We document one of several methodologies used to validate the NASA-ISRO Synthetic Aperture Radar (NISAR) mission requirements for solid earth deformation. NISAR’s deformation requirements cover steady-state, coseismic, and transient deformation processes and were designed to confirm that the mission is able to meet its solid earth science goals. We use independent observations of earth surface deformation from continuous Global Navigation Satellite System (GNSS) stations as ground truth for NISAR-observed deformation, and we provide a statistical framework to assess the quality of the associated NISAR data products. Our validation workflows have been developed as Jupyter Notebooks and are publicly available via GitHub/GitLab.

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

Launching in early 2024, the NASA-ISRO SAR (NISAR) mission is upon us and will bring an unprecedented amount of SAR data to the international SAR science community. To handle its 50PB of SAR data per year, NISAR uses novel approaches to data processing, management, and distribution. NISAR also offers a unique product portfolio that is adding several analysis ready data products to the typical SAR fare.This paper summarizes innovative approaches developed by the Alaska Satellite Facility and the NASA Jet Propulsion Laboratory to make NISAR’s massive data set accessible to the community. We summarize developed concepts for data discovery and distribution, and highlight tools and services that enable working with SAR data directly at the archive. We close with a range of education and training efforts developed across the SAR community that will help familiarize users with NISAR processing flows.

Satellite remote sensing time series have frequently been leveraged to track crop phenology changes throughout the growing season worldwide. These time series, primarily derived from optical sensors, can provide insights on changes that occur throughout the growing season compared to previous years. Additionally, time series can help monitor crop yields and overall production and can provide information about what is planted in a specific field. Optical remote sensors rely upon atmospheric and sky conditions, often causing gaps in the time series when images cannot be used because of cloud cover. The increasing availability of observations from synthetic aperture radar (SAR) allows for worldwide and repeat year-round collections. This study uses acquisitions from several different SAR missions (2018-2022) to map cropland extent using the coefficient of variation (CV) method in agricultural regions around the world, focusing predominantly on major global producers of corn, wheat, and rice.

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility – all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid Image Analysis (ARIA) project’s Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

The effect of small-scale ionospheric TEC variations (SSTV) on InSAR is assessed using ground GNSS data. The spectrum of standard deviation of differential TEC (SDT) is derived using GNSS data to characterize SSTV at spatial scales between 1 km and 50 km. Validation of this technique is conducted by comparing the GNSS SDT measurements with ALOS PalSAR data over Chili. The comparison shows consistent magnitude and variation trend between the GNSS and SAR data. The effects of SSTV on NISAR and the future Surface Deformation Change (SDC) mission are assessed using global SDT measurements during seven years from 2013 to 2019. The period covers high, medium, and low solar EUV radiation activities that affect TEC values and variations. Our analysis indicates that the effect of SSTV is substantial and can be on the order of 2 cm at high latitudes and 0.4 cm at middle and low latitudes.

Now, more than ever, there is a need for higher-quality products for remote-sensing end users. Following recent advancements in synthetic aperture radar (SAR) processing algorithms, we provide a full assessment of the radiometric dependence of geocoded C-band SAR backscatter processed with radiometric terrain correction (RTC) over differing target types. In particular, we compare the flatness of RTC-normalized backscatter coefficient gamma-naught with respect to local incidence angle over 46 Sentinel-1 (S1) datasets representing about 20 land classes in the Copernicus Global Land Service (CGLS) Land Cover 100m classification using the Observational Products for End-Users from Remote Sensing (OPERA) RTC-S1 product workflow and the ISCE3 framework. We also calculate the mean and median radar backscatter over areas of foreslope and backslope. Our results suggest that the dependence of gamma-naught on the local topography depends strongly on the land type and only exhibits near-constant behavior in certain forest land types. Evidence also suggests that as we move further away from densely tree-covered areas, the dependence of gamma-naught on the local incidence angle becomes stronger.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory (JPL) will provide a near-global Radiometric Terrain Corrected synthetic aperture radar (SAR) backscatter from Sentinel-1 (RTC-S1) product. The OPERA RTC-S1 product will deliver map-projected burst-based radar images with a geographic scope that includes all land masses excluding Antarctica, and with temporal sampling coincident with the availability of Sentinel-1 interferometric wide (IW) single-look complex (SLC) data. This paper presents the OPERA RTC-S1 product, providing details about its layers, static layers, and metadata; describing the product’s processing workflow, based on the ISCE3 framework and using the same algorithms developed for the NASA-ISRO Synthetic Aperture Radar (NISAR) mission; and outlining the algorithm verification and the product validation plan. We also present a global mosaic of preliminary OPERA RTC-S1 products generated from a global end-to-end test run from a Sentinel-1A orbit cycle. The OPERA RTC-S1 product will be publicly distributed through the Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC) free of charge, with a release date scheduled for September 2023 with forward stream production.

Ionospheric Faraday rotation distorts satellite radar observations of the Earth’s surface. While its impact on radiometric observables is well understood, the errors in repeat-pass interferometric synthetic aperture radar (InSAR) observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in nonquad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the copol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also, their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1–2 mm is of limited concern in many applications, the persistence on semiannual to multiannual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a nonnegligible source of bias in interferometric deformation measurements.

Deformation estimation from radar interferometric stacks has to confront speckle over decorrelating distributed targets. Inferring the speckle-induced uncertainty in the estimated phase history is challenging. Previously published estimates based on Fisher information (FI) can underestimate the errors by an order of magnitude. Here, we introduce three improvements to mitigate the bias. We: 1) account for uncertainty in the magnitudes of the interferometric covariance matrix elements; 2) penalize the likelihood to reduce the impact of coherence biases on the phase history uncertainty estimates; and 3) constrain the covariance magnitudes to stabilize the estimation. In simulations, these improvements substantially reduced the bias in the uncertainty estimates. Bias reduction was due to an increase in the predicted uncertainty (improvements 1–3) and a decrease in the actual error (improvements 2 and 3). Temporal correlations–crucial for model fitting and testing–were also estimated more accurately. In observations, the underestimation relative to the observed spatial variability was largely eliminated. In contrast to the alternative estimates based on spatial variability, the improved FI uncertainty estimates are applicable to small-scale phenomena such as sinkholes. They can serve as foundation for reliable uncertainty estimates of the deformation derived in subsequent interferometric processing steps, thus bolstering model testing and data fusion.

