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The Alaska SAR Facility (ASF) situated at the University of Alaska Fairbanks (UAF) is currently undergoing an extensive development effort to increase its capabilities in handling new sensors and in the areas of throughput, data handling, archive, and distribution [15].
The Alaska SAR Facility is currently undergoing an upgrade development effort to accommodate the upcoming ERS-2 and RADARSAT satellites.
The results of a calibration analysis performed on ERS-1 synthetic aperture radar (SAR) images produced by the Alaska SAR facility (ASF) are presented, together with some preliminary results on change detection on the Alaskan north slope derived from the same images. Image quality, geometric and radiometric fidelity, and repeat pass radiometric stability have all been determined to be satisfactory. A calibration workstation has been designed and implemented for use in operational data quality analysis of ASF data products. Higher-level data analysis programs have been developed for interferometry, texture analysis, and change detection.
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This paper examines the requirements and the design considerations of the Synthetic Aperture Radar (SAR) ground data system for the Alaska SAR Facility (ASF) at the University of Alaska in Fairbanks (UAF) for the new era of Radarsat/ERS-2 missions. These include a new data acquisition planning capability to manage more satellites with global planning and to manage more than one instrument mode; a new archive strategy that is cheaper, faster, and better; a product generation system to produce data on demand and to produce data for the varied instruments and modes; and a product verification ability for the new and old products. In response to these new functional requirements, JPL is using a design approach that emphasizes an open systems, client/server architecture based on industry standards and commercial off-the-shelf technology.
Radarsat is a Canadian polar orbiting remote sensing satellite scheduled for launch in September 1995. Its lone instrument on-board is a synthetic aperture radar (SAR) that is capable of operating in a number of imaging modes including the first operational ScanSAR mode. As one of the data reception, processing and archive facility for Radarsat data, Alaska SAR Facility (ASF) has responded to its Science users by establishing a Radarsat processing system to handle the data processing of all Radarsat modes. This task involves enhancements to the high throughput hardware based Alaska SAR Processor (ASP) to handle standard mode Radarsat data in addition to its existing ERS and JERS capabilities, the addition of the new ScanSAR Processor (SSP) to process the Radarsat ScanSAR mode data, and the introduction of a Precision Processor (PP) to accommodate the special Radarsat modes such as fine resolution and wide swath. For raw data ingestion and distribution to the appropriate SAR processor, a new Control Processor (CP) and Raw Data Scanner (RDS) subsystem is also incorporated.
The Alaska SAR Facility (ASF) located at the University of Alaska Fairbanks (UAF) has been in operations since 1991 serving as a key data acquisition, processing, archive and distribution center for a number of polar orbiting SAR (synthetic aperture radar) satelittes including ERS 1/2 and JERS-1.
The paper describes the architecture of the Alaska SAR Facility (ASF) at Fairbanks, being developed to generate science data products for supporting research in sea ice motion, ice classification, sea-ice-ocean interaction, glacier behavior, ocean waves, and hydrological and geological study areas. Special attention is given to the individual substructures of the ASF: the Receiving Ground Station (RGS), the SAR Processor System, and the Interactive Image Analysis System. The SAR data will be linked to the RGS by the ESA ERS-1 and ERS-2, the Japanese ERS-1, and the Canadian Radarsat.
The timing of freeze-up and break-up of Arctic lake ice is a potentially useful environmental indicator that could be monitored using SAR. In order to do this, it is important to understand how the properties and structure of the ice during its growth and decay affect radar backscatter and thus lake ice SAR signatures. The availability of radiometrically and geometrically calibrated digital SAR data time series from the Alaska SAR Facility has made it possible for the first time to quantify lake ice backscatter intensity (sigma(sup o)) variations. This has been done for ice growing on shallow tundra lakes near Barrow, NW Alaska, from initial growth in September 1991 until thawing and decay in June 1992. Field and laboratory observations and measurements of the lake ice were made in late April 1992. The field investigations of the coastal lakes near Barrow confirmed previous findings that, (1) ice frozen to the lake bottom had a dark signature in SAR images, indicating weak backscatter, while, (2) ice that was floating had a bright signature, indicating strong backscatter. At all sites, regardless of whether the ice was grounded or floating, there was a layer of clear, inclusion-free ice overlaying a layer of ice with dense concentrations of vertically oriented tubular bubbles. At some sites, there was a third layer of porous, snow-ice overlaying the clear ice.
This paper summarizes the investigation of SAR image data compression for an on-line archive data distribution system. This system is planned for the ground processing system of Alaska SAR Facility (ASF) and Shuttle Imaging Radar (SIR-C). The objective of the SAR image data compression is to enable the data archive system to provide the remote users a large data base with good image quality, short response time, low transfer cost, and minimal decoding complexity. The requirements and limitations of the on-line archive data distribution system are presented. The effects of SAR image data characteristics on data compression are addressed. The users' survey results suggest that compression ratios between 10:1 and 20:1 appear suitable. Based on the algorithm evaluation results, the two-level tree-searched vector quantization technique has been recommended as the SAR image data compression algorithm for the on-line archive data distribution system.
SAR data have been used to study sea ice with respect to its motion and formation/deformation. With the prospect of the Alaska SAR Facility development in the near future, there is a great need for robust and efficient sea-ice analysis techniques. This paper presents a sea-ice motion analysis technique that can be used for: (1) local motion analysis of a selected ice patch, and (2) a global ice motion over the entire image area. In order to meet the operational speed requirement (over fifty images per day) a sea-ice motion analysis technique has been developed which requires very little human interaction. The proposed technique uses a subset of easily distinguishable features to predict global motion characteristics. The developed technique is applied to two pairs of SEASAT SAR images, one pair with a minor motion of 'ice pack' and another with a larger and discontinuous motion of 'fast ice'. The new approach enables the development of a set of computer-aided tools for feature selection and registration and the implementation of an optimal search strategy for automatic template matching via a motion prediction model.
