ESA Earth Home Missions Data Products Resources Applications
EO Data Access
How to Apply
How to Access
ASAR Data Formats Products
Geolocation Grid ADSRs
Doppler Centroid parameters
Chirp parameters
Antenna Elevation pattern
ASAR external characterization data
ASAR external calibration data
Level 0 SPH
Level 0 MDSR
SPH for auxiliary data with N=1 DSDs
Wave Mode Geolocation ADS
ASAR Wave Mode Products Base SPH
Slant Range to Ground Range conversion parameters
Measurement Data Set containing spectra. 1 MDSR per spectra.
Ocean Wave Spectra
Map Projection parameters
ASAR Image Products SPH
Measurement Data Set 1
Auxilliary Products
ASA_XCH_AX: ASAR External characterization data
ASA_XCA_AX: ASAR External calibration data
ASA_INS_AX: ASAR Instrument characterization
ASA_CON_AX: ASAR Processor Configuration
Browse Products
ASA_WS__BP: ASAR Wide Swath Browse Image
ASA_IM__BP: ASAR Image Mode Browse Image
ASA_GM__BP: ASAR Global Monitoring Mode Browse Image
ASA_AP__BP: ASAR Alternating Polarization Browse Image
Level 0 Products
ASA_WV__0P: ASAR Wave Mode Level 0
ASA_WS__0P: ASAR Wide Swath Mode Level 0
ASA_MS__0P: ASAR Level 0 Module Stepping Mode
ASA_IM__0P: ASAR Image Mode Level 0
ASA_GM__0P: ASAR Global Monitoring Mode Level 0
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)
ASA_APC_0P: ASAR Alternating Polarization Level 0 (Copolar)
Level 1 Products
ASA_IMS_1P: ASAR Image Mode Single Look Complex
ASA_IMP_1P: ASAR Image Mode Precision Image
ASA_IMM_1P: ASAR Image Mode Medium Resolution Image
ASA_IMG_1P: ASAR Image Mode Ellipsoid Geocoded Image
ASA_GM1_1P: ASAR Global Monitoring Mode Image
ASA_APS_1P: ASAR Alternating Polarization Mode Single Look Complex
ASA_APP_1P: ASAR Alternating Polarization Mode Precision Image
ASA_APM_1P: ASAR Alternating Polarization Medium Resolution Image product
ASA_WSS_1P: Wide Swath Mode SLC Image
ASA_WVS_1P: ASAR Wave Mode Imagette Cross Spectra
ASA_WSM_1P: ASAR Wide Swath Medium Resolution Image
ASA_APG_1P: ASAR Alternating Polarization Ellipsoid Geocoded Image
Level 2 Products
ASA_WVW_2P: ASAR Wave Mode Wave Spectra
ASAR Glossary Terms
Sea Ice Glossary
Land Glossary
Oceans Glossary
Geometry Glossary
ASAR Instrument Glossary
Acronyms and Abbreviations
ASAR Frequently Asked Questions
The ASAR Instrument
Instrument Characteristics and Performance
Inflight Performance Verification
Preflight Characteristics and Expected Performance
Instrument Description
Internal Data Flow
ASAR Instrument Functionality
Payload Description and Position on the Platform
ASAR Products and Algorithms
Auxiliary Products
Common Auxiliary Data Sets
Auxiliary Data Sets for Level 1B Processing
Summary of Auxiliary Data Sets
Instrument-specific Topics
Level 2 Product and Algorithms
Level 2 Product
ASAR Level 2 Algorithms
Level 1B Products
ASAR Level 0 Products
Level 0 Instrument Source Packet Description
Product Evolution History
Definitions and Conventions
Organisation of Products
ASAR Data Handling Cookbook
Hints and Algorithms for Higher Level Processing
Hints and Algorithms for Data Use
ASAR Characterisation and Calibration
The Derivation of Backscattering Coefficients and RCSs in ASAR Products
External Characterisation
Internal Calibration
Pre-flight Characterisation Measurements
ASAR Latency Throughput and Data Volume
Data Volume
Products and Algorithms Introduction
Child Products
The ASAR User Guide
Image Gallery
Further Reading
How to Use ASAR Data
Software Tools
How to Choose ASAR Data
Special Features of ASAR
Geophysical Coverage
Principles of Measurement
Scientific Background
Geophysical Measurements
ASAR Product Handbook
ASAR instrument characterization data
Wave Mode processing parameters
ASAR processor configuration data
Main Processing parameters
ASA_WVI_1P: ASAR Wave Mode SLC Imagette and Imagette Cross Spectra
Product Terms
RADAR and SAR Glossary
Level 1B Products
Summary of Applications vs Products
Site Map
Frequently asked questions
Terms of use
Contact us


1.4 Image Gallery

Multi-look SAR Image

Figure 1.102 One look radar image showing speckled appearance

Figure 1.103 Multi-look radar image resulting from combining several images

The signal from the synthetic aperture radar can be exploited to produce an image. The radar image differs substantially from an optical image: it is, in reality, a map of apparent radar backscattering (coded as different grey levels), which not only depends on the target reflectivity at microwave wavelengths, but also depends on the viewing geometry. One additional salient feature of a microwave image, which makes it different from any optical image we are used to, is that the incoming light (in this case, the transmitted radar signal) is a monochromatic coherent light. As a result, the image appears speckled (see figure1.102 above). To reduce this effect several images are incoherently combined as if they corresponded to different looks of the same scene. The resulting improvement of the image interpretability is shown in the figure1.103 .

