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ASA_WS__BP: ASAR Wide Swath Browse Image
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ASA_EC__0P: ASAR Level 0 External Characterization
ASA_APV_0P: ASAR Alternating Polarization Level 0 (Cross polar V)
ASA_APH_0P: ASAR Alternating Polarization Level 0 (Cross polar H)
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1.1.5 Special Features of ASAR

Figure 1.17 ERS-1 image Sept. 23, 1992 Methane Emissions, Tundra, Alaska North Slope (Copyright ESA, 1992)

ASAR has a number of special features (see the index) Dual Polarisation

Figure 1.18 ERS-1 image Aug.2, 1992 Tanana Valley Alaska (Copyright ESA, 1992)

Imaging radars can transmit horizontal (H) or vertical (V) electric-field vectors, and receive either horizontal or vertical return signals, or both. The basic physical processes responsible for the like-polarised return are quasi-specular surface reflection and surface or volume scattering. The cross-polarised return is usually weaker, and often associated with multiple scattering due to surface roughness or multiple volume scattering. Scattering mechanisms and the returns from different surfaces may also vary markedly with incidence angle.

To illustrate the effect of polarisation, consider the very simple model of a vegetation canopy consisting of short vertical scatterers over a rough surface, as shown in the figure1.19 below:

Electromagnetic Wave Orientation
Figure 1.19 Electromagnetic Wave Orientation

Assuming that the scatterers will act as short vertical dipoles, then incident, horizontally polarised microwave energy will not interact with the canopy and will scatter from the surface underneath. Conversely, vertically polarised microwave energy will interact strongly with the dipoles.

ASAR provides dual-channel data. In Alternating Polarisation Mode (AP Mode), it provides one of three different channel combinations:

  • VV and HH
  • HH and HV
  • VV and VH

Dual polarisation data is important for a wide range of applications such as bare soil, vegetation studies, sea ice applications, etc.

Vertical Transmit/Vertical Receive (VV) - Like Polarisation

VV Polarisation
Figure 1.20 VV Polarisation

VV polarisation, shown in figure1.20 , is the preferred polarisation configuration in a number of applications. For instance, in studying the small-scale roughness of (capillary) waves on the water surface, VV is better than HH or cross-polarised combinations, which means it is used extensively for surface wind speed extraction.

Horizontal Transmit/Horizontal Receive (HH) - like polarisation
HH Polarisation
Figure 1.21 HH Polarisation

HH polarisation, shown in figure1.21 , is the preferred polarisation configuration in a number of applications. For instance, in the study of soil moisture, if we ignore the crop density differences, then the vertically oriented crops (e.g., wheat and barley) have improved penetration with HH, allowing the backscatter to represent the soil moisture regime better rather than the crop geometry. HH is very suitable for separating marine ice and water, since it is less sensitive to water roughness than VV polarisation, thus producing an improved contrast between the two target types. For a similar reason, HH is used for ship detection.

Vertical Transmit/Horizontal Receive (VH) - cross polarisation

VH Polarisation
Figure 1.22 VH Polarisation

Horizontal Transmit/Vertical Receive (HV) - Cross Polarisation
HV Polarisation
Figure 1.23 HV Polarisation

Since the backscatter from water surfaces is reduced under cross-polarised SAR illumination/detection, using the VH or HV technique is very suitable for detecting targets on the water surface, which accommodate multiple scattering necessary for depolarisation. Such targets are, for example: ship superstructures and various ice deformations (ridging, fractures and rubble). For a similar purpose, the separation of broadleaf from grain crops, for example, benefits from cross-polarised SAR imaging, since depolarisation is much stronger with the geometries of broadleaf vegetation where multiple scattering of the radar beam is much more likely. There are also indications that the detection of geological linears benefits from cross polarisation when the look angle is acute.

For studies of bare soil, where attention focuses on the retrieval of soil moisture and soil roughness, the use of different polarisations will improve the inversion into soil parameters. Cross polarisation provides an important improvement for soil moisture retrieval since the radar backscatter is less sensitive to surface roughness, row direction, etc.

For many vegetation studies, the use of different polarisations, in particular cross polarisation, will improve the discrimination between vegetation (volume scattering) and soil (surface scattering). In the case of forestry, the use of cross polarisation will improve the forest/non-forest discrimination and the retrieval of low biomass values (forest regeneration, regrowth, plantation). Two examples are provided below (figure1.24 and figure1.25 ) showing how the use of dual polarisation data can improve the information content.

Figure 1.24 SIR-C data (C-band, 26.5°) over Les Landes test site, France. Left image is backscatter intensity (VV polarisation) and right image is HH/VV correlation image. (Acknowledgement: Souyris et al, 1998.)

In figure1.24 above, the SIR-C backscatter intensity image (left) shows poor discrimination between vegetated and non-vegetated areas. This is because of large variations in the backscatter of bare soil surfaces related to different soil roughness and moisture conditions and is similar for both VV and HH polarisation images. However, using both polarisations to produce a HH/VV correlation image (below), it becomes possible to discriminate between non-vegetated (high-correlation) and vegetated (low-correlation) areas. On this image, the high-correlation (bright) areas correspond to recently harvested cornfields. In contrast, the different states of tillage are seen to produce large variations in backscatter intensity in the single-channel image.

figure1.25 , figure1.26 and figure1.27 below illustrates differences between like-polarised and cross-polarised images of urban areas. Three different polarisation images are shown for an area in southern Germany which was imaged by the JPL AIRSAR during the MAC Europe campaign in 1989. The area is 12 km wide, and includes forests, cultivated fields and urban areas. The two like-polarised images are seen to be very similar. However, on the cross-polarised image, urban areas are seen to be much less bright. This is because the cross-polarised return only appears through multiple scattering, while the urban areas are characterised by man-made objects that act like corner reflectors.

Three different C-band polarisation images for an area in southern Germany imaged by the JPL AIRSAR during the 1989 MAC Europe Campaign.

Figure 1.25 C-band HH polarisation

Figure 1.26 C-band HV polarisation
Figure 1.27 C-band VV polarisation

It is possible to simulate Alternating Polarisation images (VV/HH) using ERS and Radarsat data. figure1.28 shows a combination of ERS and Radarsat images taken one day apart, for an area in Oxfordshire, UK. In this example a large number of agricultural fields are seen to have a blue colour which is indicative of a high backscatter in HH polarisation compared with VV. Since these fields are all cereal fields, this is a good indication of the value of of alternating polarisation images for improving
crop classification. Over the remainder of the image, urban areas, woodland and grassland all have grey tones indicating no significant differences in HH and VV backscatter.

