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The atmosphere affects the propagation of radar signals by provoking unwanted signal phase changes. In interferometric applications, such as coherent change detection and displacement measurements, this effect may significantly degrade the system performances. Moreover, atmosphere-induced phase changes are both time and space variants, and therefore, they are not easy to be removed. This article proposes a novel method to remove atmospheric effects by using a parametric model of the refractive index, which is derived as an extension of the International Telecommunication Union—Radiocommunication model. The proposed algorithm has been tested on real data acquired by using a ground-based synthetic aperture radar system in conjunction with data collected by a weather station. Data have been acquired continuously for three consecutive days, approximatively every 5 min. Results have shown how the proposed method can effectively remove atmospheric effects and restore the signal phase.

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).

In this paper, the ground stability of the city of Constanta and surrounding areas (in Romania) is analyzed using the persistent scatterer interferometry technique, applied to Sentinel-l SAR data spanning an interval of two years. Two deformation maps are computed: one where the atmospheric phase screen is estimated and removed using ERA-Interim data and spatial filtering, and another using only spatial filtering.

Differential Interferometry SAR (DInSAR) is an advanced technique to retrieve ground deformation in the geoscience community. Atmospheric Phase Screen (APS) is one of the largest challenges limiting the application of DInSAR especially in mountainous areas. In this paper we propose an approach based on an empirical linear model for compensating stratified atmospheric phase delay in differential interferograms. Compared with conventional empirical linear model, the influence of turbulent APS is taken into consideration in our work. With this, the modelled stratified APS is more accurate. We test our algorithm using Envisat dataset and Sentinel dataset over Tenerife island (Spain). The performance of the approach is compared with Global Atmospheric Models (GAMs).

Ground-based synthetic aperture radar (GBSAR) is a powerful tool used in monitoring structures, such as bridges and dams. However, despite the extremely short range of GBSAR interferometry, the atmosphere effects cannot be neglected. The permanent scatterer technique is an effective operational tool that utilizes a long series of SAR data and detects information with high accuracy. An algorithm based on the permanent scatterer technique is developed in accordance with the phase model used in GBSAR interferometry. In this study, atmospheric correction is carried out on a real campaign (Geheyan Dam, China). The atmosphere effects created using this method, which utilizes SAR data, can be reduced effectively compared to when plumb line data are used.

The accuracy of InSAR DEMs is affected by the temporal decorrelation of SAR images which is due to atmosphere, land use/cover, soil moisture, and roughness changes. Elimination of the temporal decorrelation of the master and slave image improves the DEMs accuracy. In this study, the Independent Component Analysis was applied before interferometric process. It was observed that using three ICA entries, ICA independent sources can be interpreted as background and changed images. ICA when performed on the master and slave images using the same couple of additional images produces two background images which enable the production of high quality DEMs. However, limitations exist in the proposed approach.

When studying geophysical processes through Differential SAR Interferometry (DInSAR), it is often necessary to estimate and subtract signals due to atmospheric inhomogeneities. To this end, stochastic models are often used to describe atmospheric phase delays in DInSAR data. As a first approximation, these can be modelled as isotropic, though this is a simplification, because SAR interferograms often exhibit anisotropic atmospheric signals. In view of this, it is increasingly advocated the use of anisotropic models for atmospheric phase estimation. However, anisotropic models lead to increased computational complexity in estimating the correlation function parameters with respect to the isotropic case. Moreover, performances can degrade when use is made of interferograms with only a few sparse points usable for computations, such as in the case of persistent scatterers interferometry (PSI) applications, especially when this estimation has to be done in an automated way on several tens of interferograms. In the present work we critically analyse the main aspects connected with the use of anisotropic models for DInSAR atmospheric delays, and we evaluate the advantage given by anisotropic modeling of atmospheric phase in the case of sparse grids of points. Through analysis of APS simulated data, we observe that a slight increase in the performances of reconstruction approaches can be obtained when sufficient sampling densities are available; based on these results, some recommendations for operational atmospheric phase estimation procedures are proposed.

The paper provides a theoretical model for the Geosynchronous SAR focused data, including clutter and atmosphere decorrelation effects. The focused data model is exploited to generate realistic GEOSAR demonstrative products, on the basis of the preliminary design of a regional GEOSAR system. The provided demo products are useful for a preliminary evaluation of the expected system performances.