New ASAR Features

Figure 1.104 ASAR image using one polarisation choice

Figure 1.105 ASAR image of same area as in the other figure using different polarisation choice

Among the many new features that ASAR will present when compared, not only to ERS-1/2 but, to any other spaceborne flying SAR is the capability to transmit and receive signals with different polarisations (either vertical or horizontal). Because any given target responds in a different way when illuminated with a different polarisation (see example in the figure aside), the potential of this technique in terms of applications like classification, agriculture, detection are enormous.

ASAR will also be characterised by the capability to image large areas (up to 400 km swath width), thus reducing the revisiting time compared to ERS-1/2.

Glacial Topography

Figure 1.106 Interferogram from ERS tandem mission of part of the Vatnajokull Glacier, Iceland, May 1997

This is an interferogram from ERS tandem mission of part of the Vatnajokull Glacier, Iceland, May 1997. This shows glacial topography, including a major depression (400-600 m deep) in the upper central part of the image, caused by sub-glacial volcanic eruption (acknowledgement: H. Rott, Institut fr Meteorologie und Geophysik, Innsbruck, Austria). In this example, by comparing the phase difference between the images of the same scene taken by two slightly displaced points in space, a so-called interferogram is built. This example is from the ERS-1/2 tandem mission. Observations from separate ASAR passes can achieve a similar result.

Earth Movements Due To Earthquake

Figure 1.107 SAR differential interferogram in an area between Istanbul and the Lake of Sapanca showing ground displacement of 28 mm

Figure 1.108 SAR differential interferogram showing the surface deformation in an area between Istanbul and the Lake of Sapanca showing deformation of about 80 cm.

SAR interferometry can be used to quantify the dislocation produced by an earthquake. ERS-1 and 2 data were used to obtain a SAR differential interferogram showing the surface deformation in an area between Istanbul and the Lake of Sapanca. A theoretical deformation model derived from geophysical data was compared with the ERS SAR-derived phase interferogram.

The result of the modelled earthquake movement can be recomputed and displayed as fringes. The geophysical interpretation of the model is that the rupture occurred along an east-west fault, causing a predominantly horizontal movement (right-lateral strike). In the interferogram in figure1.107 , each colour cycle from red to yellow corresponds to a ground displacement of 28 mm in the slant range direction (ERS satellite's viewing direction). By counting the number of fringes, one can calculate the co-seismic deformation. In the figure1.108 , 28 ± 2 fringes can be observed across the image. They suggest a deformation of about 80 cm.

Wind Field Distribution

Figure 1.109 Large part of Lake Ijssel in The Netherlands

Figure 1.110 Large part of Lake Ijssel in The Netherlands showing wind field obtained from the SAR

The ERS-1/2 missions have successfully demonstrated that the radar cross-section measured over the oceans can be related to wind speed. The retrieval of the wind field from a SAR image is split into two parts. First, the wind direction is determined from wind-rows, which are often visible on the SAR image. In the second stage, the radar backscatter measured by the SAR is related to the wind speed, given the wind direction from the wind streaks.

The top SAR image, figure1.109 , shows a large part of Lake Ijssel in The Netherlands. The wind field obtained from the SAR image is shown below it in figure1.110 .

Forest Cover Classification

Figure 1.111 Classified forest image overlayed on a DEM, generated using InSAR techniques

Comparing imagery from sequential SAR passes shows different degrees of coherence. Bare or sparsely vegetated soil has a high degree of coherence as there is little or no change in the scatterer properties between the two acquisitions. Forested areas, on the other hand, show a low degree of coherence, as the elementary scatterers (i.e. leaves) in each pixel move between the two acquisitions, mainly due to wind and, hence, lead to decorrelation in the imagery. This fact can be exploited to discriminate between forest and non-forest vegetation. Moreover, in this case, given two coherence images, one prior to the storm (4/5 April 1999) and one after the storm (9/10 January 2000), a change within forested areas from low coherence to high coherence should be indicative of forest damage. Using the coherence combined with the backscatter data, a supervised classification was carried out to identify forest areas damaged, as well as the other cover types in the scene. The classified image overlayed on a DEM, generated using InSAR techniques, is shown in figure1.111 above.

Ice Classification

Figure 1.112 A multi-temporal image of Iceland

By combining three images acquired over different passes (over Iceland, in the figure above), a multi-temporal image can be produced (the colours blue, green and red are assigned in increasing date order).