Figure 1.28 Simulated Alternating Polarisation image (VV&HH) of an area in Oxfordshire, UK. (Red & Green: ERS 26/5/97. Blue: Radarsat 27/5/97). Acknowledgement: Remote Sensing Applications Consultants, UK

There is considerable interest in the Alternating Polarisation Mode for sea ice applications. From current research results using ERS and RADARSAT data, it is still not clear whether VV or HH polarisation is generally better for mapping sea ice. One of the current problems using either ERS or RADARSAT data at low incidence angles is that ice/water discrimination can sometimes be poor. Alternating polarisation, HH and VV data, will give improved ice edge/water discrimination. Cross polarisation data is expected to be particularly useful for mapping ice topography (ridging, rubble), and is also likely to give improved ice type discrimination.

Figure1.29 below, showing ERS and RADARSAT images from the Arctic acquired less than 2 hours apart, provides a very striking example of the differences that can occur between VV and HH polarisation images, although in this case it should be noted that the radar viewing directions were virtually opposite. Over much of the area one can see large signature reversals between the VV and HH images. Differences show up primarily over open water. The wind speeds recorded on board the icebreaker Oden, 55 km away from the scene centre, were 8 and 14 m/s respectively. The incidence angle varies between 21 and 26 degrees over the ERS-2 sub-image and between 29 and 34 degrees over the RADARSAT sub-image. The different VV and HH responses to wind roughening can be used for wind determination, and the sea ice differences can help in classifying ice properties.

image image image
Figure 1.29 Almost simultaneous ERS and RADARSAT images covering 80 x 80 sq km centred around 82°N 12°E. The ERS image was acquired from a descending orbit at 12:88 and the RADARSAT (which has been rotated by 90 degrees) from an ascending orbit at 14:33, both on 19 September 1996. The images have been averaged to the same pixel spacing and intensity stretched. (Acknowledgement: J. Askne and A. Li. Chalmers Univ. of Technology, Sweden.)

The EMAC-95 airborne radar experiment demonstrated the value of dual polarised data for discriminating sea ice types. Figure1.30 shows an EMISAR C-band co-polarisation ratio VV/HH image covering Baltic Sea Ice (Dierking et al., 1997 Ref. [1.5 ] ). The green and yellow areas on this image have co-polarisation ratios larger than 1, and correspond to level ice and thin ice/open water. The largest values (green) are associated with smooth ice surfaces. A low co-polarisation ratio around 1 (blue) is observed for highly deformed areas and ridges, where the return radar signal is dominated by coherent (specular) scattering.
Figure 1.30 Copolarisation ratio VV/HH image for the Baltic Sea Ice site imaged by EMISAR during EMAC-95. (Acknowledgement: Dierking et al., 1997).

Another example illustrating the value of the ratio VV/HH for discriminating ice/no ice is provided in figure1.31 , which shows HH, VV and VV/HH ratio images of a mixed pack ice and open water scene in the Gulf of St. Lawrence, imaged by the SIR-C radar. Discrimination of ice/water is complicated by incidence angle and wind conditions, and is not always distinguishable with either HH or VV polarisations. However, since the ratio of VV to HH backscatter is larger than 1 for open water, but close to 1 for pack ice, the sea ice is seen to be much darker on the VV/HH ratio image, independent of incidence angle or wind conditions.

Figure 1.31 Dual polarisation images (VV, HH and VV/HH ratio) from SIR-C for the Gulf of St. Lawrence. (Acknowledgement: L. Gray, CCRS, Canada.)

Over the oceans, the backscattering signal is stronger with VV than with HH polarisation. Experimental results indicate that oceanic features such as internal waves, fronts and sea floor topography tend to appear somewhat better with HH than with VV polarisation. Figure1.32 , RADARSAT HH polarisation, shows internal waves in the Straits of Gibraltar particularly well. In contrast, sea surface imprints of atmospheric features (in particular, convective cells) appear to be more visible with VV polarisation than with HH polarisation.

Figure 1.32 Internal wave packet seen on a RADARSAT image of the Straits of Gibraltar. (Acknowledgement: Space Dept., DERA, UK: Data copyright Canadian Space Agency.)

There are many excellent examples of atmospheric phenomena seen with VV polarisation ERS images, such as those illustrated in figure1.33 below.

Figure 1.33 ERS-1 SAR VV image (100 km x 100 km) of the Mediterranean Sea north of the Strait of Messina acquired on September 8, 1992. (Acknowledgement: W. Alpers, Univ. Hamburg. Germany.)

To the north-west of Gioia there are surface manifestations of a katabatic wind (bright area). Furthermore, between the island of Stromboli and the Sicilian coast there is a granular pattern, which is interpreted as sea surface "imprints" of atmospheric convective cells. This cellular structure is destroyed in the vicinity of the Sicilian coast by the katabatic wind blowing from the mountains onto the sea. In the lower section of the image, an oceanic internal wave train can be delineated propagating southwards in the Strait of Messina.

The ASAR Alternating Polarisation Mode is therefore of strong interest for ocean studies. Simultaneous dual polarisation images will allow discrimination between similar signatures of oceanic/atmospheric features (e.g., fronts, internal waves). The most favourable can be chosen for detection of oceanic/atmospheric features or for special applications. Wind vector retrieval from SAR images and the tuning of imaging models (e.g., in bathymetric assessment systems) becomes easier if backscatter variations with HH and VV polarisations are known.


Ref 1.5
ASAR Science Advisory Group, Editor R.A.Harris, European Space Agency 1998, "ASAR Science and Applications", ESA SP-1225

Ref 1.6
Dierking W., Askne J. & Pettersson M.I., 1997, "Baltic Sea Ice Observations during EMAC-95 using Multi-frequency Scatterometry and EMISAR Datal., Workshop Proceedings" EMAC 94/95, "Final Results", ESA WPP-136,
September 1997.