Since the launch of the Sentinel-1, the Synthetic Aperture Radar (SAR) user community has grown exponentially, adding a large group of users to the field that have only limited experience with the peculiarities of SAR and interferometric SAR (InSAR) data. This paper summarizes some of the recent education and training activities that were initiated by the members of the U.S. SAR science community to help these new user communities build expertise in the use of SAR. The focus is put on initiatives related to the upcoming NASA-ISRO SAR (NISAR) mission, whose globally-acquired, free-and-open L-band SAR data will facilitate a wide range of new science and applications activities. We introduce a set of independent, yet interrelated capacity building projects aimed at developing educational materials for different segments of the SAR user community. The paper also references recently-developed cloud-based training tools and recent education and training events.

This paper describes the concepts and recent activities of the HydroSAR project, a collaborative effort between the University of Alaska Fairbanks, the NASA Goddard and Marshall Space Flight Centers, and the Jet Propulsion Laboratory, focused on the automatic production of SAR-derived information for weather-related hazards. This paper will introduce the main processing algorithms as well as the broad product portfolio of the HydroSAR project, which includes information on surface water extent and surface water depth, in addition to capturing agriculture activity and other event-related changes. We will also highlight the cloud-based implementation of HydroSAR, which enables the system to generate data quickly and over large spatial scales. To demonstrate the performance of the service, we present results from a recent event response, where HydroSAR was used to support mapping activities related to the 2020 South Asia monsoon floods, inundating at least 25% of Bangladesh and large areas of the Bihar Province, India. In-region partner ICIMOD supported the performance assessment of the delivered products and algorithms.

The joint NASA/ISRO SAR (NISAR) satellite mission is anticipated to provide routine L-band coverage of most of the Earth's land surface every 12-days for both ascending and descending orbits. In terms of impact on solid earth science (SES), the primary measurement will be Interferometric SAR (InSAR) observations of ground deformation in two satellite line-of-sight (LOS) directions. Key observation characteristics include acquisitions with small interferometric baselines to maximize interferometric coherence and decrease sensitivity to topography, wide bandwidth allowing for split-band processing to model out the impacts of the ionosphere, and joint L- and S-band observations in selected regions. We describe here the key measurement requirements for solid earth science, as well as our approach to validating these requirements once the mission is underway.

The Gaussian speckle model for homogeneously distributed targets is commonly assumed to apply in repeat-pass radar interferometric analyses, for instance, in deformation estimation. This is despite widespread evidence from snapshot intensity observations indicating deviations from Gaussianity, as many natural land surfaces are intrinsically heterogeneous. The concern is that neglecting heterogeneity will deteriorate the phase estimates and induce underestimation of the uncertainty. Here, we introduce and theoretically characterize compound models that extend the Gaussian speckle model for repeat-pass stacks by representing heterogeneity in intensity and phase. In two L-band repeat-pass data sets, we find pervasive deviations from Gaussianity. Our estimates suggest that the heterogeneity in intensity is largely due to time-invariant, rather than dynamic, texture. Deviations from Gaussianity associated with phase heterogeneity are generally less pronounced. One notable exception with large estimated phase heterogeneity occurs over a permafrost wetland, where degrading ice wedges induce subsidence that is variable on the resolution scale. For deformation analyses, accounting for heterogeneity has, on average, a moderate impact on the phase estimates and the estimated phase uncertainty, which increases by 10% on average. However, in intrinsically heterogeneous areas, such as the permafrost wetland, the accuracy of the phase estimate can realistically improve by up to 20%, and the predicted phase uncertainty increases by 30%. The improvements in phase estimation accuracy and in the quality of the uncertainty estimates when accounting for heterogeneous speckle can, on occasion, make a notable difference for subtle or small-scale deformation.

Intense thunderstorms can bring damaging winds and large hail to agricultural regions during the prime growing season. In certain cases, large swaths of damage from the wind and hail are left behind and visible to satellite remote sensing instruments. Often times, Earth observing optical remote sensing from low to high spatial resolutions are able to view these damaged swaths. With the large number of moderate to high-resolution instruments in orbit, these damaged areas have potential to be viewed daily. However, during the prime growing season, clouds frequently block the viewing of the land surface by these optical instruments. Space-borne synthetic aperture radar (SAR) instruments allow for the viewing of the land surface in most weather conditions, but instead measure backscatter as opposed to optical sensors measuring reflected or emitted radiation. Additionally, the number of SAR instruments with free and open data lags behind the number of optical sensors. This paper describes the development of a methodology that attempts to characterize hail damaged swaths, through independent use of multiple optical and SAR platforms. This satellite-agnostic approach will focus statistical analysis by comparing undamaged areas to suspected damaged areas by using commonly derived indices from optical instruments and SAR backscatter from multiple polarizations.

In Alaska, wildfire map products have traditionally been generated from lower spatial and spectral resolution Landsat imagery such as LANDFIRE Program's Existing Vegetation Type (EVT) resulting in products that do not accurately assess fire fuel types for local sites. In this study we demonstrate the efficacy of AVIRIS-NG hyperspectral data for mapping Interior Alaska's vegetation and fuel type. Based on an evaluation of field plot data collected by the project team in 2019, the new vegetation map derived from AVIRIS-NG at Viereck IV level resulted in a 73% classification accuracy compared to the 32% accuracy of the LANDFIRE's product EVT derived from Landsat 8. Not only did our product more accurately classify fire fuels, it was also able to identify 20 dominant vegetation classes (percent cover > 1%) while the EVT product only identified eight dominant classes within the study area.

