Silicic magmatic systems are the most dangerous volcanoes on Earth, capable of large and catastrophic eruptions, yet their low eruptive frequency makes it challenging to interpret their short-term unrest. Here we present a decade-plus analysis that integrates, for the first time, time series of satellite interferometric synthetic aperture radar (InSAR) surface deformation and satellite thermal infrared edifice-scale surface warming at a large silicic system: Domuyo volcano, in Argentina. We find that deformation and warming are highly correlated, and depending on the sign and lag between the time series, either shallow sealing or magma influx could drive Domuyo’s ongoing inflation (~ 0.15 m/year; from an InSAR-derived tabular source, ~ 11 × 8 × 1 km; ~ 6.5 km depth; ~ 0.037 km3/year volume-change rate) and warming (0.3–0.4 °C/year). This study shows the potential that combined satellite surface deformation and edifice-scale surface warming time series have on assessing the physical mechanisms of silicic volcanic systems and for constraining deterministic models.

The NASA-ISRO Synthetic Aperture Radar (NISAR) mission will carry L-band and S-band SAR instruments, each with a 240 km swath width. The L-band instrument has the capability to map the entire Earth's land and ice covered surfaces from both ascending and descending orbit positions, continuously in an exact 12 day repeating cycle, given temporally dense (better than 6-day on average) sampling of Earth over the life of the mission. To achieve this swath coverage without loss of resolution or polarimetric capability, NISAR uses a reflector-feed based antenna system with scan-on-receive ("SweepSAR") capability. One of the characteristics of SweepSAR is that the pulse repetition interval of the radar is shorter than the echo receive window for the 240 km swath. Therefore, transmit events occur during the receive window and the receivers must be blanked periodically, creating gaps in the coverage. For fixed pulse rate operations, these gaps are persistent strips of blanked ranges. NISAR is being designed to allow variation of the pulse rate in order to spread out these gaps throughout the synthetic aperture, but processing these variably-acquired data is more challenging and can lead to compromises in image quality. At the highest level NISAR places requirements on science measurements rather than image quality, so there is often a debate among science and engineering team members as to whether to operate with fixed pulse rates - leading to range strips consistently blank from cycle to cycle but with optimal image quality - or to vary the pulse rate, fill in the gaps, and live with degraded image quality. NISAR's performance team has shown that science requirements can be met with the gaps. However, scientists are eager for maximum and coverage within a swath. A companion paper (Villano et al.) describes image quality for NISAR using a variable PRF approach, but does not go further to science requirements. This paper explores the issues when gaps are present in the swath due to fixed PRF operations.

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”.

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.

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 25 April 2015 M-w 7.8 Gorkha earthquake caused more than 8000 fatalities and widespread building damage in central Nepal. The Italian Space Agency's COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area four days after the earthquake and the Japan Aerospace Exploration Agency's Advanced Land Observing Satellite-2 SAR satellite for larger area nine days after the main-shock. We used these radar observations and rapidly produced damage proxy maps (DPMs) derived from temporal changes in Interferometric SAR coherence. Our DPMs were qualitatively validated through comparison with independent damage analyses by the National Geospatial-Intelligence Agency and the United Nations Institute for Training and Research's United Nations Operational Satellite Applications Programme, and based on our own visual inspection of DigitalGlobe's World-View optical pre- versus postevent 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 Lunar CRater Observations and Sensing Satellite (LCROSS) mission impacted a spent Centaur rocket stage into a permanently shadowed region near the lunar south pole. The Sheperding Spacecraft (SSC) separated ∼9 hours before impact and performed a small braking maneuver in order to observe the Centaur impact plume, looking for evidence of water and other volatiles, before impacting itself. This paper describes the registration of imagery of the LCROSS impact region from the mid- and near-infrared cameras onboard the SSC, as well as from the Goldstone radar. We compare the Centaur impact features, positively identified in the first two, and with a consistent feature in the third, which are interpreted as a 20 m diameter crater surrounded by a 160 m diameter ejecta region. The images are registered to Lunar Reconnaisance Orbiter (LRO) topographical data which allows determination of the impact location. This location is compared with the impact location derived from ground-based tracking and propagation of the spacecraft’s trajectory and with locations derived from two hybrid imagery/trajectory methods. The four methods give a weighted average Centaur impact location of −84.6796°, −48.7093°, with a 1 σ uncertainty of 115 m along latitude, and 44 m along longitude, just 146 m from the target impact site. Meanwhile, the trajectory-derived SSC impact location is −84.719°, −49.61°, with a 1 σ uncertainty of 3 m along the Earth vector and 75 m orthogonal to that, 766 m from the target location and 2.803 km south-west of the Centaur impact. We also detail the Centaur impact angle and SSC instrument pointing errors. Six high-level LCROSS mission requirements are shown to be met by wide margins. We hope that these results facilitate further analyses of the LCROSS experiment data and follow-up observations of the impact region.