Ref 1.7
Le Toan T., Smacchia P., Souyris J. C., Beaudoin A., Merdas M., Wooding M., & Lichtenegger J., 1994, "On the Retrieval of Soil Moisture from ERS-1 SAR Data", Proceedings of the Second ERS-1 Symposium "Space at the Service of our Environment", ESA SP-361 Vol. II, pp 883 to 888, January 1994. Selectable Incidence Angles

Figure 1.34 ERS-1 image, Wien Austria, Acquired at Fucino (I), 4 Jan 1993 (red) 17 August 1992 (green) 4 May 1992 (blue). Processed by I-PAF (Copyright ESA 1993)

The incidence angle is defined as the angle formed by the radar beam and a line perpendicular to the surface at the point of incidence. Microwave interactions with the surface are complex, and different scattering mechanisms may occur in different angular regions. Returns due to surface scattering are normally strong at low incidence angles and decrease with increasing incidence angle, with a slower rate of decrease for rougher surfaces. Returns due to volume scattering from a heterogeneous medium with low dielectric constant tend to be more uniform for all incidence angles. Thus, radar backscatter has an angular dependence, and there is potential for choosing optimum configurations for different applications.

The incidence angle range for each of the Swath positions and the slightly narrower range of incidence angles for Wide Swath and Global Monitoring Modes are shown in table 1.3 below:

Table 1.3 Specifications for ASAR Image Mode Swaths (for satellite altitude of 786 km).
Image Swath Swath Width(km) Ground, position from nadir (km) Incidence Angle Range Worst Case Noise Equivalent Sigma Zero
IS1 105 187 - 292 15.0 - 22.9 -20.4
IS2 105 242 - 347 19.2 - 26.7 -20.6
IS3 82 337 - 419 26.0 - 31.4 -20.6
IS4 88 412 - 500 31.0 - 36.3 -19.4
IS5 64 490 - 555 35.8 - 39.4 -20.2
IS6 70 550 - 620 39.1 - 42.8 -22.0
IS7 56 615 - 671 42.5 - 45.2 -21.9

One significant advantage of higher incidence angles is that terrain distortion is reduced. This is well illustrated by a comparison of ERS-2 and RADARSAT images of the Zillertal region in the Austrian Alps, shown in figure1.35 below. This region includes narrow valleys which range from 600 m at Mayrhofen, up to 3500 m on the highest peaks. Up to about 1900 m, the slopes are partly forested, with alpine vegetation (grass, sedge, etc.), rocks and moraines at higher levels. The two images were obtained within a few days of one another, with a very similar viewing direction. Looking first at the ERS-2 image, obtained with incidence angles of 24° to 26° from near to far range, one sees extreme terrain distortion in the form of severe foreshortening and layover ( brightening) of slopes facing the radar, combined with significant lengthening of the slopes facing away from the radar. In contrast, these distortions are seen to be much less in the RADARSAT image where the incidence angle varies between 41° and 44° from near to far range on this image extract. One clear benefit of the higher incidence angle is the extra information which can be observed on the bright steep slopes facing the radar, and this has been found to greatly improve the value of the image for classification of surface classes such as moraine, bare soil and vegetation types.

Figure 1.35 Terrain distortion effects on SAR images obtained with different incidence angles: Zillertal Region, Austrian Alps. Area covered is approx. 36 km x 40 km. (Acknowledgement: H. Rott, University of Innsbruck.)

image image

image image image

With a range of different incidence angles available, it becomes possible to select optimum angles for different applications, or to use acquisitions from two separate passes for multi-angle analysis. In the context of vegetation and soil applications, there are some general points which can be made, based on previous research results:

  • For soil moisture and soil roughness studies, the combination of different incidence angles is of interest, with the condition that there be short time intervals between acquisitions.
  • For agriculture, the use of particular incidence angles will improve selective observation of vegetation (high incidence angles) or underlying soil (low incidence angles).
  • For forestry, the use of low incidence angles enhances the sensitivity to biomass, whereas the use of high incidence angles enhances the discrimination of forest types through interaction with forest structure.

Figure1.36 below illustrates the importance of the incidence angle in isolating the radar response due to the vegetation canopy from that of the underlying soil. Each curve represents the simulated response, with C-band W polarisation, of a soybean canopy for varying gravimetric soil moisture ranging from 20% to 30% (from bottom to top). Whilst the backscatter at 20° incidence angle is still sensitive to underlying soil conditions, that at 40° is stable and invariant with respect to soil moisture.

Figure 1.36 Simulated backscatter for a soybean canopy showing increased sensitivity to soil moisture at low incidence angles. (Acknowledgement: Nghiem et al, 1993.)

Figure1.37 below provides an excellent example of how vegetation mapping can be enhanced by using high incidence angle data. This pair of RADARSAT images shows discrimination of forest clearcuts in Whitecourt, Alberta, an active logging area in the foothills of the Rocky Mountains. On the first image, acquired at an incidence angle of 20° to 27° there is poor contrast between the clearcuts and the forest. On the second image, which was acquired at a much larger incidence angle of 43° to 46°, the dark tones of the clearcut areas contrast strongly with the brighter returns from the surrounding forest.

image image image
Figure 1.37 RADARSAT images acquired at different incidence angles (a. 20° to 27°. b. 43° to 46°), showing Forest Clearcuts in Alberta, Canada. (Acknowledgement: L. Gray, CCRS, Canada.)

Images acquired with different incidence angles may be used in combination to improve land cover discrimination, but since each image has to be acquired on a different day, any composite image will also include a temporal change component.

Images acquired with different incidence angles may be used in combination to improve land cover discrimination, but since each image has to be acquired on a different day, any composite image will also include a temporal change component.
Figure1.38 below provides an illustration of the use of multiple incidence angles to improve land cover discrimination for an area near Oxford, UK.

Figure 1.38 Multiple incidence angle image of Oxfordshire area, UK. Composite of Radarsat images: Blue: 23° - 23/3/97, Green: 37° - 13/3/97, Red: 43° - 3/3/97. (Acknowledgement: Remote Sensing Application Consultants, UK)

In this case, 3 Radarsat images taken within a period of 10 days have been combined (Blue - 23° 23 rd March 97, Green - 37° 13 th March 97, Red - 43° 3 rd March 97). Most of the coloured areas on the image, indicative of
backscatter differences related to incidence angle, are bare soil fields, while grassland, woodland and urban areas tend to have grey tones, showing a similar backscatter at the different incidence angles. In the northern half of the area, which has clay soils, practically all bare soil fields have a blue colour, indicating higher backscatter at the lowest incidence angle, as one would expect. In the southern half of the area which has chalk soils, some of the bare soil fields also have blue colours, but some of the fields coloured red are also bare soil fields and this seems something of an anomaly. Possible explanations are that these fields have marked differences in soil roughness, or possibly that cultivation changes took place during the period over which the 3 images were acquired.

