Launching in early 2024, the NASA-ISRO SAR (NISAR) mission will provide global data freely accessible enabling large scale surface deformation monitoring with synthetic aperture radar (SAR) acquired at L-band and S-band radar wavelengths. In preparation for calibration and validation of the NISAR L-band data, the NISAR Solid earth science team is systematically processing over 450 Japan Aerospace Exploration Agency (JAXA) ALOS-2 PalSAR-2 wide-swath (ScanSAR) L-band acquisitions covering the West Coast of the United States for measuring co-seismic, secular and transient displacements. The area spans California, Washington and Oregon.

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.

NASA-ISRO Synthetic Aperture Radar (NISAR) mission with a near-global coverage of land and cryosphere regions with 12 days repeat will operationally produce level-1 and level-2 science products from L-band radar by the NASA data system at Jet Propulsion Laboratory. The products will be available to the users through the NASA’s Distributed Active Archive Center (DAAC) at Alaska Satellite Facility (ASF). In this paper we present the algorithms to process, calibrate and assess the quality of the NISAR products after launch. We simulate NISAR raw data from different science modes and with NISAR L-band configurations (such as left looking, squinted beam and dithered data and the same transmit chirp and receive configuration as the L-band instrument) and process them through the NISAR standard processor to from level-1 and level-2 products. We quantify the quality of formed images and their derived level-2 products such as geocoded SLC, covariance and interferometry products over point targets. We further evaluate the performance of the NISAR processor and algorithms using real L-band data acquired by ALOS-1 and ALOS-2 and reformatted to NISAR format.

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.

This paper presents an image formation algorithm for the focusing of SAR data with space-variant impulse response functions caused by eccentric orbits around small high-curvature surfaces, such as is encountered in stable orbits around Saturn’s moon Enceladus, a potential target for future SAR missions such as the Nightingale mission concept under development at JPL. Due to the extreme geometry, the range history shows a significant dependence on the target’s azimuth position within time scales significantly shorter than the synthetic aperture duration. Therefore, additional steps are needed in order to compensate for this effect and minimize image degradation. In this context, the present contribution evaluates the space variance of the geometry for SAR surveys over Enceladus and proposes a processing flow to account for it. Point target simulations using the proposed processing algorithm are shown to verify the approach.

Over the last decades, repeat-pass SAR interferometry (InSAR) for deformation measurement and topographic mapping has revolutionized our understanding of many geophysical processes on Earth. A new mission concept, currently in development at the Jet Propulsion Laboratory (JPL) and Caltech, aims at using orbital repeat-pass InSAR for deformation and topography mapping of Saturn’s ice-covered and geologically active moon Enceladus. In this paper, we present an initial performance assessment of the system and the suggested SAR processing approach, along with simulated InSAR acquisitions using a DLR in-house End-to-End performance simulator.

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.

This paper described the ongoing effort and the scientific benefits of close coordination between more than a dozen ongoing and planned spaceborne SAR missions. Specific Illustrative examples of the scientific and applications benefits are included and described.

Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.

In this article, we investigate the link between the closure phase and the observed systematic bias in deformation modeling with multilooked SAR interferometry. Multilooking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in interferometric synthetic aperture radar (InSAR) measurements. However, multilooking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multilooked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multilooked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol Trough, CA, USA. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baselines. We show that these biases can be identified and accounted for.

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.

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.

Modern spaceborne radar systems today achieve near-global coverage at moderate resolution with weekly or better repeat intervals. Consideration of adequate temporal sampling and the need to average images to reduce noise suggests that reducing radar revisit times can enable heretofore impossible applications. We propose that the aspirational goal of fine-resolution repeat coverage every 6 hours would be a revolutionary advance in radar and InSAR capability and utility, and should be a guidepost for new system design. Here we review the science and user needs for such a system, and show that current technologies, with some evolutionary extensions can enable such a system.

The NASA-ISRO SAR (NISAR) mission will map the Earth's surface every 12 days using a reflector-feed system with scan-on-receive (SweepSAR) capability. An alternative for continuous swath coverage is to employ staggered SAR operation, i.e., to vary the pulse repetition intervals over time. In this way, the blocked (missing) samples are spread in range and azimuth and can be interpolated prior to focusing. However, the relatively low azimuth oversampling intended for NISAR in combination with the strong non-uniformity of the staggered data grid forbids the use of conventional interpolation kernels. This paper discusses a processing approach for the NISAR staggered data and validates the strategy with simulated data. Moreover, the impact of the staggered operation for contrast-based and interferometric applications is discussed with examples.

