The differential ionospheric path delay is a major error source in L-band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical processes. In this paper, we proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split-spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. We tested the method using various Advanced Land Observing Satellite Phased-Array type L-band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.

Efficient estimation of the interferometric phase and complex correlation is fundamental for the full exploitation of interferometric synthetic aperture radar (InSAR) capabilities. Particularly, when combining interferometric measures arising both from distributed and concentrated targets, the interferometric phase has to be correctly extracted in order to preserve its physical meaning. Recently, an amplitude-based algorithm for the adaptive multilooking of InSAR stacks was proposed where it was shown that a comparison of the backscatter amplitude statistics is a suitable way to adaptively group and average the pixels in order to preserve the phase signatures of natural structures in the observed area. In this letter, different methods to compare amplitude statistics will be presented, compared through simulation and applied to real data. Based on these, recommendations are made concerning which method to use in practice.

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There is a need for scattering models that link quantitatively SAR interferometric observables to soil moisture. In this work we propose a model based on plane waves and the Born approximation, deriving first the vertical complex wavenumbers in the soil as a function of geometrical and dielectric properties and successively the complex interferometric coherences. It is observed that soil moisture behaves on the phase in a similar way as tomography does, breaking the phase consistency in triplets of interferograms. The proposed model is validated with L-band airborne SAR data; preliminary inversion results based on interferogram triplets and coherence magnitudes are presented.

This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.

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<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

Terrafirma is an ESA project and a service element in the framework of the Global Monitoring for Environment and Security (GMES) service element programme. Based on the Persistent Scatterer Interferometry (PSI), the project provides a Pan European ground motion hazard information service. The motion monitoring is supplied by commercial companies which act as Operational Service Providers (OSPs). A Product Validation Workgroup (PVW) has been formed for the validation of the motion data products. At the moment, four OSPs operate processing chains for the generation of the basic level 1 product. These take part in a special validation project which intends to demonstrate reliability and accuracy of the PSI motion monitoring. Two independent and complementary strategies for the validation are foreseen in the validation project in order to drive sustainability. On the one hand, the Product Validation utilizes available ground truth information for the validation and assesses the final geocoded motion data. On the other hand, the Process Validation is a new type of PSI validation and compares the intermediate data in slant range regarding a reference processing and consequently avoids the problems resulting from the geocoding. This newly developed validation approach and the results of the assessment are presented. Essentially, the Process Validation experimentally provides the actual estimation performance for a typical PSI processing based on ERS or Envisat/ASAR stacks. In principle, the lower bound for the deformation deviation an end user can expect ordering a test site processing from different OSPs is reported.