Potential of an Automatic Grounding Zone Characterization using wrapped InSAR Phase

The work deals with the identification and the characteriza-tion of the grounding zone area using InSAR data. The ideais to point towards a methodology that minimizes the role ofthe operator and provides results with performance that canbe mathematically described using input parameters. The ap-proach uses the information of the interferometric phase gra-dient to follow the path of the grounding zone and fit themusing a physical model that describes the ice bending. Theapproach is tested on more than 300 km grounding zone com-paring also the results with existing products

Estimating Strain and Rotation tensors of glacier flow from wrapped SAR interferograms

This letter aims to discuss a general framework that allows the direct interpretation of the wrapped DInSAR phase in terms of surface strain S and rotation R components. The methodology is demonstrated showing the estimation of strain and rotation components of a glacier flow using three TerraSAR-X interferometric geometries (ascending right-looking, descending right-looking and descending left-looking. Finally since the left looking geometry can be difficult to obtain on a regular basis, the surface parallel flow assumption is extended to the phase gradients inversion in order to reduce the amount of necessary geometries from three to two

Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method

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

InSAR Performance for Large-Scale Deformation Measurement: Impact of Tropospheric Corrections and Validations

This paper deals with the analysis of InSAR performance for large-scale deformation measurement. The study evaluatesthe use of models, especially numerical weather prediction re-analysis, to mitigate disturbances in SAR interferograms. The impact of such corrections is evaluated and, using GNSS measurements, the predicted error bars are validated on a large Sentinel-1 dataset

Fading Signal: An Overlooked Error Source for Distributed Scatterer Interferometry

We reveal the presence of a peculiar physical signal which compromises the accuracy of Earth surface deformation estimates for distributed scatterers [1]. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. To indicate its nature, this physical effect is referred to as fading signal. Designing a simple approach in the evaluation of distributed scatterers, we reveal a prominent bias in the deformation velocity maps. The bias is the result of propagation of small phase error through the time series. We further discuss the role of the phase estimation algorithms in significant reduction of the bias and put forward the idea of a unified analysis-ready InSAR product for achieving high-precision deformation monitoring

Validation of the Tropospheric Corrected Interferograms and Analysis of the expected Performance in Deformation Rate Estimation

Previous studies have shown that a consistent reduction of tropospheric-related phase biases can be introduced mitigating the interferometric phase using numerical weather models. The work investigates the achieved accuracy of the tropospheric corrections and their effects on deformation rates estimation using GNSS data. The GNSS derived Zenith Path Delay is used to assess if the corrected interferogram reaches the expected numerical weather models accuracy. Then the GNSS derived deformation rates are compared with DInSAR results to validate their expected accuracy

Phase inconsistencies and water effects in SAR interferometric stacks

SAR Interferometry with stacks has already shown its potential in identifying permanent scatterers, in processing decorrelating targets, mitigating atmospheric delays, etc., but we believe that there is still potential for retrieving information on the scattering environment which has not been extensively studied yet. In particular interferometric stacks can reveal systematic phase inconsistencies which are not detectable in single interferograms, challenging any simple interpretation of the interferometric phase and associated coherence. The explanation of such inconsistencies requires more complex propagation models than the one based on a simple delay

InSAR Displacement Time Series Mining: A Machine Learning Approach

Interferometric Synthetic Aperture Radar (InSAR)-derivedsurface displacement time series enable a wide range of ap-plications from urban structural monitoring to geohazardassessment.With systematic data acquisitions becomingthe new norm for SAR missions, millions of time series arecontinuously generated. Machine Learning provides a frame-work for the efficient mining of such big data. Here, we focuson unsupervised mining of the data via clustering the similartemporal patterns and data-driven displacement signal re-construction from the InSAR time series. We propose a deepLong Short Term Memory (LSTM) autoencoder model whichcan exploit temporal relations in contrast to the commonlyused shallow learning methods, such as Uniform ManifoldApproximation and Projection (UMAP). We also modify theloss function to allow the quantification of uncertainties inthe time series data. The two approaches are applied to theLazufre Volcanic Complex located at the central volcaniczone of the Andes and thereby compared

Large Scale Interferometric Processing of Sentinel-1 Data over the Atacama Desert - a Contribution to the TecVolSA Project

The project TecVolSA (Tectonics and Volcanoes in South America) aims at developing an intelligent Earth Observation (EO) data processing system for monitoring the earthquake cycle and volcanic events in South America. The Remote Sensing Technology Institute of DLR participates to this project together with GFZ (German Research Centre for Geosciences). The project is partially financed by Helmholtz. So far we have processed about 40 Sentinel-1 slices covering the Atacama Desert with mixed Permanent Scatterer and Distributed Scatterer (PS/DS) techniques. The area is very dry and the spatio-temporal coverage is excellent. Tropospheric correction have been applied using ECMWF ERA5 data, hence improving the performance in observing both topography related and large scale deformation signals. The current results reveal, as expected, plenty of interesting signals to be interpreted (see attached figure for an overview of the velocity field). Preliminary GPS cross-validation, thanks to data freely available from the Geodetic Nevada Laboratory, confirm that the InSAR relative error in the estimated velocities is in the order of 1 mm/yr at large scale (>100 km) and confirms the large scale signal related to the subduction of the Nazca plate (see attached figure). More GNSS validation will be possible with additional GPS stations. The challenge of the project is the separation of different contributions to the InSAR measurements: apart from the tectonic effects, there are contributions coming from volcanic unrest, atmospheric delays, moisture effects, snow, flank instability (likely downhill creep or solifluction related to permafrost, see attached figure), salt lake growth, mining, and likely more. We are dealing with this complexity with a diversity of tools: physical modeling and statistical analysis, deep neural networks, and expert knowledge. GFZ contributes process knowledge, historic seismic data, in-situ motion measurements and observations and 4D geophysical modelling codes for producing a diverse database for the training of neural networks in order to autonomously discover significant events in noisy data. We tackle the problem as a semi-supervised multi-class classification approach where the labeling of the known deformation phenomena is provided by GFZ. Signals for which the source of deformation is unknown are identified and clustered automatically using advanced unsupervised machine-learning techniques. Therefore, we leverage from the advantages of both supervised and unsupervised learning and improve the accuracy for detection and classification of different deformation sources. The networks and AI-based methods are developed at DLR. This new approach (InSAR + Artificial Intelligence) should be able to process the massive data stream of the Copernicus Sentinel-1 SAR mission. South America was selected because manifold geophysical signals can be expected there in short time scales and plenty of in-situ data are available. This project will complement the current model-based geophysical research by a data-driven AI-based approach. Training and applying this intelligent system shall improve our understanding of geophysical processes related to natural and anthropogenic hazards. At a later stage the system shall be scalable to global processing capacity. Future developments on the InSAR processing will include ionospheric corrections based on split-spectrum and mosaicking of the velocity and displacement series. Some issues with the L1 processor are hindering the deployment of the split-spectrum technique. Stacks from the ascending geometry are already being processed and will help the geophysical interpretation