Ship detection with ERS data was limited to a certain extent by the steep incidence angles. As illustrated in figure1.39 , RADARSAT has now clearly demonstrated the benefits of higher incidence angle data for the detection of ocean-going trawlers (typically, 55 m long) and RADARSAT images are already used pre-operationally for monitoring fishing activity in the Barents Sea. The pattern of trawlers seen on this image shows a marked concentration in International Waters along the boundary with Norwegian Waters. Several of the outer ASAR standard beams will be capable of detecting trawlers, although in a rather narrow swath. Also, cross-polarised images from the Alternating Polarisation Mode should further improve detection capability at steeper incidence angles.

Figure 1.39 RADARSAT image showing fishing vessels in the Barents Sea, similar to what will be possible with ASARs higher incidence angles (26 km scene width). (Data copyright Canadian Space Agency.)

Although higher incidence angles are preferable for ship detection, wide swath and ScanSAR images, such as that shown in figure1.40 below, can be used across most of the incidence angle range, giving excellent wide area coverage.

Figure 1.40 RADARSAT ScanSAR image of the English Channel and North Sea showing ship/oil rig detections. The enlarged inset shows a cluster of oil rigs in the North Sea. (Acknowledgement: Space Dept., DERA, UK; Data copyright Canadian Space Agency.)

For a further discussion of this subject see the section entitled "Ocean Applications." Wide Area Coverage and Frequency of Coverage

ASAR low-resolution images provided by the Wide Swath and Global Monitoring Modes open up new possibilities for applications requiring large area coverage and/or more frequent revisit. Both modes will provide 105 km swath coverage for applications where higher resolution is necessary (better than a 5-day frequency).

ASAR Wide Swath is aimed primarily at sea ice and other oceanographic applications, where there is a special interest in obtaining a wide area view with high temporal frequency. (See Figure in the section entitled "Geophysical Coverage" to view a graphic portrayal of the comparative swaths for the ENVISAT instruments). figure1.42 below shows a RADARSAT wide swath image of the Gulf of St. Lawrence on which ice types are seen in various shades of light grey, in contrast with water which has the darkest tones. Such images are now used routinely by the Canadian Coast Guard for ice breaker operations and routing of ships in the Gulf.

Sea Ice Monitoring using Radarsat ScanSAR data in the Gulf of St. Lawrence, Canada, 6/3/96. Swath coverage i9 450 km, with 250 m pixel spacing. (Radarsat Data Copyright Canadian Space Agency/Agence spatiale canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and distributed by Radarsat International. Imagery enhanced and interpreted by CCRS).
Figure 1.42 Sea Ice Monitoring using RADARSAT ScanSAR data in the Gulf of St. Lawrence, Canada, March 6, 1996. Swath coverage is 450 km, with 250 m pixel spacing. (RADARSAT Data Copyright Canadian Space Agency/Agence spatiale canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and distributed by RADARSAT International. Imagery enhanced and interpreted by CCRS.)

The ASAR Global Monitoring images, with low data rates, promise to be particularly valuable for sea ice mapping over extensive areas. figure1.43 below shows a simulation of an ASAR Global Monitoring Mode image for regional ice reconnaissance. The simulation is based on a RADARSAT ScanSAR wide, C(HH), image of the southern Beaufort Sea. The original 100 m resolution, 500 km image, which was acquired on October 11, 1996, shows the Mackenzie delta and Tuktoyuktuk peninsula to the south, the new ice and open water in the lead just north of the shore-fast ice, and the pack ice to the north occupy most of the image. The simulation was obtained by reducing the swath from 500 km to 400 km, then sub-sampling and smoothing the image to simulate both the spatial resolution (1 km) and the radiometric resolution of the ASAR Global Monitoring Mode. The simulation shows the potential value of the resulting product. Although the transition from land to shore-fast ice is not distinct, there is still sufficient detail in the ice imagery to recognise areas of slightly lower pack ice concentration (to the east), and to recognise the westward drift of the pack ice just north of the shore-fast ice. This is consistent with the normal clockwise ice movement in the Beaufort Sea gyre.

ASAR Global Monitoring Mode simulation using Radarsat ScanSAR wide data. (Acknowledgement: L. Gray. CCRS)
Figure 1.43 ASAR Global Monitoring Mode simulation using RADARSAT ScanSAR wide data. (Acknowledgement: L. Gray, CCRS.)

The frequent large area coverage which ASAR is able to provide is also important for monitoring ice sheets. Of course, ERS already provides frequent revisits of polar regions and the value of this has been well demonstrated in a study of the collapse of the northern Larsen Ice Shelf, Antarctica (Rott et al, 1996). Figure1.44 below shows two ERS-1 SAR images of the Larsen Ice Shelf. The first (left image) is a strip of three 100 km x 100 km images from a descending pass on January 30, 1995, in which the ice sheet can be seen to be breaking up. Looking at the second (right) image, which was taken just five days earlier, one sees the ice shelf still largely intact. Such images provide valuable information on the timing and rates of change, in this case illustrating the extremely rapid disintegration of the ice shelf over a few days at the end of January 1995. ASAR will be able to provide daily coverage of such phenomena in the polar regions and another important advantage will be the on-board storage capability. For Antarctica, the O' Higgins ERS receiving station currently operates only for two 5-week periods per year.

ERS SAR images of the Larsen Ice Shelf, Antarctica. The left image is a strip of 3 standard 100 km x 100 km images (descending pass) taken on 30/1/95, showing break-up of the ice shelf. The right image is another strip of 100 km images (ascending pass) taken on 25/1/95, when the ice shelf can be seen to be largely intact. (Acknowledgement: H. Rott, University of Innsbruck, Austria).
Figure 1.44 ERS SAR images of the Larsen Ice Shelf, Antarctica. The left image is a strip of 3 standard 100 km x 100 km images (descending pass) taken on January 30, 1995, showing break-up of the ice shelf. The right image is another strip of 100 km images (ascending pass) taken on January 25, 1995, when the ice shelf can be seen to be largely intact. (Acknowledgement: H. Rott, University of Innsbruck, Austria.)

Over the land, interests focus on the potential of low-resolution SAR data for soil moisture and vegetation monitoring.

