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Radar technology is used extensively by NASA for remote sensing of the Earth and other Planetary bodies. In this case study, we learn about different computational concepts for processing radar data. In particular, we learn how to correct a slanted radar image by projecting it on the surface that was sensed by a radar instrument.
The capabilities of using remote sensing data, and in particular multifrequency/multipolarization SAR data, like AIRSAR, for the retrieval of surface parameters, depend considerably on the specificity of each application. The potentials, and limitations, of SAR data in ecological investigations are well known. Because the chemistry is a major component in such studies and because of the almost lacking chemical information at the wavelengths of SAR data, the capabilities of using SAR-derived information in such studies are considerably limited. However, in the case of surface energy/water balance studies, the determination of the amount of water content, both in the soil and in the plants, is a major component in all modeling approaches. As the information about water content is present in the SAR signal, then the role of SAR data in studies where water content is to be determined becomes clearly predominant. Another situation where the role of SAR data becomes dominant over other remote sensing systems is the case of dense canopies. Because of the penetration capabilities of microwave data, which is especially superior as compared to optical data, information about the canopy as a whole and even the underlying soil is contained in the SAR data, while only the top canopy provides the information content in the case of optical data. In the case of relatively dense canopies, as has been demonstrated in this study, such different penetration capabilities provide very different results in terms of the derived total canopy water content, for instance. However, although all such capabilities are well known, unfortunately there are also well known limitations. Apart from calibration-related aspects (that we will not consider in this study), and apart from other intrinsic problems (like image noise, topographic corrections, etc.) which also significantly affect the derived results, we will concentrate on the problem of extracting information from the data. Even at this level, methods are still not fully well established, especially over vegetation-covered areas. In this paper, an algorithm is described which allows derivation of three fundamental parameters from SAR data: soil moisture, soil roughness and canopy water content, accounting for the effects of vegetation cover by using optical (Landsat) data as auxiliary. Capabilities and limitations of the data and algorithms are discussed, as well as possibilities to use these data in energy/water balance modeling studies. All the data used in this study were acquired as part of the Intensive Observation Period in June-July 1991 (European Multisensor Aircraft Campaign-91), as part of the European Field Experiment in a Desertification- threatened Area (EFEDA), a European contribution to the global-change research sponsored by the IGBP program (Bolle et al., 1993).
This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.
In preparation for the launch of SIR-C/X-SAR and design studies for future orbital SAR, a program has made considerable progress in the development of an SAR terrain classifier and algorithms for quantification of biophysical attributes. The goal of this program is to produce a generalized software package for terrain classification and estimation of biophysical attributes and to make this package available to the larger scientific community. The basic elements of the SAR (Synthetic Aperture Radar) terrain classifier are outlined. An SAR image is calibrated with respect to known system and processor gains and external targets (if available). A Level 1 classifier operates on the data to differentiate: urban features, surfaces and tall and short vegetation. Level 2 classifiers further subdivide these classes on the basis of structure. Finally, biophysical and geophysical inversions are applied to each class to estimate attributes of interest. The process used to develop the classifiers and inversions is shown. Radar scattering models developed from theory and from empirical data obtained by truck-mounted polarimeters and the JPL AirSAR are validated. The validated models are used in sensitivity studies to understand the roles of various scattering sources (i.e., surface trunk, branches, etc.) in determining net backscatter. Model simulations of sigma (sup o) as functions of the wave parameters (lambda, polarization and angle of incidence) and the geophysical and biophysical attributes are used to develop robust classifiers. The classifiers are validated using available AirSAR data sets. Specific estimators are developed for each class on the basis of the scattering models and empirical data sets. The candidate algorithms are tested with the AirSAR data sets. The attributes of interest include: total above ground biomass, woody biomass, soil moisture and soil roughness.
Artisanal and small-scale gold mining (ASGM) is responsible for a large fraction of deforestation and disturbance in Amazonia. These activities cause severe impacts on the rainforest ecosystem and socioeconomic state of the region. NASA DEVELOP partnered with the Asociación para la Conservación de la Cuenca Amazónica (ACCA), NASA SERVIR Science Coordination Office, and the Spatial Informatics Group to enhance ASGM-related deforestation detection methods. ACCA currently uses the Omnibus Q-test Change Point Detection Algorithm to identify changes in Synthetic Aperture Radar (SAR) monthly-aggregated temporal data from the Sentinel-1 satellite. The team determined the algorithm's accuracy by comparing a stratified random sample of change points against data from January 2019 to June 2020 identified using PlanetScope and Landsat 8 Operational Land Imager (OLI) Earth observations through Collect Earth Online. Our results indicated a users' accuracy of 55% for temporal change detection and producer's and user's accuracies of 99% and 97%, respectively, for detecting when change did not occur. Of the labeled change points, only 19% were due to mining activity. This research can help our partners have a more accurate understanding of where illegal gold mining may be taking place and inform decisions to remediate this activity.