As defined in the United States by its National Oceanic and Atmospheric Administration (NOAA) and National Weather Service (NWS), severe thunderstorms are those that produce either tornadoes of any duration and intensity, hail with diameter in excess of 2.54 cm (1 in.), or winds in excess of 26 ms-1(58 mph). These storms often produce heavy rains as individual events and regional flooding if aggregated over multiple days of similar events. Damage to the land surface, urban settlements, and infrastructure is often apparent visually in commercial high resolution and moderate resolution, publicly available satellite imagery, though frequently limited by cloud cover. Synthetic aperture radar (SAR) complements optical remote sensing approaches to identify damage to crops and vegetation from these storms. SAR provides an opportunity for nearly all-weather remote sensing of the land surface through changes in backscatter and coherence among paired images of disparate time but common viewing angle. Changes in polarized backscatter and multi-image coherence observed in regions observed having experienced severe thunderstorms are apparent in regions of hail, wind, and tornado damage to the land surface. In this study, SAR techniques are used to objectively map damage to vegetation caused by severe weather and complemented by traditional measures of vegetation greenness and damage from optical remote sensing, focused on events in the central United States.

In this work we present a cloud-computing based strategy for generating surface displacement time series and mean deformation velocity maps of very large areas by exploiting Sentinel-1 (S1) data. Our approach relies on the simultaneous exploitation of a fast access to the S1 data archives, High Performance Computing resources and external geodetic data.The presented pipeline is based on an advanced cloud-computing implementation of the differential SAR interferometry (DInSAR) Parallel Small BAseline Subset (P-SBAS) processing chain, which allows the fully unsupervised processing of huge Interferometric Wide Swath (IWS) Sentinel-1 data volumes.In particular, for what concerns the S1 data archives, we benefited from the NASA's Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), which stores and distributes S1 SAR data from Amazon Web Service (AWS) for advancing Earth science research. The ASF-DAAC allowed us to reach a very high performance level in terms of downloading time and system reliability. Moreover, we exploited the geodetic measurements provided from the Magnet + Global GPS Network Map of the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL), which supply GPS measurements daily updated and continuosly available. The GPS measurements were used to account for regional trends and to better discriminate the low (tectonic) and high (local) frequency deformation patterns.The presented solution is highly scalable and has been migrated to the Amazon Web Service (AWS) environment. It runs out along an automatic routine to generate the mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area.The developed pipeline has been tested on ascending and descending Sentinel-1 archives acquired over a large area of Southern California (US), which extends over about 150,000 square kilometers.

This paper describes a collaborative effort between the University of Alaska Fairbanks and the NASA Short-term Prediction Research and Transition Center (SPoRT) to expand the use of SAR in the operational response to weather-related hazards in the U.S.Work described in this paper includes a short introduction of the developed SAR-based flood monitoring service with its technical implementation and main hazard information products. We also present results from the application of this systems to two major recent flood hazard events in the U.S. Specifically, the paper will describe the application of the developed hazard monitoring service in operational response to the major hurricanes Harvey, Irma, and Maria (2017), as well as Florence (2018). The analyzed hurricanes caused significant flooding and related damages.For both events periods, we describe how SAR was integrated into hazard response, show representative data products, and describe the main lessons learned from the use of SAR products during event response.

This paper describes the SARVIEWS processing system, a cloud-based hazard monitoring system that provides near real-time information from Sentinel-1 SAR for hazard events related to severe weather, earthquakes, and volcanic unrest.Work conducted within the SARVIEWS project includes (1) the development of hardened fully-automatic processing algorithms for the generation of hazard information products from Sentinel-1 SAR acquisitions; (2) the design of a cloud-based production pipeline to automatically produce these products in near real-time over hazard-affected regions; (3) the implementation of data distribution mechanisms; and (4) the application of SARVIEWS to a range of natural disasters.In this paper, we describe the main goals of the SARVIEWS hazard monitoring system and provide information about its implementation. We showcase the benefits of SARVIEWS for a range of geophysical hazard events and discuss the contributions SARVIEWS made to these events.

Ionospheric parameters e.g., ionospheric height, TEC and the level of disturbances, are estimated using L-band SAR data acquired by ALOS-2 PALSAR-2 over Fairbanks, Alaska. These parameters are compared with coinciding ground-based monitoring data form the Poker Flat Incoherent Scatterer Radar (PFISR) and keograms measured by the University of Alaska, Fairbanks. The comparison results are discussed.

This paper describes a collaborative effort between the University of Alaska Fairbanks and the NASA Short-term Prediction Research and Transition Center (SPoRT) to expand the use of SAR in the operational response to weather-related hazards in the U.S. Work conducted within this effort includes (1) the development of SAR-derived value-added products that can be run in fully-automatic production pipelines and generate easy-to-understand hazard information layers; (2) the design of a cloud-based production and delivery pipeline that can be initiated by an end-user and automatically delivers value-added products to data analysts; and (3) the application of the developed hazard monitoring service in operational response scenarios as well as the evaluation of its performance. In this paper, the various components of the developed service will be outlined. The application of SAR-based hazard data to a range of weather-related disaster events will be described and first findings regarding the performance of the developed services will be shared.

Due to their weather and illumination independence and due to their large area coverage at high spatial resolution, Synthetic Aperture Radar (SAR) images have been recognized as a valuable data source for the mapping and tracking of aufeis flooding events. We modified and utilized the change detection approach of [1], based on wavelet analysis to map aufeis-related flooding on the Sagavanirktok River in northern Alaska, collected in the spring of 2015. This paper provides near real-time monitoring by generating detailed flood parameters such as flood classification probabilities, flood-related backscatter changes, and flood extent. The generated flood maps show the spatial extent and day-to-day progression of the 2015 flooding event across a 1004 km2area, that was determined by processing a series of seven TerraSAR-X datasets. From our analysis, we learned that in early to late April, the formation of aufeis from the Sagavanirktok River crossed the Dalton Highway at several points. In late April to early May, associated warm temperatures led to open water flooding, which flooded the Sagavanirktok River at several locations. It can be argued that along the Sagavanirktok River, warm temperatures led to aufeis growth with the distribution of flow moving from the western channel to the eastern channel.