Previous work carried out over land using ERS Wind Scatterometer data has already shown that low-resolution measurements, in this case 50 km spatial resolution, can provide useful information concerning vegetation dynamics and freeze/thaw on a continental and regional scale (Wismann & Boehnke, 1994). Figure1.45 shows how the ERS Wind Scatterometer has been used to monitor seasonal variations over the African continent. Besides the scatterometer image for summer 1993, the so-called Hovmoeller diagram shows a slice through Africa from 35°N to 35°S extending longitudinally from 20° to 26°E. Monthly averages of the radar intensity are plotted for the period from 1991 to 1997. The predominant signal in the Hovmoeller diagram is the annual variation in radar backscatter in the savannah region north and south of the rain forest, and it can be seen how the pattern of increased backscatter in 1992 is repeated every year. The much better resolution of the ASAR Global Monitoring Mode will provide a significantly improved capability for continental or regional scale measurements.

Figure 1.45 ERS-1 scatterometer map of Africa and a Hovmoeller diagram for a slice through Africa from 35°N to 35°S at a longitude of 20°E. Monthly averages are plotted for 1991 to 1997. (Acknowledgement: V Wismann and H. Boehnke, WARS, Germany.)

The availability of approximately 5-day revisit coverage (in Central Europe) using Image Mode promises to be particularly important for flood mapping. For floodplain mapping and emergency management, a resolution of around 30 m is required, with frequent coverage. The much improved temporal coverage possible using ASAR is vital for developing operational systems. In addition, the high incidence angles and HH polarisation capabilities of ASAR will give better mapping of flood extent.

For applications where this improved temporal coverage is important, the main issue then becomes the utility of data acquired at a wide range of different incidence angles. Combined use of images acquired with different incidence angles poses a new set of challenges. Interferometry

Figure 1.46 ERS-1 Interferogram, Feb. 7 2002, Bay of Naples/Vesuvius Italy (Copyright (c) 2001 by SRC SASA, original data by ESA) Principles

As was discussed in the section entitled "Scientific Background" , a SAR works by illuminating the Earth with a beam of coherent microwave radiation, retaining both amplitude and phase information in the radar echo during data acquisition and subsequent processing. This radiation can be described by three properties:

  • Wavelength - the distance between peaks on the wave.
  • Amplitude - the displacement of the wave at the peak.
  • Phase - describes the shift of the wave from some other wave. Phase is usually measured in angular units, like degrees or radians.

Synthetic Aperture Radar (SAR) interferometry exploits this coherence, using the phase measurements to infer differential range and range change in two or more complex-valued SAR images of the same surface, thereby deriving more information about an object than is obtainable with one single image.

Figure 1.47 Phase shift

The resulting difference of phases is a new kind of image, called an interferogram, which is a pattern of fringes containing all of the information on relative geometry. Figure1.48 below, offers an example of such an image.

image image image
Figure 1.48 Radar interferogram of a portion of the Rutford ice stream in Antarctica, based on two ERS-1 images taken six days apart. The fringe pattern (colour cycle) is essentially a map of ice flow velocity, with one fringe representing 28 mm of range change along the radar line of site. (Image courtesy Jet Propulsion Laboratory, California Institute of Technology)

For a second SAR image to provide additional information, it must be acquired from a different sensor position or at a different time. The difference between the acquisitions of the first and second images determines the type of interferometer that results. Some of the most common forms are:

  • Across-track - used primarily for topographical information, this type utilises a difference in across-track position, or look angle.
  • Along-track - used primarily for ocean currents information and moving object detection, this type utilises a difference in the along-track position, which can be achieved by a small difference in acquisition time, on the order of microseconds to seconds.
  • Differential - this method utilises a difference in time, on the order of days to years, and is used primarily to observe glacier (ice field) or lava flows, if the time difference is within days. If the time difference is measured in days to years, it can be a very useful method of observing subsidence, seismic events, volcanic activity, or crustal displacement.

Each of these types will be touched on briefly below. Across-track Interferometry (InSAR)

The best known application of SAR Interferometry is the reconstruction of the Earth topography by using different look angles to compare the same object. This is what is referred to as across-track interferometry. Across-track is also known as the range direction, defined as the dimension of an image perpendicular to the line of flight of the radar.

Consider two radar antennas, A1 and A2, simultaneously viewing the same surface and separated by a baseline vector B with length B and angle image with respect to horizontal, as shown in figure1.49 below. A1 and A2 may also represent a single antenna viewing the same surface on two separate passes.

Figure 1.49 Basic imaging geometry for InSAR

A1 is located at height h above some reference surface. The distance between A1 and the point on the ground being imaged is the slant range image , while image is the distance between A2 and the same point.

In the case of simultaneous imaging from two separate antennas, one antenna both transmits and receives the radar signal. This antenna is known as the master. The second antenna, know as the slave, only receives. This method is sometimes referred to as single-pass interferometry. In the case where a single-antenna SAR system revisits the same position and images the same area on the ground after several days or weeks, the repeat-pass interferometry method is used. With this method, each antenna acts as both transmitter and receiver, as depicted in figure1.50 below.

InSAR Data Collection
Figure 1.50 InSAR Data Collection

The phase of pixel value in a complex SAR image depends on the scattering mechanism in the resolution cell, and the distance from the antenna to the point. If the scattering mechanism in the two images is similar, then the phase difference between the two complex SAR images is proportional to the difference in slant range from the two antenna to the point.

The similarity in scattering mechanism in the two images is indicated by the correlation coefficient of the image, which is also called coherence in SAR interferometry literature. Any dissimilarity of the scattering mechanism between the two images, indicated by a low coherence, results in phase noise. A certain loss of coherence results from the different look angles from two antenna to the point, and from receiver noise. Coherence loss can also result from changes in the surface between acquisitions, in the case of repeat-pass interferometry.

The difference between image and image can be measured by the phase difference between the two complex SAR images. That is, by multiplying one image by the complex conjugate of the other image, an interferogram is formed whose phase is proportional to the range difference to the point. The phase of the interferogram, displayed as an image, contains fringes that trace the topography like contour lines, as shown in figure above.

Interferograms are often displayed by showing the phase difference as colour, and the SAR amplitude as brightness (see figure1.51 below).

SAR Amplitude (0..300) image Phase Difference (-pi..pi) image
Figure 1.51 Interferogram creation

SAR Amplitude (0..300) + Phase Difference (-pi..pi) =


The phase of the above interferogram shows the topography of an imaged mountain.

The slant range difference is proportional to the full phase or absolute phase of the complex-valued interferogram. However, the measured phase values of the interferogram can only take values between 0 and 2 image . That is, the phase is 'wrapped'. Thus, in order to compute the slant range difference which is needed to compute topography, the 2 image ambiguity inherent in the phase measurements must be solved, using techniques of 'phase-unwrapping'.