The evolution of SAR digital data processing and management ground systems developed at the JPL for earth science missions is discussed. Attention is given to the SAR ground data system requirements, the early data processing systems, the Seasat SAR system, and the SIR-B data processing system. Special consideration is given to two currently operational SAR data systems: the JPL aircraft SAR processing system that flies on the NASA DC-8 and the Alaska SAR Facility at Fairbanks.
Present techniques of ice tracking make use both of cross-correlation and of edge tracking, the former being more successful in heavy pack ice, the latter being critical for the broken ice of the pack margins. Algorithms must assume some constraints on the spatial variations of displacements to eliminate fliers, but must avoid introducing any errors into the spatial statistics of the measured displacement field. We draw our illustrations from the implementation of an automated tracking system for kinematic analyses of ERS-1 and JERS-1 SAR imagery at the University of Alaska - the Alaska SAR Facility's Geophysical Processor System. Analyses of the ice kinematic data that might have some general interest to analysts of cloud-derived wind fields are the spatial structure of the fields, and the evaluation and variability of average deformation and its invariants: divergence, vorticity and shear. Many problems in sea ice dynamics and mechanics can be addressed with the kinematic data from SAR.
The European Space Agency Remote Sensing Satellite (ERS-1) satellite carried a C-band synthetic aperture radar (SAR) to study the earth's polar regions. The radar returns from sea ice can be used to infer properties of ice, including ice type. An algorithm has been developed for the Alaska SAR facility (ASF)/Geophysical Processor System (GPS) to infer ice type from the SAR observations over sea ice and open water. The algorithm utilizes look-up tables containing expected backscatter values from various ice types. An analysis has been made of two overlapping strips with 14 SAR images. The backscatter values of specific ice regions were sampled to study the backscatter characteristics of the ice in time and space. Results show both stability of the backscatter values in time and a good separation of multiyear and first-year ice signals, verifying the approach used in the classification algorithm.
The synthetic aperture radar (SAR) data processing algorithm used by the Alaska SAR Facility (ASF) for the European Space Agency's first Remote-Sensing Satellite (E-ERS-1) SAR data are examined. Preprocessing highlights two features: signal measurement, which includes signal-to-noise ratio, replica measurement, and noise measurement; and Doppler measurement, which includes clutter lock and autofocus. The custom pipeline architecture performs the main processing with controls at the input interface, range correlator, corner-turn memory, azimuth correlator, and multi-look memory. The control software employs a flexible control scheme. The Committee on Earth Observation Satellites (CEOS) format encapsulates the ASF products. System performance for SAR image processing of E-ERS-1 data is reviewed.
The Alaska SAR Facility (ASF) collects, processes, archives, and distributes data from synthetic aperture radar (SAR) satellites in support of scientific research. ASF has been in operation since 1991 and presently has an archive of over 100 terabytes of data. ASF is performing an analysis of its magnetic tape storage system to ensure long-term preservation of this archive. Future satellite missions have the possibility of doubling to tripling the amounts of data that ASF acquires. ASF is examining the current data systems and the high volume storage, and exploring future concerns and solutions.
We present nearly coincident observations of backscattering from the Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and of near-surface temperature from six drifting buoys in the Beaufort Sea, showing that the onset of melting in snow on multiyear sea ice is clearly detectable in the SAR data. Melt onset is marked by a clean, steep decrease in the backscattering cross section of multiyear ice at 5.3 GHz and VV polarization. We investigate the scattering physics responsible for the signature change and find that the cross section decrease is due solely to the appearance of liquid water in the snow cover overlying the ice. A thin layer of moist snow is sufficient to cause the observed decrease. We present a prototype algorithm to estimate the date of melt onset using the ERS 1 SAR and apply the algorithm first to the SAR data for which we have corresponding buoy temperatures. The melt onset dates estimated by the SAR algorithm agree with those obtained independently from the temperature data to within 4 days or less, with the exception of one case in which temperatures oscillated about 0 C for several weeks. Lastly, we apply the algorithm to the entire ERS 1 SAR data record acquired by the Alaska SAR Facility for the Beaufort Sea north of 73 deg N during the spring of 1992, to produce a map of the dates of melt onset over an area roughly 1000 km on a side. The progression of melt onset is primarily poleward but shows a weak meridional dependence at latitudes of approximately 76 deg-77 deg N. Melting begins in the southern part of the study region on June 13 and by June 20 has progressed to the northermost part of the region.
Spatial and temporal backscatter intensity (sigma(sup o)) variations from ice growing on shallow lakes during winter 1991-92 near Barrow, NW Alaska, have been quantified for the first time using ERS-I C-band SAR data acquired at the Alaska SAR Facility. A field and laboratory validation program, including measurements of the thickness and structure-stratigraphy of the ice, indicates that sigma(sup o) values are strongly dependent on whether the ice freezes to the lake bottom, or remains afloat. Backscatter intensity decreases significantly when the ice grounds on the bottom. Strong backscatter from floating ice is attributed to a specular ice-water interface and vertically oriented tubular bubbles. During the spring thaw, backscatter undergoes a reversal; sigma(sup o) values from ice that was grounded increase, while sigma(sup o) values from ice that was afloat decrease. This phenomenon has not previously been reported.