This paper outlines the development of a new SIR-C processor to replace the original processing system, which is no longer functional. It is important to be able to process raw SIR-C data, because there are several applications for which these data can be used. Two exemplary applications are provided in this paper. First, SIR-C data received with two along-track antennas are used to validate the so-called orthogonal projection algorithm with spaceborne data. Additionally, changes in urban development and infrastructure between the SeaSat and SIR-C missions are analyzed by comparing SAR images of the two radar systems.

This paper presents a global ionospheric error model that was developed for the upcoming L-band NASA ISRO Synthetic Aperture Radar (NISAR) mission. The approach is combining statistical models with empirical data analysis to arrive at global error predictions. The model enables an assessment of capabilities and performance of the NISAR system by providing ionospheric phase noise estimates globally and as a function of environmental conditions. The physical background of the model is introduced and error prediction results are presented.

This paper studies the applicability of radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band frequencies, to the network-wide mapping of pavement roughness of roads in the United States. Based on a comparison of high-resolution X-band images from the Cosmo-SkyMed satellites with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. We developed a signal model that relates radar brightness to IRI, and successfully inverted this model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reached an overall accuracy of 92%. This study demonstrates the capacity of X-band SAR for pavement roughness mapping and suggests that an incorporation of X-band SAR data into DOT operations could provide benefits that may result in costs savings.

In recent years, large number of Interferometric SAR applications in earthquake, cryosphere and tectonic geodesy show the great needs for ionospheric effect correction [1-4]. Several approaches, which mainly include the split spectrum InSAR technique, the Faraday rotation based method and the azimuth shift based method (the Multiple Aperture Interferometric and conventional azimuth shift) have been proposed to meet this end [5-11]. These techniques exploit different aspects - phase delay, polarimetric property and geometric distortion of the ionospheric effect on SAR data to retrieve the differential ionosphere in InSAR. These techniques work well in certain conditions, however, their performance might be limited in cases due to noise effect and the weakness of each technique itself. In this study, we are proposing a combined estimator by joining the Faraday rotation based method and the split spectrum technique of InSAR ionospheric effect correction, which is supposed to achieve a more robust and higher accuracy performance comparing to individual technique itself.

Summary form only given. Monitoring ice motion is of great importance for determining ice mass balance and its contribution to sea level rise. InSAR has become a reliable and important tool for monitoring the ice dynamic due to its known allweather and all-day capabilities. Since 1990s, with the emergence of various sensors of different wavelengths, such as the C-band ERS1/2, Envisat ASAR, Radarsat-1/2, X band TerraSAR-X, Sentinel-1 and the recent L-band ALOS 1&2 PALSAR SAR data, InSAR has become a major technique for monitoring large scale ice motion with unprecedented resolution. Recently the first comprehensive ice motion of the Greenland and the Antarctica have been generated with this technique. Using InSAR to detect the ice motion, the ionospheric phase delay has always been a problem and this is still not properly resolved. This is especially true for low-frequency SAR data. Recent publications of comprehensive ice motion mapping with L-band data shows ionospheric errors are about 17 m/yr near magnetic pole, which can be larger than the actual signal in some areas. Filter-based methods and empirical methods have typically been used to mitigate ionospheric effects, however, without a proper consideration of the physical origin of the ionospheric phase, these methods are prone to error and may introduce spurious biases. In this study, we are going to assess the benefits of ionospheric correction on ice motion mapping using our newly developed split spectrum InSAR-based ionospheric correction. The split spectrum technique forms two or more interferograms from non-overlapping range frequency sub-bands, and estimates the differential ionospheric phase signal by exploiting the dispersive nature of ionospheric delay. Robust co-registration techniques, automatic phase unwrapping error correction, and adaptive filter techniques were developed to enable ionospheric correction with high accuracy. In this paper, we will first present an outline of our split-spectrum InSAR-based ionospheric correction approach, including our advanced error correction and filtering algorithms. We will apply this algorithm to a large number of ionosphere-affected dataset over the large ice sheets (Antarctica and Greenland) to evaluate correction performance and to estimate the benefit of split-spectrumbased ionospheric correction for ice motion analysis. For validation purposes, we will compare the corrected interferometric phase to stable feature like ice islands, emergent mountains and exposed rock out crops. We will also compare the corrected phase data to known ice velocity fields for the analyzed areas. These velocity fields are available to us through cooperation with the lead U.S. scientists for the respective ice sheets (Greenland: Ian Joughin; Antarctic: Eric Rignot). A preliminary result is displayed below. Panel (a) shows the ionosphere contaminated interferogram, and panel (b) is the estimated ionosphere phase which is wrapped, and Panel (c) is the ionosphere corrected interferogram. More analysis about the residue signal will be discussed in detailed in the full paper.

This paper introduces a newly developed interferometric coherence model that can be used to preselect pairs of images for InSAR time-series processing. As coherence values are pre-calculated, automatic image selection can be performed at very little computational cost and computing resources can be dedicated to the few remaining selected interferograms. This can lead to significant throughput improvements in an environment where the number of possible interferometric pairs is fast increasing (through rapidly repeated observations) and the processing of all possible pairs may no longer be computationally feasible. We are in the process of implementing this coherence-based preselection approach in an automatic processing system aimed at the continuous monitoring of volcanoes in Alaska [3].

It is well known that many of the nighttime acquisitions of the L-band Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) instrument over equatorial regions show significant distortions of the image amplitude information. These distortions have the form of amplitude stripes that are roughly aligned with the local geomagnetic field. While ionospheric scintillation has been identified as the source of these distortions, the exact nature of the induced artifacts on synthetic aperture radar (SAR) image quality and SAR signal phase has not yet been studied in sufficient detail. Hence, this paper provides a quantitative analysis of equatorial scintillation effects on SAR image quality and SAR phase. We have performed a statistical analysis of ALOS PALSAR images over equatorial regions to describe the observed distortions and relate them to ionospheric parameters. An ionospheric simulator was developed and validated that is capable of simulating ionospheric distortions based on ionospheric scintillation parameters. Using this simulator, we found that ionospheric scintillation in the equatorial zone can cause significant distortions of SAR image amplitudes, image focus, and SAR signal phase. We determined threshold ionospheric environmental conditions that lead to the formation of these image distortions. Based on these thresholds, we quantified the likelihood of occurrence of ionospheric distortions for the global equatorial belt and for L-band sensors ALOS PALSAR, ALOS-2 PALSAR-2, and NASA-ISRO SAR (NISAR).