The United States National Aeronautics and Space Administration (NASA) has initiated a five-year study of a Surface Deformation and Change Observing System Architecture. The goal of the study is to define an observing system that would meet the recommendations of the 2017 US National Academy of Sciences, Engineering, and Medicine Decadal Survey for developing an affordable synthetic aperture radar-based system or systems that can address fundamental scientific questions about the nature of hazards, disasters, landscape change, and serve applications communities as well. The study is to consider the existing international constellation of SAR systems, both civil and commercial, as well as new technologies that could be employed to address scientific and applications objectives and to improve affordability. The study will be conducted in four phases: 1) expanding the architectural trade space; 2) assessing the science and applications value of each identified architecture; 3) down selecting to three final candidates and performing detailed assessments; 4) final reporting and preparation for mission formulation. The first phase is approximately 2 years in duration, and will concentrate on the scope of research and application areas to be addressed, defining the possible architectures to consider, and beginning the assessment of their performance. The study team comprises five NASA centers. Broad participation from the research, applications, technology, and commercial sectors is planned.

We study the temporal variability of soil and vegetation backscatter at L-band using a dense airborne time-series. Backscatter is assumed to change over time due to diurnal variations in soil and canopy water content as well as precipitations. A two-layer SAR backscattere model traditionally used for above-ground biomass retrieval is augmented here with the time dimension in order to guide the data analysis. The model is informed by examining a 5-year time-series of 32 L-band polarimetric UAVSAR images acquired over a vegetated area near the Sacramento Delta in California. Our initial results reported in this paper show that the temporal variability of soil and vegetation backscatter in absence of precipitation events fits well a lognormal probability distribution with mean and standard deviation related to each other. Characterizing the diurnal, seasonal and interannual variability of L-band backscatter may be critical for the successful estimation of ecosystem variables from dense time-series to be acquired globally by the NISAR mission.

The InSAR Scientific Computing Environment (ISCE) was first developed under the NASA Advanced Information Systems Technology as a flexible, extensible object-oriented framework for Interferometric Synthetic Aperture Radar (InSAR) processing. The ISCE framework uses Python 3 at the workflow level, controlling modules of compiled code for functional processing, and managing inputs, outputs, and other flow control services. The currently released version, called ISCE 2.1, is distributed to the research community through the Western North America InSAR Consortium under a research license. The ISCE team is working on the next generation of the code in order to prepare for the NASA-ISRO SAR (NISAR) mission operational processing. Innovations in this code include augmentation or conversion of the custom Python framework elements in ISCE with the Pyre framework, new workflows for interferometric and polarimetric stack processing, a more intuitive and graphically based user interface, and flow control for hybrid computing environments including CPU/GPU clusters, logging and error tracking facilities, and new more efficient computational modules that exploit graphical processor units (GPUs) when available. The ISCE 3.0 framework is designed to work in an operational environment as well as on a single user's laptop or compute cluster, with services to discover capabilities and scale computations accordingly.

The NASA-ISRO Synthetic Aperture Radar (NISAR) mission [4] will redefine the future of earth science in terms of both the quality as well as the quantity of data that will be downlinked daily. The InSAR Scientific Computing Environment (ISCE) [1], a powerful, modular, and extensible framework, is expected to be used as the processing engine for generating tens of terabytes of SAR data products from the NISAR mission on a daily basis. Until recently, the ISCE framework was designed to exploit traditional CPU-based computational architectures. Simulations with SAR data from ESA's Sentinel-1 constellation have shown that these traditional implementations maybe insufficient to handle the daily influx of data from the NISAR mission. This paper describes the initial efforts by the NISAR project team to explore the use of Graphics Processing Units (GPUs) to accelerate SAR data processing. Preliminary results from “GPU-izing” interferogram generation workflows indicate that processing times can be reduced by an order of magnitude without loss in precision, potentially setting a new standard for radar processing in the world of “Big SAR Data”.

