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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
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1.1.6 Summary of Applications vs Products Summary of Applications Introduction

The original focus of the ERS missions was oceans and ice monitoring, and there has been an impressive range of scientific investigations in oceanography, polar science, glaciology, and climate research which will be supported by ASAR. These include measurements of ocean surface features (currents, fronts, eddies, internal waves), directional ocean wave spectra, sea floor topography, snow cover and ice sheet dynamics. Operational systems have been developed for mapping sea ice, oil slick monitoring and ship detection.

The ASAR Instrument will provide continuity and improvement upon the ocean, coastal one, and land cover monitoring capabilities of the previous ERS SAR instruments. ASAR promises to be important for modelling changes in vegetation, oceans, ice sheets, snow and sea ice. Data is required in order to initiate or validate models, and for long-term monitoring over global to regional scales, as well as smaller areas of particular interest.

Three different groups of users of ASAR data are:

  1. Remote Sensing Science
  2. Earth Science Community
  3. Commercial Applications Remote Sensing Science

A considerable amount of research has been undertaken into the processing and use of spaceborne SAR data from ERS-1 and ERS-2, as well as other SAR instruments. The need for further research will continue, both in data processing and analysis techniques, and in the development of geophysical retrieval models. Research will be undertaken by a range of users from universities and research institutes, to value-added companies and industries seeking to improve the product or service they are selling or using. The availability of multi-polarised data and data from the Global Monitoring Mode will be of particular interest.

Preliminary processing of data at ground stations and by value-added companies includes calibration and validation of data, feature detection, texture analysis, reduction of speckle, image registration, geocoding and radiometric corrections. Techniques are continually being developed; particularly when data from a new sensor like ASAR becomes available. Data from all modes will be required for specific study sites over land, ocean and ice.

The availability of data from multiple incidence angles provides opportunities to develop new data processing techniques which may give new information on soil moisture, forest characteristics, geological structure, etc. Data will be required at all modes and polarisations for specific study sites covering a range of incidence angles.

Algorithm Development

Considerable ocean-related research is currently being undertaken by defence departments to monitor fishing boats and ships in busy traffic lanes. The likelihood of detecting ships and ship wakes will improve with the use of multi-polarised data. In the coastal zones, research is currently being undertaken to develop methods of mapping shorelines and the sea bottom in shallow areas. Some of these applications are now operational and will continue to expand with ASAR.

Much of the basic research on classification of ice types has been carried out, and good algorithms are available. The value of multi-polarised data for ice type discrimination, especially during the ice formation and ice melting periods has been demonstrated using airborne systems. Data from both the Alternating Polarisation and Wide Swath (HH and VV) Modes available with ASAR will be utilised throughout the year for test areas in the Arctic Ocean.

To develop agricultural and vegetation products, multi-temporal data across the major crop and cover types, from which backscatter models of crop type, area, height and condition will be developed.

Backscatter of Sitka Spruce and Upland Pasture at Llyn Brianne. Wales.
Figure 1.64 Backscatter of Sitka Spruce and Upland Pasture at Llyn Brianne, Wales. (Acknowledgement: Luckman and Baker, 1995.) Above: Polarisation and incidence angle effects on backscattering. Below: Relative contributions of surface, double-bounce and volume scattering.

Forest mapping is of particular interest in the humid tropics and other persistently cloudy areas. Important research topics include the classification of forest types, identification of burned forest, assessment of forest stress and monitoring of logging concessions. The availability of multi-polarised and variable incidence angle data from ASAR should improve on the accuracy of ERS results. For example, figure1.64 above shows how backscatter varies with VV, HH and HV polarisations across a range of incidence angles, for Sitka Spruce and Upland Pasture, and how these measurements have been used in a decomposition model to determine the relative contribution of surface, double-bounce and volume scattering mechanisms.

Many hydrological and agricultural applications use soil moisture data. Current research is investigating the relationship between soil moisture and backscatter across a range of soil conditions (Le Toan et al, 1994). The use of multi-polarised and multi-incidence angle data should increase the accuracy of models by reducing the effect of surface roughness and vegetation. There is a strong interest in the use of Wide Swath and Global Monitoring Modes, because of the much improved temporal frequency of coverage (Zmuda et al, 1997). Snow melt and hydrology applications require information on snow cover distribution and snow water equivalent.