Finding solutions for the mitigation of atmospheric phase delay patterns from differential synthetic aperture radar interferometry (d-InSAR) observations is currently one of the most active research topics in radar remote sensing. Recently, many studies have analyzed the performance of regional numerical weather prediction (NWP) models for this task; however, despite the significant efforts made to optimize model parameterizations, most of these studies have concluded that current regional NWPs are not able to robustly reproduce the atmospheric phase delay structures that affect SAR interferograms. Despite these previous findings, we have revisited the application of NWPs for atmospheric correction using a different analysis strategy. In contrast to earlier studies, which assessed the quality of NWP-derived phase screen data, we have studied NWPs from a statistical angle by analyzing whether they are able to provide realistic information about the statistical properties of atmospheric phase signals in d-InSAR data. We have determined that NWP forecasts can provide relevant statistical information about the atmospheric phase screen captured in d-InSAR data. Based on this, this study presents a new atmospheric phase filtering approach that is using statistical atmospheric information as a prior in order to optimize the choice of unknown filter parameters. The mathematical concept of the prior-driven filtering approach is outlined, and its implementation is explained. We have determined the performance of this new filter concept and have shown that it comes very close to a filter optimum.

The distribution of coherent targets takes a key role in the assessment of the performance and the evaluation of the processing results of multi-temporal Synthetic Aperture Radar interferometry (InSAR) approaches. In previous studies, the evaluation of the spatial distribution of coherent targets was largely based on rather simple parameters such as the spatial point density. While these parameters provide information on the total number of coherent points in an area, they are often insufficient to fully describe their spatial distribution. Hence, with this paper, new descriptors are introduced that better characterize the spatial distribution of coherent targets in an interferogram and serves as a better indicator of InSAR performance. A quantitative study is established both via simulated and real SAR data and the performance of the new parameters are discussed to demonstrate the capability of the developed parameters in describing the spatial distribution of coherent targets.

Radio frequency interference (RFI) is a known issue in low-frequency radar remote sensing. In synthetic aperture radar (SAR) image processing, RFI can cause severe degradation of image quality, distortion of polarimetric signatures, and an increase of the SAR phase noise level. To address this issue, a processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations. In addition to being the basis for image correction, the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of RFI-related information that allows for mapping, characterizing, and classifying RFI signatures across large spatial scales. The extracted RFI information is expected to be valuable input for SAR-system design, sensor operations, and the development of effective RFI-mitigation strategies. The concepts of RFI detection, analysis, and mapping are outlined. Large-scale RFI mapping results are shown. In case studies, the benefit of detailed RFI information for customized RFI filtering and sensor operations is exemplified.

The dominant error source for short wavelength spaceborne radar signals is due to water vapor present in the neutral atmosphere (neutrosphere). This distortion signal is characterized by high variations in time and space, and can be exploited as a valuable source for quantifying the water vapor content of the Earth's atmosphere. Available water vapor measurements provided by Envisat Medium Resolution Imaging Spectrometer (MERIS) and simulations from numerical weather prediction models are still limited in observing rapid fluctuations of water vapor. Therefore, we are investigating Interferometric Synthetic Aperture Radar (InSAR) for water vapor mapping. In this paper, water vapor maps derived from Persistent Scatterer InSAR (PSI), MERIS, and the Weather Research and Forecasting (WRF) model are presented with comparative analyses.

After more than a decade of continuous improvements, Persistent SAR Interferometry (PSI) has become an accepted tool for long-term monitoring of geophysical phenomena such as volcanoes, earthquakes, and city deformation. To extract surface deformation from PSI observations, different phase component are separated based on their physical or stochastic properties. Atmospheric signals, one of the main error contributions, are usually mitigated using a low-pass filter in time. This approach provides unsatisfactory results if the temporal sampling of the surface deformation signal is close to Nyquist, and if there are sampling gaps in the time series. Hence, in many cases, a more sophisticated way to mitigate atmospheric signals is required. This paper proposes an adaptive filter design that is utilizing knowledge of the stochastic properties of the atmosphere at SAR acquisition times. The concept of the filter is presented and its performance is tested on simulated signals. In these studies, the adaptive filter design performed favorably and we are expecting this approach to be of major benefit for PSI processing. In current work, the performance of the adaptive filter is tested on real data. For this study, the pre-known statistics of atmospheric delay is provided by Numerical Weather Forecast Models.

Radio Frequency Interference (RFI) is a known issue of low-frequency Synthetic Aperture Radar (SAR) data. In this paper, a processing system is described that is successful in detecting, characterizing, and mitigating RFI signatures in data of the L-band SAR system ALOS PALSAR. An RFI analysis system that is integrated into the SAR processor is logging a wealth of RFI-related information that is used to map, characterize, and classify RFI signatures across large spatial scales. The concept of the RFI detection and mitigation algorithm is described, the RFI analysis concept is outlined, and examples of spatial RFI maps are shown.

The theoretical framework necessary to facilitate the mitigation of ionospheric physical effects on amplitude and phase of low-frequency space-based synthetic aperture radars (SAR) is presented. By way of a unique research collaboration the authors have developed mathematical and stochastic descriptors of the spatio-temporal structure and its impact on SAR signals passing through the ionosphere during different levels of scintillation. The authors have developed a prototype integrated ionospheric processor that combines multiple existing and newly developed correction methods with the goal to maximize correction robustness and accuracy. For the first time, realistic stochastic models describing the amplitude and spatial structure the ionosphere were developed and validated; thereby, allowing the study and discovery of the origin of SAR image artifacts that are caused by ionosphere scintillation; i.e., a noise-like high frequency phase distortion.