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.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing a synthetic aperture radar (SAR) mission to map Earth's surface every 12 days, known as the NASA-ISRO SAR (NISAR) Mission. NISAR has completed its preliminary design and successfully passed its Preliminary Design Review in June 2016. This paper describes the radar instrument design, engineering model hardware development status, test results, and the plans for future maturation toward the Critical Design Review in late 2018. NISAR has two radars sharing a mechanical structure and reflector, one operating at L-band (24 cm wavelength) and the other at S-band (10 cm wavelength). To achieve wide-swath observations at both wavelengths, NISAR is designed as a reflector-feed system where the feed aperture elements are individually sampled to allow a scan-on-receive capability. In the partnership, NASA provides the instrument structure for both L- and Sband electronics, the L-band electronics, the reflector and associated boom, and an avionics payload to interface with the radar including a solid-state data recorder, high-rate Ka-band telecommunication link, and a GPS receiver. In addition to providing the spacecraft and launch vehicle for the mission partnership, ISRO is also providing S-band radar electronics, and an additional high-rate Ka-band telecom package. This paper will describe aspects of this technically and logistically complex international instrument and mission development, one that requires careful definition of interfaces, tracking of resources, joint analysis of designed capabilities, and detailed integration and test plans.

PLANT (Polarimetric-interferometric Lab and Analysis Tools) is a new collection of software tools developed at the Jet Propulsion Laboratory to support processing and analysis of Synthetic Aperture Radar (SAR) data for ecosystem and land-cover/land-use change science and applications. PLANT inherits code components from the Interferometric Scientific Computing Environment (ISCE) to generate high-resolution, coregistered polarimetric-interferometric SLC stacks from Level-0/1 data for a variety of airborne and spaceborne sensors. The goal is to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g., maps of above-ground biomass and forest disturbance). In this paper we introduce the capabilities of PLANT and report first results obtained with the tools developed up to date.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing a synthetic aperture radar (SAR) mission to map Earth's surface every 12 days, known as the NASA-ISRO SAR (NISAR) Mission. NISAR has two radars sharing a mechanical structure and reflector, one operating at L-band (24 cm wavelength) and the other at S-band (10 cm wavelength). To achieve wide-swath observations at both wavelengths, NISAR is designed as a reflector-feed system where the feed aperture elements are individually sampled to allow a scan-on-receive capability. In the partnership, NASA provides the instrument structure for both L- and S-band electronics, the L-band electronics, the reflector and associated boom, and an avionics payload to interface with the radar including a solid-state data recorder, high-rate Ka-band telecommunication link, and a GPS receiver. ISRO provides the spacecraft and launch vehicle, and the S-band radar electronics, and an additional high-rate Ka-band telecom package. Hardware prototyping has matured designs for engineering models, which are currently under development.

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agency's (ASI's) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agency's (JAXA's) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITAR's (United Nations Institute for Training and Research's) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobe's WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

The Orbiting Arid Subsurface and Ice Sheet Sounder (OASIS) mission concept is the first to directly explore the signatures of climate change beneath the surface of two of the least well-understood arid regions of the Earth: the polar ice sheets and the hyper-arid deserts (Figure 1). With these first-of-a-kind measurements of land ice and shallow aquifers, OASIS has two well-defined science objectives. The first is to determine the thickness, inner structure, and basal boundary conditions of Earth's ice sheets to understand their dynamics and to improve models of current and future ice sheet response to climate change and, hence, to better constrain ice sheet contribution to sea level rise. The second objective is to perform detailed mapping of the spatial distribution of shallow (<;100 m deep) aquifers in the most arid regions on Earth to understand groundwater hydrology, enhance groundwater flow models, and provide new insights into available water resources and paleoclimatic conditions. These two mission objectives, which align closely with two NASA Earth Science program objectives on climate and water cycle, are achieved using measurements made by a single, low-cost and proven-heritage instrument: a 45 MHz center frequency radar sounder with 10 MHz bandwidth. The OASIS radar is similar to instruments on two successful Mars missions, Mars Express and Mars Reconnaissance Orbiter, presently probing the Martian subsurface.