Artisanal and small-scale gold mining (ASGM) is responsible for a large fraction of deforestation and disturbance in Amazonia. These activities cause severe impacts on the rainforest ecosystem and socioeconomic state of the region. NASA DEVELOP partnered with the Asociación para la Conservación de la Cuenca Amazónica (ACCA), NASA SERVIR Science Coordination Office, and the Spatial Informatics Group to enhance ASGM-related deforestation detection methods. ACCA currently uses the Omnibus Q-test Change Point Detection Algorithm to identify changes in Synthetic Aperture Radar (SAR) monthly-aggregated temporal data from the Sentinel-1 satellite. The team determined the algorithm's accuracy by comparing a stratified random sample of change points against data from January 2019 to June 2020 identified using PlanetScope and Landsat 8 Operational Land Imager (OLI) Earth observations through Collect Earth Online. Our results indicated a users' accuracy of 55% for temporal change detection and producer's and user's accuracies of 99% and 97%, respectively, for detecting when change did not occur. Of the labeled change points, only 19% were due to mining activity. This research can help our partners have a more accurate understanding of where illegal gold mining may be taking place and inform decisions to remediate this activity.
A data compression technique is developed for synthetic aperture radar (SAR) imagery. The technique is based on an SAR image model and is designed to preserve the local statistics in the image by an adaptive variable rate modification of block truncation coding (BTC). A data rate of approximately 1.6 bit/pixel is achieved with the technique while maintaining the image quality and cultural (pointlike) targets. The algorithm requires no large data storage and is computationally simple.
Imaging algorithms based on exact point-target responses. Developed for use in reconstructing image of target from data gathered by radar, sonar, or other transmitting/receiving coherent-signal sensory apparatus following circular observation path around target. Potential applications include: Wide-beam synthetic-aperture radar (SAR) from aboard spacecraft in circular orbit around target planet; SAR from aboard airplane flying circular course at constant elevation around central ground point, toward which spotlight radar beam pointed; Ultrasonic reflection tomography in medical setting, using one transducer moving in circle around patient or else multiple transducers at fixed positions on circle around patient; and Sonar imaging of sea floor to high resolution, without need for large sensory apparatus.
By definition, ice which survives the summer is classified as multiyear ice. Thus the area covered by multiyear ice during the winter should be nearly equivalent to the ice area during the previous summer's minima. This condition provides a reasonable criterion for the evaluation of ice concentration and ice type retrieval algorithms using remote-sensing data sets. From special sensor microwave imager (SSM/I) data the NASA Team algorithm estimates the multiyear, first-year, and total ice concentrations during the winter using combinations of the polarization and spectral gradient ratios. The Team algorithm provides only estimates of ice concentration in the summer. From ERS 1 synthetic aperture radar (SAR) data the remarkably stable contrast between multiyear ice and first-year ice in winter provides consistent estimates of multiyear ice concentrations. In the summer, multiyear ice concentration cannot be estimated from SAR or SSM/I data because free water on the surface effectively masks the backscatter and emissivity signature of this ice type. From SAR data a technique which takes advantage of the high backscatter of wind-roughened open water as a discrimination feature is used to estimate the total ice concentration in the summer. With a year-long (January 1992 to January 1993) data set from the Beaufort Sea we found that the multiyear ice concentration estimates from the SAR data are stable and are nearly equivalent to the ice concentration estimated at the end of the previous summer. We contrast this with the variability of the multiyear ice concentration and ice friction estimates obtained using SSM/I data. The Team algorithm produces ice concentration and multiyear ice estimates which are consistently lower than those from the SAR data. We discuss reasons for these discrepancies and the implications of the higher than previously noted multiyear ice concentrations.
The real-time operational requirements for SARCOM translation into a high speed image data handler and processor to achieve the desired compression ratios and the selection of a suitable image data compression technique with as low as possible fidelity (information) losses and which can be implemented in an algorithm placing a relatively low arithmetic load on the system are described.
Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.
A combination of satellite microwave data sets are used in conjunction with ECMWF (Medium Range Weather Forecasts) and NCEP (National Center for Environment Prediction) meteorological analysis fields to investigate seasonal variability in the circulation and sea-ice dynamics of the Weddell and Ross Seas. Results of sea-ice tracking using SSM/I (Special Sensor Microwave Imager), Scatterometer and SAR images are combined with in-situ data derived from Argos buoys and GPS drifters to validate observed drift patterns. Seasonal 3-month climatologies of ice motion and drift speed variance illustrate the response of the sea-ice system to seasonal forcing. A melt-detection algorithm is used to track the onset of seasonal melt, and to determine the extent and duration of atmospherically-led surface melting during austral summer. Results show that wind-driven drift regulates the seasonal distribution and characteristics of sea-ice and the intensity of the cyclonic Gyre circulation in these two regions.