Snow Cover

Figure 1.113 ERS-2 ascending and descending passes showing snowmelt runoff

The extent of snow covered area is a key parameter for snowmelt runoff modelling and forecasting. Because SAR sensors provide repeat pass observations, irrespective of cloud coverage, they are of interest for operational snowmelt runoff modelling and forecasting.

The algorithm for mapping melting snow is based on repeat pass images of C-band SAR and applies change detection to eliminate the topographic effects of backscattering. At C-band dry snow is transparent and backscattering from the rough surfaces below the snowpack dominates. This is the reason why the return signal from dry snow and snow-free areas is very similar. When the snow becomes wet, backscattering decreases significantly. Therefore wet snow can be detected by the backscatter changes when compared to dry snow or snow- free conditions [Nagler,1996 ].

An example of snow maps derived from ERS-2 ascending and descending passes are shown above figure in figure1.113 (on 12 May 1997 in blue and green and on 16 June 1997 in green only),

Storm Damage Assessment

Figure 1.114 Land cover measured with SAR before a storm in the forest of Haguenau, 30 km north of Strasbourg

Figure 1.115 Land cover measured with SAR after a storm in the same forest of Haguenau, as shown in the figure above showing a strong increase of the coherence level within forested areas.

Figure 1.116 Damage image composite of the land cover measured with SAR after a storm in the same forest of Haguenau, as shown above.

Figure1.114 , figure1.115 and figure1.116 above show the comparison of land cover measured with SAR before (figure1.114 ) and after (figure1.115 and figure1.116 ) a storm in the forest of Haguenau, 30 km north of Strasbourg. The top image is a coherence standard product showing bare soils and cultivated areas as orange-red, and wooden areas as green.

After the storm, the coherence product shown in figure1.115 , indicates a strong increase of the coherence level within forested areas.

In the 'damage' image composite, shown in figure1.116 , pink tones provide an estimate of the level of the damage. In this case, a level of damage of 50% had been reported by the forest service which corresponds to the increase of coherence over the area. This imagery was taken 31 Oct 1999 and 9 January 2000.

Flood Monitoring

Figure 1.117 SAR image of flooded area

Floods are among the most severe risks on human lives and properties. The forecast and simulation of floods is essential for planning and operation of civil protection measures (e.g.dams, reservoirs) and for early flood warning (evacuation management). The economic importance of flood forecasting becomes clear considering that 85 % of civil protection measures taken by the EC Member States are concerned with floods (EC Report Task Force Water,1996 ). Floods can be monitored in real time by ASAR as shown by the example given in figure1.117 above..

Soil Moisture And Flood Forecasting

Figure 1.118 An example of top soil moisture distribution

Hydrological modelling for flood forecast is widely applied and makes use of satellite remote sensing data. A remotely sensed, interferometrically derived, elevation model is used to determine topographic information on the watershed. Together with a soil map, the watershed is then classified into hydrologically relevant classes of water storage capacity.

For the dynamic part of the system, which deals with a specific flood event, rainfall information is required as driving variable. In addition, soil moisture information is of relevance for runoff modelling because it determines the extent of saturation of the watershed and thereby the partitioning of rainfall into surface runoff and infiltration. The same amount of rainfall, which normally does not lead to a significant increase in water level, can cause a severe flood, if the soil has already been filled with water and the storage capacity is close to zero.

SAR data is used in the model also to derive soil moisture distributions to improve the antecedent moisture characterisation of the watershed. The basis of the approach for surface soil moisture determination from SAR is an algorithm developed for ERS data (Mauser et al. 1995 )which was already successfully used in a series of applications (Rombach &Mauser 1997,Schneider & Oppelt 1998 ). An example of top soil moisture distribution is illustrated in figure1.118 above.

Sea Ice Navigation

Figure 1.119 Ship performance plotted on an ERS-SAR scene (Image courtesy of Nansen Environmental and Remote Sensing Center)

Radar extracted sea-ice information can satisfy operational needs for navigation, offshore operations and weather forecasting.

Radar images downloaded via the Internet are used in real time to organise icebreaker interventions and to address vessel routes. The image in figure1.119 above illustrates ship performance plotted on an ERS-SAR scene (left: four days after the pass of an icebreaker, right: eight days after). The distance between each point represents one hour of sailing. Use of lower resolution modes, such as wide swath and global monitoring modes, provided from ASAR will offer the possibility to monitor larger areas with more frequent revisits. The variable incidence angle can be used to enhance sea-ice edges. Polarisation will allow improved ice-type discrimination and probably will help in forecasting of leads or ice pack development.

Image Mode Medium-Resolution ASAR Image

Figure 1.120 Image Mode Medium-Resolution Image of Walgreen Coast, Antarctica generated from ERS raw data using the ASAR processing algorithm

Image Mode Precision ASAR Image

Figure 1.121 Image Mode Precision Image, generated from ERS raw data using the ASAR Processor, of Bathurst Island, Canada.

Pixel = 12.5 m
Spatial Res. = 30 m
ENL = 3.9
Coverage = 56 to 107 km width x 100 km