Since the 2007 National Academy of Science “Decadal Survey” report “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond” [1], the National Aeronautics and Space Administration (NASA) has been studying concepts for a Synthetic Aperture Radar (SAR) mission to determine Earth change in three disciplines - ecosystems, solid earth, and cryospheric sciences. One of the most promising and original concepts involves an innovative international partnership between NASA and the Indian Space Research Organization (ISRO). Previous NASA concepts had focused on exploiting an L-band array-fed reflector SAR configuration that enabled > 200 km swath at full SAR resolution and full polarimetry simultaneously in order to meet requirements in all three disciplines [2]. The feed where the electronics are housed in this design is relatively compact compared to a planar phased array antenna with similar azimuth resolution capability. This compactness allows for straightforward addition of feed array elements at other frequencies. As the partnership concept with ISRO developed, it became clear that flying dual L- and S-band SAR capabilities, with L-band electronics supplied by NASA and S-band electronics by ISRO, would satisfy science and application requirements of the US and India. A dual-frequency fully polarimetric SAR with the potential for global coverage every 12 days would offer unprecedented capability that researchers could exploit in new and exciting ways.

The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROIPAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quad-polarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.

We present a simple approach to jointly exploit stripmap and ScanSAR acquisitions to generate differential synthetic aperture radar interferometry (DInSAR) time series. In particular, we extend the capability of the Small BAseline Subset (SBAS) approach to compute deformation time series from a set of stripmap images by filling possible temporal gaps in the available SAR data sequence with ScanSAR acquisitions. The starting point of our approach is the raw data focusing step, which is properly carried out to align the characteristics of the ScanSAR images to those of the stripmap ones. To achieve this task, we exploit stripmap processing codes to focus both SAR data types, the ScanSAR ones being processed on a burst-by-burst basis, accounting also for possible differences of the pulse repetition frequency with respect to that of the stripmap data. The coherent combination of the focused bursts generates phase-preserved ScanSAR images with the same output geometry and pixel spacing as the stripmap ones. This allows a straightforward implementation of the next steps of the SBAS processing chain, including the interferogram generation operation. In this case, we concentrate on a selection of small baseline (SB) stripmap–stripmap multilook interferograms identified through a Delaunay triangulation, which are complemented with a set of hybrid SB stripmap–ScanSAR interferograms. This interferogram selection permits us to develop an effective phase unwrapping algorithm based on a two-step processing strategy. Finally, the whole data set of unwrapped interferograms is inverted through the SBAS technique to retrieve the final deformation time series, including both stripmap and ScanSAR data. The proposed stripmap–ScanSAR SBAS processing approach is particularly attractive because it is very easy to implement since it requires only limited modifications with respect to the conventional stripmap-based SBAS algorithm. Our approach has been applied to descending and ascending hybrid stripmap–ScanSAR data sets of Envisat/ASAR C-band acquisitions from the Big Island of Hawaii. In spite of not including any common-band azimuthal filtering, which would account for the ScanSAR burst spectral properties at the expense of the algorithm simplicity, the presented results show that we may retrieve DInSAR time series with an accuracy ranging between 5 and 10 mm, consistent with previous C-band data analyses using only stripmap data.

Until recently the coarse resolution of topographic mapping acted as a break on understanding the forces and processes that shape the Earth's surface. However, active surface deformation is an important indicator for the earth crustal dynamics since it is directly linked to earthquakes, volcanic eruptions and landslides. Both airborne laser scanning systems (LiDAR) and spaceborne interferometric synthetic aperture radars (InSAR) have provided valuable information for many case studies requiring highresolution characterization of ground movement in relatively large areas to assess the threat and impact of natural hazards especially for volcanic eruptions. The Piton de la Fournaise volcano (Reunion Island, France) is one of the most active basaltic shield volcanoes in the world. It has reached an anomalous activity level in the past years with a major eruption occurring in April 2007. In this paper, we explore the statistical, spatial and temporal behavior of the L-Band backscattering coefficient at both HH and HV polarizations over different type of terrains in the Fournaise lava field as a function of the LiDAR intensity data. The correlation will be used in setting empirical models to correct for the L-Band phase distortion on ash and rough surfaces in volcanic terrains.

Tandem-L is a proposal for an innovative interferometric and polarimetric radar mission that enables the systematic monitoring of dynamic processes on the Earth surface. Important mission objectives are global forest height and biomass inventories, large scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The innovative mission concept and the high data acquisition capacity of Tandem-L provide a unique data source to observe, analyze and quantify the dynamics of a wide range of mutually interacting processes in the bio-, litho-, hydro- and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics.