Changes in the seasonal CO(sub 2) flux of the boreal forests may result from increased atmospheric CO(sub 2) concentrations and associated atmospheric warming. To monitor this potential change, a combination of remote sensing information and ecophysiological models are required. In this paper we address the use of synthetic aperture radar (SAR) data to provide some of the input to the ecophysiological models: forest type, freeze/thaw state which limits the growing season for conifers, and leaf on/off state which limits the growing season for deciduous species. AIRSAR data collected in March 1988 during an early thaw event and May 1991 during spring breakup are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off and soil freeze/thaw transitions. Finally, a CO(sub 2) flux algorithm which utilizes SAR data and an ecophysiological model to estimate CO(sub 2) flux is presented. CO(sub 2) flux maps are generated from which areal estimates of CO(sub 2) flux are derived.
A technique for estimation of the Doppler centroid of an SAR in the presence of large uncertainty in antenna boresight pointing is described. Also investigated is the image degradation resulting from data processing that uses an ambiguous centroid. Two approaches for resolving ambiguities in Doppler centroid estimation (DCE) are presented: the range cross-correlation technique and the multiple-PRF (pulse repetition frequency) technique. Because other design factors control the PRF selection for SAR, a generalized algorithm is derived for PRFs not containing a common divisor. An example using the SIR-C parameters illustrates that this algorithm is capable of resolving the C-band DCE ambiguities for antenna pointing uncertainties of about 2-3 deg.
It has been demonstrated and recognized that radar interferometry is a promising method for the determination of digital elevation information and terrain slope from Synthetic Aperture Radar (SAR) data. An important application of Interferometric SAR (InSAR) data in areas with topographic variations is that the derived elevation and slope can be directly used for the absolute radiometric calibration of the amplitude SAR data as well as for scattering mechanisms analysis. On the other hand polarimetric SAR data has long been recognized as permitting a more complete inference of natural surfaces than a single channel radar system. In fact, imaging polarimetry provides the measurement of the amplitude and relative phase of all transmit and receive polarizations. On board the NASA DC-8 aircraft, NASA/JPL operates the multifrequency (P, L and C bands) multipolarimetric radar AIRSAR. The TOPSAR, a special mode of the AIRSAR system, is able to collect single-pass interferometric C- and/or L-band VV polarized data. A possible configuration of the AIRSAR/TOPSAR system is to acquire single-pass interferometric data at C-band VV polarization and polarimetric radar data at the two other lower frequencies. The advantage of this system configuration is to get digital topography information at the same time the radar data is collected. The digital elevation information can therefore be used to correctly calibrate the SAR data. This step is directly included in the new AIRSAR Integrated Processor. This processor uses a modification of the full motion compensation algorithm described by Madsen et al. (1993). However, the Digital Elevation Model (DEM) with the additional products such as local incidence angle map, and the SAR data are in a geometry which is not convenient, since especially DEMs must be referred to a specific cartographic reference system. Furthermore, geocoding of SAR data is important for multisensor and/or multitemporal purposes. In this paper, a procedure to geocode the new AIRSAR/TOPSAR data is presented. As an example an AIRSAR/TOPSAR image acquired in 1994 is geocoded and evaluated in terms of geometric accuracy.
Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROC) for evaluating detection performance when the ground truth is subjective.
A spotlight aircraft SAR in a circular flight path can efficiently obtain an image with very high azimuth resolution or a wider azimuth viewing angle. An analysis on the spotlight SAR is made regarding the required PRF, the predicted resolution, and the computation complexity as a function of the aircraft altitude and the distance between a target and the center of the flight path projection. An efficient processing algorithm based on the exact wide beam spectrum is presented. The results of simulation indicate that the impulse responses meet the predicted resolution performance.
Synthetic Aperture Radar (SAR) Earth observation data are becoming increasingly ubiquitous as new spaceborne systems become operational and their data are made available to scientists and applications users. The characteristic of active sensors like SAR to be able to observe Earth independent of weather or solar illumination, coupled with regular data acquisition, fosters reliability and encourages the investment in algorithm and product development toward a beneficial result. As SAR systems typically contain proprietary or nationally important technologies, civilian SAR systems are typically developed with a national focus, or in the case of the European Union, with the Union’s focus. As a result, when viewed from a global perspective, SAR programs can be generally viewed as independent developments, each with their own requirements, schedules, development approaches, and data policies. At the same time, these systems can be expensive, and particularly in an era of increasingly open data policies, coordination of programs could reduce redundancy in observations, increase sampling density and measurement diversity, and improve dependability of data streams in the long term. Since 2018, agency representatives from NASA, ESA, DLR, JAXA, ISRO, ASI, and CONAE have been evaluating the possibilities for programmatic and technical coordination of future SAR systems, data sharing, and scientific exploitation. In this paper, we describe the work in discovering trends and possibilities associated with flight systems, by evaluating current and future plans for SAR systems around the world, and identifying opportunities for coordination.