Once the absolute phase of each pixel of the interferogram is known, the geometry of the figure can be used to compute the topography z(y), if the baseline vector (vector B in figure1.49 ) is known. The result is a Digital Elevation Model (DEM) of the observed area. Along-track Interferometry

Along-track (azimuth) interferometry uses two antennas; the master that transmits and receives, and the slave that receives only. Such a system takes two images of the same target, with a time delay that results in an along-track difference in position. Typically, this time delay is between 10 microseconds (ms) and 100 ms. If the target remains stationary between acquisitions, the two data sets are ideally identical (i.e. in the absence of any system phase noise) and the interferometric phase is zero. However, any relative range shift of the targets between the two images will result a non-zero interferometric phase. The along-track interferometric method is most often used when detecting relatively fast motion, such as ocean currents. Differential Interferometry (D-InSAR)

The temporal separation in repeat-pass interferometry of days, months, or even years, can be used to advantage for long term monitoring of geodynamic phenomena, in which the target has changed position at a relatively slow pace. This would be true when monitoring glacial or lava flows. It is also useful for analysing the results of single dramatic events, such as earthquakes. If two acquisitions are made at different times from the same position, so there is no across-track baseline, then the phase of the interferogram depends only on the change in topography between the acquisition times. In general, a difference in across-track, (azimuth), position of the acquisitions also exists. In this case, multiple acquisitions can be made to measure the topography, and measure the change in topography (differential effects) over time.

The block diagram below depicts a typical D-InSAR processor using one SAR and one ScanSAR image, that incorporates a Digital Elevation Model (DEM).

Figure 1.52 Block diagram of a typical D-InSAR processor.

In the 35 day repeat orbit scenario, the wide swath capabilities of ASAR, combined with the predictable high reliability and repeatability of the orbits, will allow the retrieval of extremely useful data for differential interferometry (D-InSAR). Another
important benefit of ASAR will be the availability of different viewing angles and in particular higher off-nadir angles, that enhance the interferometric visibility of steeper slopes, which are otherwise in layover with the 23° incidence angle of ERS-1/2. As for ERS, in order that terrain can be imaged with differential interferometry the surface conditions should be stable enough so that more than 30-40% of the strong scatterers remain unchanged in two images acquired 35 days apart.

ASAR will be able to measure very small terrain displacements due to co-seismic motions, subsidence, volcanic upswelling, landslides, ice movement and possibly oscillatory effects like earth tides and the loading of sea tides on the continental shelf. ( See section entitled "Land Applications" )

An excellent example of the detection of surface subsidence is shown in figure1.53 below. The image displays an exaggerated three dimentional (3D) perspective view of the Belridge (middle left) and Lost Hills (lower left) Oil Fields, California, viewed from the north-west. Both oil fields are located in the San Joaquin Valley. The surface deformation derived from ERS-1 data collected in September and November 1992 (70 days time difference), shows subsidence of up to 6 cm.

Figure 1.53 Subsidence in the Belridge Oilfields, California. The colours derived from ERS-1 data collected in September and November 1992 (70 days time difference) show subsidence ranging from 1 cm (blue) to 6 cm (red-brown). The 3D view has been produced using the DEM generated from the ERS tandem pair, combined with a Radarsat image. (Acknowledgement: M. van der Kooij, Atlantis Scientific Inc.; Radarsat Data Copyright Canadian Space Agency/Agence spatiale canadienne 1996)

Usually, D-InSAR surveys are generated starting from two full resolution SAR images. Yet, it is possible to combine low-resolution, Wide Swath (WS) images with full resolution ones to give high quality D-InSAR images. See Low Resolution Interferometry below. Coherence Evaluation

Coherence, when associated with interferometry, is related to phase variance between the two SAR images. For the purpose of processing the interferometry data into topography of motion information, the coherence can be a useful tool in indicating areas of noisy phase. For example, during phase unwrapping, areas of noisy phase - as indicated by a low value of coherence - can be avoided.

In addition, coherence is another useful parameter that can be extracted from repeat-pass interferograms. Coherence provides information on stability over time, or temporal stability, and is therefore an important feature for land cover classification. Temporal decorrelation can be caused by such things as changes in vegetation, freezing and thawing, or human activities like plowing. All of these changes are observed over periods of days to years, whereas some changes to water surfaces can occur in a matter of milliseconds. Low Resolution Interferometry

Pairs of complete Wide Swath (WS) or Global Monitoring (GM) Mode images will be unsuitable for interferometry because, in the lower resolution modes, the data are sampled in bursts along the azimuth direction (along-track). For interferometry the sampling bursts need to be spatially aligned in the two interfering frames. However, there are interesting possibilities for using low resolution images in conjunction with full resolution, Image Mode (IM) images. A full resolution image with the same incidence angle as the subswath of the Wide Swath or Global Monitoring Mode image is needed.

Once a worldwide archive of Wide Swath or Global Monitoring Mode images is built up it will be possible to obtain interferometric pairs of particular areas of interest within 35 days, by acquiring full resolution (IM) data with the same imaging geometry as the relevant portion of the low resolution image. Because the previously acquired burst images are of low resolution, the quality of the fringes will be proportionally lower than if two full resolution images had been used.

Figure1.54 is a simulation of co-seismic motion retrieval using low resolution differential interferometry. In this example ERS SAR images for the Landers 1992 earthquake have been used to simulate interferograms, using an Image Mode image together
with either Wide Swath Mode or Global Monitoring Mode images. On these images one fringe corresponds to half a wavelength displacement along the radial direction.

Figure 1.54 Simulation of low resolution interferograms of the Landers 1992 earthquake, using ERS SAR data. Image Mode (right), Wide Swath Mode (top left) and Global Monitoring Mode (bottom left), for an area of 48 km x 45 km. (Acknowledgement: F. Rocca, Politecnico di Milano, Italy). Conclusions

The above discussion of interferometry only scratches the surface of a rich and complex branch of remote sensing and the list of references provided below offers just a small portion of the wealth of material dedicated to this expanding area of research.