The paper evaluates the effects of Atmospheric Phase Screen on focused Geosynchronous SAR data. We derive theoretical models for the resolution, the interferometric de-correlation and the induced clutter in presence of an APS, whose joint time and space power spectrum is modeled as an extension of the Kolmogorov power law. Finally we provide some statistics gathered from a GB RADAR experiment.

We propose an efficient method for the azimuth compression and Atmospheric Phase Screen estimation of Geosynchronous SAR data. The method is based on the iterative processing of sub-apertures of increasing size, allowing to gradually refine the quality of the focused data and of the estimated APS. The whole processing can be easily parallelized. Results over simulated data are shown. © 2013 IEEE.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as tectonic movements, landslide monitoring and digital elevation model extraction. One of its major limitation is the atmospheric effect, and in particular the high water vapour spatial and temporal variability which introduces an unknown delay in the signal propagation. This paper describes the general approach and some results achieved in the framework of an ESA funded project devoted to the mapping of the water vapour with the aim to mitigate its effect in InSAR applications. Ground based (microwave radiometers, radiosoundings, GPS) and spaceborne observations (AMSR-E, MERIS, MODIS) of columnar water vapour were compared with Numerical Weather Prediction model runs in Central Italy during a 15-day experiment. A dense network of GPS receivers was deployed close to Como, in Northern Italy, to complement the operational network in order to derive Zenith Total Delay as well as Slant Delay which can support InSAR processing. A comparison with Atmospheric Phase Screens (APS) derived from a sequence of Envisat multi pass interferometric acquisitions processed using the Permanent Scatters technique on the two test sites has been also performed. The acquired experimental data and their comparison give a valuable idea of what can be done to gather information on water vapour, which, besides InSAR applications, plays a fundamental role in weather prediction and radio propagation studies. The work has been carried out in the framework of an ESA funded project, named “Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects” (METAWAVE). This paper presents the general approach an the various methodologies exploited in the project, together with the overall intercomparison of the results. In deep details on the comparison with the InSAR APS maps derived by the PS technique, as well as on GPS receiver processing and water vapour tomography are reported in two companion papers.

The PSInSAR technique, invented by Ferretti et. al. [1], [2], [3] ten years ago, meanwhile has proven it's capability for very precise measurement of surface deformations. To achieve this, the influence of the atmospheric phase screen (APS) has to be removed. We investigated the APS for two series of TerraSAR-X high resolution spotlight data of a scene in Bavaria. Our approach was to consider the APS as composed of a phase ramp, a part stratified with height and a turbulent component. We estimated the turbulent component via kriging. The variograms show for short distances a regime which is not visible for lower resolutions. In this paper we discuss the choice of appropriate variogram models with respect to our data.

It is well know that atmospheric phase screen(APS) effect in Small Baseline Subset (SBAS) D-InSAR processing to obtain ground deformation in long time series leads to additional interferometry phase and the unreliable result. To solve the problem above, MODIS product provide a mean to stimulate atmospheric field of SAR data for reducing the APS effect in D-InSAR processing. In this paper, 12 ENVISAT ASAR images are used to process SBAS method and 6 MODIS MOD05_L2 products are used to process atmosphere correction. A case study in the Jiaxing-Huzhou plain in Zhejiang province, China shows successfully stimulate the atmospheric field of SAR data and analysis its effect on SBAS application.

Motivated by prospective synthetic aperture radar (SAR) satellite missions, this paper addresses the problem of differential SAR tomography (D-TomoSAR) in urban areas using spaceborne bistatic or pursuit monostatic acquisitions. A bistatic or pursuit monostatic interferogram is not subject to significant temporal decorrelation or atmospheric phase screen and, therefore, ideal for elevation reconstruction. We propose a framework that incorporates this reconstructed elevation as deterministic prior to deformation estimation, which uses conventional repeat-pass interferograms generated from bistatic or pursuit monostatic pairs. By means of theoretical and empirical analyses, we show that this framework is, in the pursuit monostatic case, both statistically and computationally more efficient than the standard D-TomoSAR. In the bistatic case, its theoretical bound is no worse by a factor of 2. We also show that reasonable results can be obtained by using merely six TerraSAR-X add-on for digital elevation measurements (TanDEM-X) pursuit monostatic pairs, if additional spatial prior is introduced. The proposed framework can be easily extended for multistatic configurations or external sources of scatterer’s elevation.