Radar Interferometric Studies of the Greenland Ice Sheet
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Engineering topics
Publications and source records attributed to Rignot, Eric.
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The first topographic and ice-motion maps of the northwestern flank of Hielo Patagonico Norte (HPN, northern Patagonia Icefield), in Chile, were produced using satellite synthetic-aperture interferometric radar data acquired by NASA's Spaceborne Imaging Radar C instrument in October 1994. The topographic map has a IO m vertical precision with a 30 m horizontal spacing, which should be sufficient to serve as a reference for monitoring future mass changes of the icefield. The ice-motion map is accurate to within 4 mm/ d (or 1/ ma). The radar-derived surface topography and ice velocity are used to estimate the ice discharge from the accumulation area of four outlet glaciers, and the calving flux and mass balance of Glaciar San Rafael. The results demonstrate the use of SAR interferometry for monitoring glaciological parameters on a spatial and temporal scale unattainable by any other means.
Interferometric radar observations of Glaciar San Rafael, Chile, were collected in October 1994 by NASA's Spaceborne Imaging Radar C (SIR-C) at both L- (24cm) and C-band frequency (5.6cm), with vertical transmit and receive polarization. The C-band data did not yield good geophysical products, because the temporal coherence of the signal was significantly reduced after 24h. The L-band data were, however, successfully employed to map the surface topography of the icefield with a 10m uncertainty in height, and measure ice velocity with a precision of 4 mm/d or 1.4 m/a. The corresponding error in strain rates is 0.05/a at a 30 m horizontal spacing. The one-dimensional interferometric velocities were subsequently converted to horizontal displacements by assuming a flow direction and complemented by feature-tracking results near the calving front. The results provide a comprehensive view of the ice-flow dynamics of Glaciar San Rafael. The glacier has a core of rapid flow, 4.5 km in width and 3.5 degrees in average slope,surrounded by slower moving ice, not by rock. Ice velocity is 2.6 m/d or 0.95 km/a near the equilibrium line altitude (1200m), increasing rapidly before the glacier enters the narrower terminal valley, to reach 17.5 m/d or 6.4 km/a at the calving front. Strain rates are dominated by lateral shearing at the glacier margins (0.4-0.7/a), except for the terminal-valley section, where longitudinal strain rates average close to 1/a. This spectacular longitudinal increase in ice velocity in the last few kilometers may be a fundamental feature of tidewater glaciers.
Mapping forest types in the boreal ecosystem in an integrated part of any modeling excercise of biogeophysical processes characterizing the interaction of forest with the atmosphere. In this paper, we report the results of the land cover classification of the SAR data acquired during the BOREAS (BOReal Ecosystem Atmospheric Study) intensive field campaigns over the modeling sub-grid of the southern study area in Saskatchewan , Canada. A Bayesian-maximum-a-posteriori classifier has been applied on the NASA/JPL AIRSAR images covering the region during the peak of the growing season in July, 1994.
Covers problems with existing data collected with high-resolution optical sensors. They say active microwave sensors could complement other sensors in getting through things like cloud cover. They analyzed SIR-C data in combination with Landsat TM data, a 9-year time series of SPOT XS data, and a preliminary field survey. They report findings and draw conclusions, including that SARs operating at long radar wavelengths, with both like and cross-polarizations, are needed for tropical deforestation studies.
Covers SIR-C (Spaceborne Imaging Radar C) SAR (Synthetic Aperture Radar) imaging of the 1988 Yellowstone National Forest fires. Discusses some of the images and data collected, and some conclusions drawn from them about both the fires, and SIR-C SAR imaging capabilities.
Over the floating section of a tide-water glacier, single radar intererograms are difficult to use because the long-term steady motion of the ice is intermixed with the tidal vertical motion of the glacier. With multiple interferograms, it is however possible to isolate the tidal signal and remove it from the single interferograms to estimate the ice velocities. The technique is applied to ERS-1 synthetic aperture radar (SAR) images of Petermann Gletscher, north Greenland.
Repeat-pass, interferometric, radar observations of tropical rain-forests colected by the Shuttle Imaging Radar SIR-C in Rondonia, Brazil reveal C-band (5.6-cm) radar signals completely decorelate over forest, whereas L-band (24-cm) signals remain strongly coherent over the intire landscape. At L-band, the difference in elevation between forest and clearings is within the 8-m height noise of the data. Atmospheric delays are large, however, forming kilometer-sized anomalies with a with a 1.2-cm rms one way. Radar interferometric studies of humid tropics miust be conducted at long radar wavelengths, with kilometric baselines or with two antennas operating simultaneously.