For spaceborne SAR (Synthetic Aperture Radar) systems, the dispersive effects of the ionosphere on the propagation of the SAR signal can be a significant source of phase error. While at X-band frequencies the effects are small, current and future P-, Land C-band systems would benefit from ionospheric compensation to avoid errors in topographic retrieval. In this paper the focus is on the effects of the ionosphere on repeat-pass SAR interferometry from Pthrough X-bands and methods for their estimation which are demonstrated on L-band ALOS-PALSAR acquisitions.

When a Synthetic Aperture Radar (SAR) signal propagates through the atmosphere on its path to and from the sensor, it is inevitably affected by atmospheric effects. In particular, the applicability and accuracy of Interferometric SAR (InSAR) techniques for volcano monitoring is limited by atmospheric path delays. Therefore, atmospheric correction of interferograms is required to improve the performance of InSAR for detecting volcanic activity, especially in order to advance its ability to detect subtle pre-eruptive changes in deformation dynamics. In this paper, we focus on InSAR tropospheric mitigation methods and their performance in volcano deformation monitoring. Our study areas include Okmok volcano and Unimak Island located in the eastern Aleutians, AK. We explore two methods to mitigate atmospheric artifacts, namely the numerical weather model simulation and the atmospheric filtering using Persistent Scatterer processing. We investigate the capability of the proposed methods, and investigate their limitations and advantages when applied to determine volcanic processes.

High resolution numerical weather models have recently raised a great interest in the InSAR community for atmospheric phase screen (APS) mitigation. Following the re search carried out in [1], in this study we focus on investigating the sensitivity of WRF (Weather Research and Fore casting) predictions to the model parameter settings which may substantially affect the result of water vapor modeling and to different boundary conditions. We validate the model predictions using atmosphere-only interferograms as well as radiosonde records. Our result shows that the radiosonde records (on average) agree very well with the WRF predictions based on our new model settings. However, in terms of spatio-temporal delay variation, the new settings do not always lead to a better prediction and the correction of atmospheric delay is case dependent. Therefore, we conclude that WRF lacks the reliability to correct the realistic APS in interferograms.

Remote sensing of surface deformation from volcanic activity is essential to modern volcanology. This paper provides an example of how observations from multiple sensors can be combined to measure topographic changes resulting from active volcanism. High resolution DEMs were derived from optical triplets acquired by the ALOS-PRISM sensor, and processed with microwave data from ALOS-PALSAR to produce centimeter scale deformation maps and decorrelation maps of March-April, 2009 lahars in the Drift River Valley, Alaska. We demonstrate that multi-sensor data fusion of readily available, inexpensive satellite images and data is an immediately available method of increasing accuracy and precision in measuring volcanic deposition and deformation.

This paper analyzes severe, broadband, radio frequency interference (RFI) signatures that are commonly observed in L-band ALOS PALSAR images acquired close to the North American Arctic coast. These RFI signals are caused by military over-the-horizon radar systems. We introduce the specifics of the interfering signals and demonstrate that standard RFI Filters used in the operational ALOS PALSAR processor are insufficient to remove their influence. A new approach for filtering the identified RF interferences is presented and its performance is analyzed by a cross comparison with reference methods. The capabilities of the new correction method are emphasized by presenting several processing results, A statistical and geographical analysis of the strength and distribution of observed RFI signatures is shown, indicating widespread contamination of imagery across the American Arctic coast.

This paper focuses on deriving a realistic statistical model for ionospheric effects in low-frequency Synthetic Aperture Radar (SAR) data. The approach used to develop this statistical model is based on the assumption that, for a certain range of scales, ionospheric plasma turbulence can be considered a scale-invariant process that can be described by power-law functions or fractal statistics. Based on the parameters of a power law model, covariance functions and ionospheric variance-covariance matrices are derived. An ionospheric phase statistics simulator (IP STATS) is presented that is capable of calculating a variety of statistical descriptors and is able to predict representative ionospheric phase screens. To demonstrate its functionality and performance, the IP-STATS system is used to derive statistical models for a 10 year time series of T-band SAR data over the area of Alaska. The developed theory has potential applications in statistical modeling of SAR data, sensitivity analysis of spaceborne SAR systems, and signal simulation. IP-STATS may also be a useful tool in mission design especially in selecting favorable system center frequencies and orbit parameters.

In recent years, significant progress has been made in developing theory and methods for modeling, detecting, and correcting ionospheric effects in low-frequency synthetic aperture radar (SAR) data. While a large number of correction methods have been developed that differ in sensitivity, data needs, and spatiotemporal accuracy, a lack of performance requirements for ionospheric correction has prevented an evaluation of their suitability for operational implementation. Hence, this paper focuses on the development of performance requirements for the correction of ionospheric effects in low-frequency SAR data. The requirements are derived considering the data quality needs of a set of SAR applications and will ensure the SAR data after ionospheric correction to meet calibration specifications and maintain full performance during all ionospheric conditions. The proposed requirements can serve as a benchmark for a performance assessment of ionospheric correction methods and will help define their suitability for operational implementation. Requirements are determined for SAR polarimetry, SAR imaging, SAR interferometry, and ionospheric research.

For spaceborne SAR (Synthetic Aperture Radar) systems, the dispersive effects of the ionosphere on the propagation of the SAR signal can be a significant source of phase error. While at X-band frequencies the effects are small, current and future L-band systems would benefit from ionospheric compensation. We consider two ways to estimate the ionospheric delay in SAR signals and evaluate them on L-band ALOS-PALSAR acquisitions.

The influence of the turbulent atmosphere is seen as the main performance limitation for high-quality Interferometric Synthetic Aperture Radar (InSAR) techniques in ground deformation monitoring applications. Atmospheric correction using numerical weather prediction (NWP) models is widely seen as a promising emerging technology for mitigation of atmospheric signals. First results showed promising capabilities for correction of stratified delay yet have revealed limited performance for modeling and mitigating turbulent atmospheric water vapor signals from SAR [1, 2]. This paper presents an integration of InSAR observations with predictions from the high-resolution Weather Research and Forecasting Model (WRF). Special focus is put on investigating improvements in the weather model parameterization to achieve enhanced performance in atmospheric correction. First, a statistical analysis of the quality of absolute delay predictions is presented based on a comparison of vertically integrated WRF delays with radiosonde measurements. Second, the performance of WRF for atmospheric correction of InSAR data is analyzed by comparing WRF phase delay maps to SAR interferograms and analyzing structure functions and variances of the residual atmospheric delay signal. Here, significant improvements could be achieved through modifications of the WRF model parameterization, which are highlighted in Section 3.2. From our study, we conclude that the performance of latest generation high-resolution NWPs can be significantly improved if the setup and parameterization of the model domain is optimized.