We investigate the displacement phenomena affecting Mauna Loa and Kïlauea volcanoes at Big Island (Hawaii, USA), by applying an advanced ScanSAR-to-stripmap differential Synthetic Aperture Radar Interferometry (InSAR) approach. The implemented method, based on the application of the well-known Small BAseline Subset (SBAS) technique, allows the generation of LOS mean deformation velocity maps and corresponding time series, leading us to characterize the complex deformation of Mauna Loa and Kïlauea volcanoes. The presented analysis relies on the use of a SAR dataset composed by 49 ASAR ENVISAT satellite images, relevant to both stripmap and ScanSAR operational modes, acquired on descending orbits (track 200) from January 2003 to September 2008. Moreover, in order to assess the quality of the proposed combined ScanSAR-to-stripmap approach, we perform a comparison between the achieved DInSAR results and the LOS-projected GPS displacement measurements.

The National Research Council Earth Science Decadal Survey, Earth Science Applications from Space, recommends that DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice), an integrated L-band InSAR and multibeam Lidar mission, launch in the 2010-2013 timeframe. The mission will measure surface deformation for solid Earth and cryosphere objectives and vegetation structure for understanding the carbon cycle. InSAR has been used to study surface deformation of the solid Earth and cryosphere and more recently vegetation structure for estimates of biomass and ecosystem function. Lidar directly measures topography and vegetation structure and is used to estimate biomass and detect changes in surface elevation. The goal of DESDynI is to take advantage of the spatial continuity of InSAR and the precision and directness of Lidar. There are several issues related to the design of the DESDynI mission, including combining the two instruments into a single platform, optimizing the coverage and orbit for the two techniques, and carrying out the science modeling to define and maximize the scientific output of the mission.

The National Research Council's Decadal Survey for Earth Science identified InSAR (Interferometric Synthetic Aperture Radar) observations among the highest priorities for new NASA Earth missions. A system making observations required by the solid Earth, vegetation, and ice/climate science communities is recommended. In response, analyses are underway to evaluate efficient combinations of science objectives and mission/instrument scenarios. The InSAR component can be satisfied by a new radar instrument concept capitalizing on existing technology and hardware, including a large commercial mesh reflector antenna and transmit/receive modules developed for the UAVSAR airborne radar. This InSAR system satisfies key science objectives and addresses several shortcomings of existing InSAR capable satellites. To reduce temporal decorrelation, L-Band (23 cm) wavelength is used. A 300 km wide-swath scanSAR mode with 8 day repeat enhances study of ice dynamics, pre/post earthquake deformation, volcano monitoring, and other dynamic phenomena. With a minor orbit change, global biomass surveys are possible using multipolarization. Key challenges are involve scheduling to optimize conflicting observational requirements of various science communities served.

Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

An interferometric radar technique for topographic mapping of surfaces promises a high-resolution approach to the generation of digital elevation models. The authors present analyses of data collected by the synthetic aperture radar instrument on-board the ERS-1 satellite on successive orbits. Use of a single satellite in a nearly repeating orbit is attractive for reducing cost and spaceborne hardware complexity; also it permits inference of changes in the surface from the correlation properties of the radar echoes. The data have been reduced to correlation maps and digital elevation models. The correlation maps show that temporal correlation decreases significantly with time, but not necessarily at a constant well-defined rate, likely depending on environmental factors. When correlation among passes remains high, however, it is possible to form digital elevation models. Analyses of noise expected in ERS-1 interferometric data collected over Alaska and the southwestern United States indicate that maps with relative errors less than 5 m rms are possible in some regions. However, orbit uncertainties imply that tie points are required in order to reduce absolute height errors to a similar magnitude. The authors find that about 6 tie points per 40/spl times/40 km scene with 5 m rms or better height accuracy are needed to keep systematic map height errors below 5 m rms. The performance of the ERS-1 radar system for topographic applications, though useful for a variety of regional and local discipline studies, may be improved with respect to temporal decorrelation errors and absolute height acuity by modifying the orbit repeat period and incorporating precise orbit determination techniques. The resulting implementation will meet many, but not all, objectives of a global mapping mission.>