of K-Means clustering (in a gray level mode) to both original and preprocessed SAR amplitude data sets, followed by a morphological sequence task to generate optimized datasets, which will be compared for change detection between tides. Results will show the segmentation of intertidal regions from flooding tide's comparison.

A method to derive accurate spatially dense maps of 3-D terrain displacement velocity is presented. It is based on the merging of terrain displacement velocities estimated by time series of interferometric synthetic aperture radar (InSAR) data acquired along ascending and descending orbits and repeated GPS measurements. The method uses selected persistent scatterers (PSs) and GPS measurements of the horizontal velocity. An important step of the proposed method is the mitigation of the impact of atmospheric phase delay in InSAR data. It is shown that accurate vertical velocities at PS locations can be retrieved if smooth horizontal velocity variations can be assumed. Furthermore, the mitigation of atmospheric effects reduces the spatial dispersion of vertical velocity estimates resulting in a more spatially regular 3-D velocity map. The proposed methodology is applied to the case study of Azores islands characterized by important tectonic phenomena.

A method that is used to generate synthetic interferograms of the atmospheric phase delay temporal changes is presented. The Weather Research and Forecasting Model is used to forecast the spatial distribution of the main atmospheric parameters at the acquisition times of synthetic aperture radar (SAR) images. The method is applied to mitigate atmospheric artifacts in SAR interferograms. The Lisbon Region and the Pico and Faial Islands in the Azores archipelago are chosen as case studies. They are characterized by a different temporal behavior of atmospheric phase delay properties. Results are assessed by means of a statistical analysis.