Active remote sensing techniques, like SAR tomography and full-waveform LIDAR, are able to capture the 3D reflectance function at or inside objects. They are therefore of special interest for analyzing forest environments. Research goals are the derivation and characterization of the different physical measurement aspects of data taken over forested areas, as well as establishing mutual relations in such a way that LIDAR data can be used to calibrate and correct 3D density data of SAR tomography. The paper outlines the phenomenology of forested areas in SAR tomograms and full-waveform LIDAR data and sketches a simple mathematical methodology for linking SAR and LIDAR reflection density profiles.

Ionospheric signal distortions are commonplace in low-frequency space-borne SAR observations and can lead to the degradation of SAR data quality and data consistency if no signal compensation is applied. In this paper we will give an overview of the problem of ionospheric influence in SAR, PolSAR, and InSAR data. We will characterize the spatiotemporal signal properties of ionospheric signals, introduce a selection of currently available correction methods, and present a list of performance requirements to be met by ionospheric correction.

Auroral physics is an exceedingly rich and complex subject. However, due to a lack of high resolution data of ionospheric activity during auroral events, not all phenomena in the high latitude ionosphere are fully understood. Recent research has proven that L-band SAR data is significantly affected by the ionosphere and can be used for mapping its activity. With this paper we will prove and unambiguously verify the potential of L-band SAR to capture auroral activity. We will present examples of aurora signatures mapped from ALOS PALSAR data and will verify the results from SAR with observations provided from ground based measurements.

Tropospheric water vapor delay is the main error source limiting the accuracy of SAR interferometric measurements. In Persistent Scatterer Interferometry (PSI), the presence of atmospheric effects increases the required data amount for PSI immensely, limiting the reaction time and applicability of the technique. On ENVISAT, water vapor products derived from the optical sensor MERIS may be used to mitigate tropospheric effects in simultaneously acquired ASAR data. In this paper the atmospheric signals in MERIS and ASAR are compared, and methods for their optimal combination are suggested.

The impact of ionospheric propagation effects on the signal properties of SAR systems is significant and increases with decreasing carrier frequency. Besides polarimetric applications, also interferometric SAR processing can be significantly affected. Relative range shifts, internal image deformations, range and azimuth blurring, and interferometric phase errors are the most significant effects to be considered. In this paper we provide the theoretical background for ionospheric effects on InSAR. We quantify expected magnitudes of the respective effects for various existing SAR sensors and discuss methods for their detection and correction. Real data examples, mainly stemming from the ALOS PALSAR mission, are presented to verify the derived theory.

The influence of the ionosphere on spaceborne SAR signals can be significant, especially approaching wavelengths at L-band or larger. By measuring these effects and inverting the underlying physical model, these effects can be used to measure and map aspects of the ionosphere. ALOS PALSAR data is employed to demonstrate this capability. Single-pol data is used to determine second-order gradients of the iononsperic Total Electron Content (TEC) (the second derivative of intensity). Dual-pol can yield lateral TEC variations in homogeneous areas. Finally, Faraday rotation measurements from full-pol data can be used to create 2-D maps of the absolute TEC.

With the synthetic aperture radar (SAR) sensor PALSAR onboard the Advanced Land Observing Satellite, a new full-polarimetric spaceborne L-band SAR instrument has been launched into orbit. At L-band, Faraday rotation (FR) can reach significant values, degrading the quality of the received SAR data. One-way rotations exceeding 25degare likely to happen during the lifetime of PALSAR, which will significantly reduce the accuracy of geophysical parameter recovery if uncorrected. Therefore, the estimation and correction of FR effects is a prerequisite for data quality and continuity. In this paper, methods for estimating FR are presented and analyzed. The first unambiguous detection of FR in SAR data is presented. A set of real data examples indicates the quality and sensitivity of FR estimation from PALSAR data, allowing the measurement of FR with high precision in areas where such measurements were previously inaccessible. In examples, we present the detection of kilometer-scale ionospheric disturbances, a spatial scale that is not detectable by ground-based GPS measurements. An FR prediction method is presented and validated. Approaches to correct for the estimated FR effects are applied, and their effectiveness is tested on real data.

Persistent Scatterer Interferometry is a well-known technique to obtain displacement rates in urban areas from a stack of SAR interferograms. Besides the original method introduced by A. Ferretti, C. Prati and F. Rocca in the late 1990's, which estimates the displacement rates and DEM corrections by an ensemble coherence maximization approach (periodogram) based on a common master image, several other algorithms have been introduced in the past few years. One of these approaches has been developed at DLR It incorporates the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method that was originally developed for fast GPS double difference ambiguity estimation. In this paper different parameters are tested to investigate robustness and performance of this estimation method. At first the effects of a reduced number of observations and varying reference points on the estimation with LAMBDA are analyzed while in the second part a direct comparison between LAMBDA and ensemble coherence maximization (periodogram) is performed.

Faraday rotation can degrade the quality of low- frequency spaceborne SAR data, making an estimation and correction of these effects a prerequisite for data quality continuity. In this paper, methods for predicting and estimating Faraday rotation are presented and tested on ALOS/PALSAR data. A first example for unambiguous detection of Faraday rotation in SAR is shown. In addition, the improvement after correcting for FR is proven using a real data example.

Traffic data acquisition from space has evolved to an important task over the last years. Future SAR satellite missions will provide high resolution dual-channel SAR data and therefore a possibility to collect traffic parameters of a large area from space. In this paper a detection approach for vehicles will be presented, which considers simultaneously the effects moving objects suffer from in the SAR image. The performance of the proposed detection scheme is analyzed using experimental airborne SAR data.