SAR-REG software package registers synthetic-aperture-radar (SAR) image data to common reference frame based on manual tie-pointing. Image data can be in binary, integer, floating-point, or AIRSAR compressed format. For example, with map of soil characteristics, vegetation map, digital elevation map, or SPOT multispectral image, as long as user can generate binary image to be used by tie-pointing routine and data are available in one of the previously mentioned formats. Written in FORTRAN 77.
POLMAP computer program classifies multifrequency, polarimetric synthetic-aperture-radar (SAR) data into homogeneous classes of radar back-scatter. Software implements maximum-a-posteriori classification scheme. Performs by use of simulated-annealing procedure, takes much longer to compute than ICM solution, but it maximizes posteriori distribution of classes for whole scene. Input statistics of image classes must be created by some independent means, and supplied as input to POLMAP. Used as input to image processing application program. Written in FORTRAN 77.
A number of planetary objects exhibit large radar reflectivity and polarization ratios, and more recently, a similar behavior has been observed over a vast portion of the Earth's surface: the percolation facies of the Greenland Ice Sheet. Surface-based ranging radar data and snow stratigraphy studies demonstrated that the radar properties of that portion of Greenland are caused by enhanced scattering from massive, large, solid-ice bodies buried in the top few meters of the dry, cold, clean snowy surface of the ice sheet and created by seasonal melting and refreezing events. Here, we model the icy inclusions as randomly oriented, discrete, noninteracting, dielectric cylinders embedded in a transparent snow medium. An exact analytical solution is used to compute the scattered field from the cylinders. Using this model, we correctly predict the polarimetric radar observations gathered by an airborne imaging system at three wavelengths (5.6, 24, and 68 cm), between 19 deg and 65 deg incidence angle. The diameter and number density of the cylinders that are inferred from the radar data using the model are consistent with in situ observations of the icy inclusions. The large radar reflectivity and polarization ratios are interpreted as arising internal reflections of the radar signals in the icy inclusions that first-order external reflection models fail to predict. The results compare favorably with predictions from the coherent backscatter or weak localization theory and may provide a complementary framework for interpreting exotic radar echoes from other planetary objects.
During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.
In recent years, monitoring vegetation biomass over various climate zones has become the primary focus of several studies interested in assessing the role of the ecosystem responses to climate change and human activities. Airborne and spaceborne Synthetic Aperture Radar (SAR) systems provide a useful tool to directly estimate biomass due to its sensitivity to structural and moisture characteristics of vegetation canopies. Even though the sensitivity of SAR data to total aboveground biomass has been successfully demonstrated in many controlled experiments over boreal forests and forest plantations, so far, no biomass estimation algorithm has been developed. This is mainly due to the fact that the SAR data, even at lowest frequency (P-band) saturates at biomass levels of about 200 tons/ha, and the structure and moisture information in the SAR signal forces the estimation algorithm to be forest type dependent. In this paper, we discuss the development of a hybrid forest biomass algorithm which uses a SAR derived land cover map in conjunction with a forest backscatter model and an inversion algorithm to estimate forest canopy water content. It is shown that unlike the direct biomass estimation from SAR data, the estimation of water content does not depend on the seasonal and/or environmental conditions. The total aboveground biomass can then be derived from canopy water content for each type of forest by incorporating other ecological information. Preliminary results from this technique over several boreal forest stands indicate that (1) the forest biomass can be estimated with reasonable accuracy, and (2) the saturation level of the SAR signal can be enhanced by separating the crown and trunk biomass in the inversion algorithm. We have used the JPL AIRSAR data over BOREAS southern study area to test the algorithm and to generate regional scale water content and biomass maps. The results are compared with ground data and the sources of errors are discussed. Several SAR images in synoptic modes are used to generate the parameter maps. The maps are then combined to generate mosaic maps over the BOREAS modeling grid.
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In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived form sensor position and Digital Elevation Model (DEM) data must always be considered.
Sensitivity of radar backscatter to the dielectric and geometric character of forested regions suggests significant changes in backscatter are expected with season due to freezing temperatures, snow, wind, leaf fall, and drought. The first European Remote Sensing Satellite, ERS-1, offers a unique opportunity to monitor a complete seasonal cycle for the Alaskan taiga forest ecosystem with synthetic aperture radar. During the 3-day repeat Commissioning Phase of ERS-1, from August 1991to December 1991, ERS-1 SAR data were collected in the region of Manley Hot Springs, Alaska, along the Tanana River, west of Fairbanks. In parallel with the SAR data collection, meteorological data from three weather stations positioned in three forest stands were collected continuously along with in situ measurements of the dielectric and moisture properties of the canopy and of ground cover which were collected during each overflight. The in situ data were collected in floodplain forest stands dominated by balsam poplar, white spruce, and black spruce. These results from the Commissioning Phase as well as preliminary results from the 35-day Repeat Phase will be presented.
We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.