It is now well established that SAR interferometry provides a valid form of geophysical measurement. The huge archive of data acquired by the C-band sensors ERS-1 and ERS-2 has imposed a de facto standard for interferometry. The Advanced Synthetic Aperture Radar (ASAR) sensor, which is also a C-band instrument, will provide considerably higher flexibility compared with the two ERS SARs. ASAR provides a choice of two polarisations ( out of H, VV, and VH ) as well as a variety of imaging modes, such as different incidence angles, standard and ScanSAR wide swath modes. ( Refer to the other subsections within "Special Features of ASAR" 1.1.5. for more information on these topics ). In particular, since ASAR can provide higher off-nadir angles than was the case with the ERS-1/2 sensors, the problems of layover will be reduced, thereby enhancing the interferometric visibility of steeper slopes. References

Ref 1.8
ASAR Science Advisory Group, Editor R.A.Harris, European Space Agency 1998, "ASAR Science and Applications", ESA SP-1225

Ref 1.9
Bamler, R., P. Hartl, "Synthetic aperture radar interferometry", R. Bamler, P. Hartl, Inverse Problems 14, pp. R1-R54, 1998

Ref 1.10
Dixon, T. H, "Report of a Workshop Held in Boulder, Colorado : February 3-4, 1994", prepared by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Ref 1.11
Feigl, K. L., A. Sergent, and D. Jacq, "Estimation of an earthquake focal mechanism from a satellite radar interferogram": application to the December 4, 1992 Landers aftershock, Geophys. Res. Lett., in press, 1994.

Ref 1.12
Geudtner, D., "The interferometric processing of ERS-1 SAR data", European Space Agency, Technical Translation of DLR-FB 95-28, ESA-TT-1341, 1995.

Ref 1.13
Goldstein, R. M., H. Engelhardt, B. Kamb, and R. M. Frolich, "Satellite radar interferometry for monitoring ice sheet motion: application to an Antarctic ice stream", Science, 262, 1525-1530, 1993.

Ref 1.14
Goldstein, R. M., H. A. Zebker, and C. Werner, "Satellite radar interferometry: two-dimensional phase unwrapping", Radio Science, 23, 713-720, 1988.

Ref 1.15
Graham, L. C., "Synthetic interferometer radar for topographic mapping", Proc. IEEE, 62, 763-768, 1972.

Ref 1.16
Li, F., and R. M. Goldstein, "Studies of multibaseline spaceborne interferometric synthetic aperture radars", IEEE Trans. Geosci. Remote Sensing, 28, 88-97, 1990.

Ref 1.17
Massonet, D., K.L.Feigl, "Radar interferometry and its application to changes in the earth surface", Reviews of Geophysics Vol. 36, Number 4, Nov. 1998, pp.441-500.

Ref 1.18
Massonnet, D., M. Rossi, C. Carmona, F. Adragna, G. Peltzer, K. Feigl, and T. Rabaute, "The displacement field of the Landers earthquake mapped by radar interferometry", Nature, 364, 138-142, 1993.

Ref 1.19
Massonnet, D., K. Feigl, M. Rossi, and F. Adragna, "Radar interferometric mapping of deformation in the year after the Landers earthquake", Nature, 369, 227-230, 1994.

Ref 1.20
Massonnet, D., and K. L. Feigl, "Discriminating geophysical phenomena in satellite radar interferograms", Geophys. Res. Lett., in press, 1995a.

Ref 1.21
Monti Guarnieri, A., Prati, C., Rocca, F., and Desnos, Y-L, "Wide Baseline Interferometry With Very Low Resolution SAR Systems", Dipartimento di Elettronica e Informazione - Politecnico di Milano, ESTEC ( available on-line at:

Ref 1.22
Peltzer, G., K. Hudnut, and K. Feigl, "Analysis of coseismic surface displacement gradients using radar interferometry: new insights into the Landers earthquake", J. Geophys. Res., 99, 21971-21981, 1994.

Ref 1.23
Rodriguez, E., and J. Martin, "Theory and design of interferometric SARs", Proc. IEEE, 139, 147-159, 1992. Ref. [1.22 ] Solaas, G.A., "ERS-1 interferometric baseline algorithm verfication", ESA Tech. Note ES-TN-DPE-OM-GS02, 69 p., 1994.

Ref 1.24
Zebker, H., and R. Goldstein, "Topographic mapping from interferometric synthetic aperture radar observations", J. Geophys. Res., 91, 4993-5001, 1986.

Ref 1.25
Zebker, H., and J. Villasenor, "Decorrelation in interferometric radar echoes", IEEE Trans. Geosci. Rem. Sensing, 30, 950-959, 1992.

Ref 1.26
Zebker, H., P. Rosen, R. Goldstein, A. Gabriel, and C. Werner, "On the derivation of coseismic displacement fields using differential radar interferometry: the Landers earthquake", J. Geophys. Res., 99, 19617-19634, 1994a. Ref. [1.25 ] Zebker, H.A., C. Werner, P.A. Rosen, and S. Hensley, "Accuracy of topographic maps derived from ERS-1 interferometric radar", IEEE Trans. Geosci. Rem. Sens., 32, 823-836, 1994c. Wave Spectra

Figure 1.55 ERS-1 SAR image, Dundas Peninsula (in the Parry Islands of northern Canada), Aug. 17, 1994 (Copyright ESA 1994)

The exchange of energy between the ocean and atmosphere, between the upper layers of the ocean and the deep ocean, and transport within the ocean, all have a role in controlling the rate of global climate change and the patterns of regional change. Long continuity of measurements of sea surface temperature, winds, topography, geostrophic currents and ocean colour is essential.

Figure1.56 below, of the Gulf of Gaeta, Italy, shows the first multitemporal ERS-2/ERS-1 image acquired at Fucino (I) and Kiruna (S), processed by ESA/ESRIN: 578x900 pixels, 475 Kb. The ERS-1 and ERS-2 images of May 1st and 2nd reveal a difference in soil moisture due to changed weather conditions, shown by the greenish land colour. The colours of the sea correspond to different wind and current conditions during the three acquisitions, while black indicates calm areas at all acquisition dates.

Figure 1.56 Gulf of Gaeta, Italy ERS-1 ( blue: acquired Mar 27, 1995. green acquired May 1, 1995 ) and ERS-2 (red: acquired May 2, 1995)

ASAR products of interest to ocean scientists include wind speed and wave spectra from the Wave Mode. Other ASAR modes are of interest for wind field measurement, studies of internal waves and eddies and the detection of atmospheric phenomena, with Wide Swath and Global Monitoring Modes being of particular interest because of the larger area and more frequent coverage.