Standard synthetic aperture radar (SAR) processing algorithms use analytically derived transfer functions in the 2D frequency and range/Doppler domains. These rely on the assumption of hyperbolic range histories of monostatic SARs with straight flight paths. For bistatic SARs, the range histories are no longer hyperbolic, and simple analytic transforms do not exist. This paper offers two solutions for bistatic SAR data processing under the restriction of quasi-stationarity, i.e., sufficiently equal velocity vectors of transmitter and receiver. 1) Moderately bistatic configurations can be handled satisfactorily by using hyperbolic range functions with a modified velocity parameter, which is a solution already well known for the accommodation of curved orbits in the monostatic case. This "equivalent velocity" approach is shown to be of surprising range of validity even for pronounced bistatic situations. It is not to be confused with the "equivalent monostatic flight path" approximation, which is shown to be inapplicable for any practical case. 2) With increasing separation of transmitter and receiver, the equivalent velocity approximation deteriorates. To cope with extreme bistatic configurations, a general approach named "NuSAR" is proposed, where the involved transfer functions are replaced by numerically computed ones. This paper describes how the transfer functions are computed from the given orbits and the shape of the Earth surface. In any of these two cases, the bistatic SAR data can be processed by standard SAR processors; only the conventional transfer functions need to be replaced. Neither are there time-domain prefocusing or post focusing steps required nor complicated mathematical expansions involved. The presented algorithms are also applicable to very high resolution wide-swath (or squinted) SARs on curved orbits.

Standard SAR processing algorithms use analytically derived transfer functions in the 2-D frequency and the range- Doppler domains. These rely on the assumption of hyperbolic range histories of monostatic SARs on straight flight paths. A moderate deviation from these conditions can often be handled satisfactorily by a range-variant velocity parameter. However, bistatic SARs with considerable separation between transmitter and receiver can no longer be approximated accurately enough by hyperbolic range histories. In [1] we have presented an alternative approach, NuSAR. We suggested using numerically computed transfer functions for bistatic SAR processing. We presented the idea and gave hints on how to compute the three transfer functions required for SAR processing. In this paper we present bistatic simulation results with the NuSAR algorithm. It is shown, that NuSAR works well even in extreme bistatic configurations, as long as we restricted ourselves to quasi-stationarity, i.e. where the velocity vectors of receiver and transmitter are similar enough that we may assume the point response function to be sufficiently azimuth-invariant within a single azimuth processing block.

Ionospheric propagation effects have a significant impact on the signal properties of low-frequency synthetic aperture radar (SAR) systems. Range delay, interferometric phase bias, range defocusing, and Faraday rotation are the most prominent ones. All the effects are a function of the so-called total electron content (TEC). Methods based on two-frequency global positioning system observations allow measuring TEC in the ionosphere with coarse spatial resolution only. In this letter, the potential of broadband L-band SAR systems for ionospheric TEC mapping is studied. As a basis, the dispersive nature of the ionosphere and its effects on broadband microwave radiation are theoretically derived and analyzed. It is shown that phase advance and group delay can be measured by interferometric and correlation techniques, respectively. The achievable accuracy suffices in mapping small-scale ionospheric TEC disturbances. A differential TEC estimator that separates ionospheric from tropospheric contributions is proposed

This paper reviews the theoretical background for upcoming dual-channel Radar satellite missions to monitor traffic from space. As it is well-known, an object moving with a velocity deviating from the assumptions incorporated in the focusing process will generally appear both displaced and blurred in the azimuth direction. To study the impact of these (and related) distortions in focused SAR images, the analytic relations between an arbitrarily moving point scatterer and its conjugate in the SAR image have been reviewed and adapted to dual-channel satellite specifications. To be able to monitor traffic under these boundary conditions in real-life situations, a specific detection scheme is proposed. This scheme integrates complementary detection and velocity estimation algorithms with knowledge derived from external sources as, e.g., road databases.

The launch of the advanced high resolution radar satellite TerraSAR-X in summer 2006 opens new possibilities for the demonstration of traffic monitoring from space. DLR is currently developing an operational traffic processor for the TerraSAR-X ground segment. The paper presents results from an airborne SAR GMTI campaign that was part of a study for algorithm development and processor design. DLR’s E-SAR sensor was used in an Along-Track Interferometry (ATI) mode to image vehicles in controlled and uncontrolled situations. The paper gives an overview on the experiment and presents the results of across- and along-track velocity estimation. Adapted SAR processing techniques were applied to enhance the peak energy of the moving objects in the focused SAR images. The paper presents first results and discusses the techniques.

This paper analyzes the potential of the upcoming German satellite mission TerraSAR-X to monitor traffic from space. As it is well-known, an object moving with a velocity deviating from the assumptions incorporated in the focusing process will generally appear both displaced and blurred in azimuth direction. To study the impact of these (and related) distortions in focused SAR images, the analytic relations between an arbitrary moving point scatterer and its conjugate in the SAR image have been derived and adapted to the TerraSAR-X specifications. To be able to monitor traffic under these boundary conditions in real-life situations, a specific detection strategy is proposed. This strategy makes use of knowledge derived from external sources, as e.g. GIS and semantic models for traffic flow.

This paper describes a new technique to separate topography- and displacement-related phase components in SAR interferograms of Arctic glaciers. Compared to standard 4-pass-interferometry, a combination of several interferograms in a least-squares adjustment on the basis of a extended glacier flow model is proposed. The method enables to detect areas with significant changes of glacier flow velocity, supports a detailed accuracy analysis, and allows the estimation of the influence of systematic errors. The paper includes a description of the model, accuracy analysis and an error budget.

We propose a general method to separate topography- and displacement-related phase components in repeat-pass interferograms based on adjustment theory. The method is exemplarily demonstrated for the analysis of rheology of Arctic glaciers. More than two multitemporal interferometric data sets are combined in a least squares adjustment based on a Gauss-Markov model. Within the adjustment algorithm glacier flow is modeled by a polynomial function. This technique on the one hand allows us to improve the separation of topography- and displacement-related phase components and on the other hand provides glaciological information about the flow characteristics of observed glaciers.