We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms.

During the 2008-2009 year, the Goldstone Solar System Radar was upgraded to support radar mapping of the lunar poles at 4 m resolution. The finer resolution of the new system and the accompanying migration through resolution cells called for spotlight, rather than delay-Doppler, imaging techniques. A new pre-processing system supports fast-time Doppler removal and motion compensation to a point. Two spotlight imaging techniques which compensate for phase errors due to (i) out of focus-plane motion of the radar and (ii) local topography, have been implemented and tested. One is based on the polar format algorithm followed by a unique autofocus technique, the other is a full bistatic time-domain backprojection technique. The processing system yields imagery of the specified resolution. Products enabled by this new system include topographic mapping through radar interferometry, and change detection techniques (amplitude and coherent change) for geolocation of the NASA LCROSS mission impact site.

Mapping the Moon's topography using Earth based radar interferometric measurements by the Goldstone Solar System Radar (GSSR) has been done several times since the mid 1990s. In 2008 we reported at this conference the generation of lunar topographic maps having approximately 4 m height accuracy at a horizontal posting of 40 m. Since then GSSR radar has been improved to allow 40 MHz bandwidth imaging and consequently obtained images and interferograms with a resolution of about 4 m in range by 5 m in azimuth. The long synthetic aperture times of approximately 90 minutes in duration necessitated a migration from range/Doppler image formation techniques to spotlight mode processing and autofocusing methods. The improved resolution imagery should permit the generation of topographic maps with a factor of two better spatial resolution with about same height accuracy. Coupled the with the recent availability of new lidar topography maps of the lunar surface made by orbiting satellites of Japan and the United States the geodetic control of the radar generated maps products can be improved dramatically. This paper will discuss the hardware and software improvements made to the GSSR and present some of the new high resolution products.

NASApsilas long-term exploration goals include a return to manned missions to the Moon that will culminate in a permanent manned station on the Moon. Prior to embarking on such a mission, a series of precursor unmanned robotic missions are required to ascertain the best locations for manned exploration or a permanent lunar base. These precursor missions will consist of both orbital missions, such as the Lunar Reconnaissance Orbiter (LRO) and missions for which probes will be landed on the lunar surface. The south polar region of the Moon has attracted much recent attention due to the possibility of significant amounts of frozen water being trapped in permanently shadowed regions in craters in that region. In order to better plan and determine the optimal landing sites for such probes, as much a priori information as possible on the lunar south polar region is desired. Of particular importance from a landing survivability and accessible exploration region perspective is knowledge concerning the lunar topography. This paper will discuss processing of recently acquired radar interferometric data from Earth-based radar, namely the NASA Goldstone Solar System Radar, into topographic maps.

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python-based software framework that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

For spherical atmospheres of ideal gases in hydrostatic equilibrium, the barometric equation has two kinds of functional solutions when gravity and temperature are assumed to be proportional to arbitrary powers of radius. Normalized pressure is given either by a restricted solution, which is a power of normalized radius, or more broadly by the exponential of a function of radius. Atmospheric models have been based on several special cases of the exponential type. In some applications, however, the power-of-radius model may be more versatile and useful since all of the functions that describe atmospheric structure (temperature, pressure, density, pressure scale height, and density scale height) and content (density integrated along semi-infinite vertical and infinite tangential straight paths) are simple powers of radius. Starting from the exponential solution for pressure, the general result for vertical content involves more esoteric functions or is infinite while the tangential content, when finite, is not expressible as a standard function. For particular cases where the tangential content or its derivatives are finite and tractable, a mix of modified Bessel and modified Struve functions can come into play. There is a special interest here in the simpler power-law models as building blocks for a superposition-of-models technique that is under development. Preliminary results involving the superposition of two model densities have demonstrated that it is feasible to illustrate, study, and elucidate connections between distinctive atmospheric structure and the resulting features of remote sensing measurements as obtained in stellar and spacecraft occultation experiments. This paper is the first of several planned on this subject. It is limited to a consideration of the properties of the power-of-radius and exponential types of models for representing atmospheric structure and content.