ASAR Wave Mode will provide wave spectra derived from imagettes of minimum size (5 km x 5 km), similar to the ERS AMI Wave Mode, spaced 100 km along-track in either HH or VV polarisation. The position of the imagette across-track can be selected to be either constant or alternating between two across-track positions over the full swath width.

ERS Wave Mode products are based on image spectra (wave number and direction) estimated from SAR intensity imagettes using standard Fourier Transform techniques. These products are therefore symmetric containing 180° propagation ambiguity. Techniques involving the use of Wave Model predictions have been developed to solve the ambiguity problem, though this can be subject to error when opposite or near opposite wave components exist.

For ASAR, this problem was solved by using the new wave product preserving the phase and a new algorithm called "inter-look cross spectral processing," whereby information on the wave propagation direction is computed from pairs of individual look images separated in time by a fraction of the dominant wave period. (Engen & Johnsen, 1995.) Figure1.57 shows a simulated ASAR Wave Mode Spectrum, with the top left plot being the real part of the cross spectrum (symmetric and equivalent to an ERS product) and the top right plot the new imaginary part (asymmetric, giving wave propagation direction).

image image image
Figure 1.57 SAR ocean image cross spectrum (real and imaginary part) processed from ERS-1 data using the ENVISAT ASAR Wave Mode Cross Spectra algorithm. The corresponding directional buoy spectrum is also shown. (Acknowledgement: NORUT IT, Norway.)

In the above example, the output from the new algorithm is seen to correspond with the wave direction provided by buoy measurements, as shown in the bottom of the figure.

Figure1.58 shows an example of a Level 1B ASAR wave spectra, as displayed by EnviView . Note that the image is anti-symmetric in this case. The image is a polar plot of ASAR wave spectrum data placed into discrete bins. Each wave spectra image is composed of 864 bins arranged in a polar plot.

  • The first bin (1,1) is at the top (from 0° to 10°) of the image.
  • The spectrum image is divided up into 36 sectors or 'spokes', each 10° wide. Note that the data is redundant due to the symmetry of the spectra, so only data from the first 180° of the spectra are provided (sectors 1 to 18). The other half of the spectrum display (sectors 19 to 36) can be constructed from the first half, as the spectrum is either symmetric or anti-symmetric.
  • Each radial sector or 'spoke 'contains 24 bins, numbered from 1 at the outside edge of the spectra display to 24 at the centre.

Level 1B ASAR Wave spectra image
Figure 1.58 Level 1B ASAR wave spectra image. Each bin is colour-coded based on the value of the bin data mapped onto a colour scale shown to the right-hand side of the image.

Figure1.59 below provides the Wave Mode Cross Spectra Format

Figure 1.59 Wave Mode Cross Spectra Format

The method for reconstructing the entire cross spectrum from that stored in the cross spectum Measurement Data Set (MDS) is shown below in Figure1.60 .

Figure 1.60 Method to reconstruct full cross spectra from product cross spectra Simultaneous Observations

The ENVISAT Mission offers simultaneous acquisition of ASAR and MERIS data, and this promises to be particularly valuable for ocean and coastal studies. With MERIS having a swath width of 1150 km around nadir, simultaneous data acquisition is possible up to ASAR incidence angles of approximately 34°, therefore including IS1 to IS5 in Image Mode, and all but the outer edge of the low-resolution modes. It will be possible to use ASAR data together with AATSR data, but simultaneous data acquisition is restricted to a narrow overlapping swath.

Figure1.61 provides an illustration of the type of simultaneous SAR and optical large area data acquisition that will be possible. In this example a low pressure system is seen on both a Wide Swath Radarsat image and a NOAA AVHRR image acquired within 30 minutes of each other. On the SAR image one sees the differences in sea surface roughness associated with the depression, while on the AVHRR image the same feature is depicted through cloud patterns.

image image image
Figure 1.61 A low pressure system seen on Radarsat Wide Swath (left) and NOAA AVHRR (right) images acquired on 30/3/97. Area shown on the Radarsat image is 500 km x 700 km. (Radarsat Data Copyright Canadian Space Agency/Agence spatiale Canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and distributed by Radarsat International. Imagery enhanced and interpreted by CCRS)

Figure1.62 below shows the combination of a SAR image and satellite derived sea surface temperatures. The ERS and
AVHRR images were acquired on 3 rd October 1992, off the west coast of Norway. In the AVHRR IR image the surface temperature decreases from nearly 14°C (white) in the coastal water to 12°C (purple) in the Atlantic water offshore. The pattern of the sea surface temperature field with the curvilinear temperature fronts represents meso-scale variability of
10 to 50 km, characteristic of the unstable Norwegian Coastal Current (Johannessen et al., 1994). The ERS image, acquired 7 hours later, contains frontal features at a scale, configuration and orientation that are in good agreement with those seen in
the IR image. The SAR image shows both bright and dark radar modulations of various width across the boundaries, which clearly show current boundaries including meanders.

Figure 1.62 Comparison of a 1 km resolution AVHRR image acquired at 14:20 on 3/10/92 (white is 14°C and purple is 12°C; + denotes buoy position; land is masked in green and clouds in black) and a 100m resolution ERS-1 SAR ( Copyright ESA 1992 ) image acquired at 21:35 on 3/10/92. Both images cover the same 100 km x 300 km region off the west coast of Norway between 59°N and 62°N. (Acknowledgement: J.A. Johannessen et al., 1994).

In figure1.63 below, the temperature information provided by the Advanced Very High Resolution Radiometer (AVHRR) satellite combined with the resolution of ERS-1 SAR data, both acquired on February 24, 1992, aids in calculating heat fluxes and in deriving salt fluxes for coastal shelf processes and global climate models. In the composite image of St. Lawrence Island shown below, the land mask is shown in blue.

Combined AVHRR and ERS-1 Image of St. Lawrence Island Alaska ( image courtesy of the Alaska SAR Facility) image Sigma-0 image Temperature
Figure 1.63 Combined AVHRR and ERS-1 Image of St. Lawrence Island Alaska (image courtesy of the Alaska SAR Facility).


Ref 1.27
ASAR Science Advisory Group, Editor R.A.Harris, European Space Agency 1998, "ASAR Science and Applications", ESA SP-1225

Ref 1.28
Johannessen J.A., Digranes G., Esdedal H., Johannessen O.M., Samuel P, Browne D, & Vachon P., 1994, "ERS-1 SAR Ocean Feature Catalogue", ESA SP-1174. October 1994.