Table 1.4 provides a list of the ASAR modes for applications being addressed by remote sensing science. In particular, it shows a very strong demand for Alternating Polarisation (AP) Mode. The other modes shown are: Image (IM) Mode and Wide Swath (WS) Mode.

Table 1.4 ASAR Modes for Remote Sensing Science
Mode Polarisation Swath Remarks
Agriculture AP VV/VH IS4-6 Multi-temporal
Land cover AP VV/VH IS4-6 Multi-temporal
Forestry AP VV/VH' IS4-6 Multi-temporal and interferometry
Soil moisture AP VV/VH IS1-3 High revisit
Snow melt IM HH or VV IS3-7 High revisit
Hydrology AP VV/VH IS2-5 High revisit
Geology IM HH IS4-7
Urban mapping AP HH/HV IS3-7
Inland water IM VV IS2-4
Oceanography AP VV/HH IS2-6
Coastal phenomena AP VV/HH IS2-6
Sea ice AP VV/HH IS2-6
Ship detection AP HH/HV IS2-7
Marine meteorology AP VV/HH IS2-6
Pollution monitoring WS VV Earth Science

The past decade has seen increasing public concern about the Earth, its environment and mankind's impact upon it. Global threats such as climate warming, stratospheric ozone depletion, tropospheric pollution and, more recently, regional events such as the very intense El Niño, the fires in S.E. Asia, and the floods in mid Europe and China, have left us more concerned than ever about the need to monitor and understand what is going on in the Earth's environment. There are many aspects of the complex evolving Earth System that we still do not understand.

These concerns have led to the establishment of the concept of a Global Climate Observing System (GCOS), including both space- and surface-based systems, to measure on a routine basis all major elements of the global climate system. Table 1.5 below provides a summary of the principal observations required in support of GCOS.

Ultimately, the way in which our understanding of the Earth will improve, is by the development of Earth System models which integrate into data from various sources. Earth observation from space is a critical tool in this task because of the unique synoptic view and high repeat frequency that it provides. Some of the earliest initiatives, including METEOSAT and SPOT, have already developed into long-term applications programmes integrated into regular operational use. The ERS satellites have made major contributions in areas as diverse as global and regional ocean and atmospheric science, sea ice, glaciology and snow cover investigations, land surface studies and the dynamics of the Earth's crust (seismology and volcanology). ENVISAT will provide new capabilities to monitor atmospheric composition and chemistry, with ASAR providing continuity and improvement upon the ocean, coastal zone and land cover monitoring capabilities of the ERS SAR instruments.

ASAR promises to be particularly important for modelling and monitoring changes in vegetation, oceans, ice sheets, snow and sea ice.

Table 1.5 Summary of GCOS principal observations
Cloud Amount
Cloud Drop Size Distribution
Surface Fluxes (heat, water)
Global Solar Irradiance
Radiative Surface Radiation Fluxes
Properties Earth Radiation Budget
GLOBAL Multispectral Albedo
Ocean Colour
Ocean Topography/Geoid
Ocean Sea Ice Cover
Characteristics Sea Surface Temperature
Ocean Salinity
Sea Surface Temperature
Ocean Ocean Wind Vectors/Seed
Atmosphere Sea Ice Cover (as tracer)
Boundary Ocean Wave Height Spectra
OCEANS Atmospheric Surface Pressure
Temperature Profile
Cloud Clearing
Atmospheric Wind Profile
Thermodynamics Liquid Water/Ice
Humidity (profile/total)
Constituents (total/profile)
Atmospheric Atmospheric Dynamics
Composition Ozone (total/profile)
ATMOSPHERE & Chemistry Aerosols (total/role)
Vegetation Characteristics
Land Soil Moisture
Atmosphere Snow & Ice Cover
Interaction Land Surface Temperature
Vegetation Change
Land Land Use Change
LAND Biosphere Climate Response

The following applications are considered to be the major areas of use for ASAR data within Earth Sciences:

  • Vegetation Monitoring
  • Sea Ice Monitoring
  • Glaciology and Snow Mapping
  • Oceanography
  • Coastal Zone Processes

These primary ASAR applications are described in more detail in the following sections.

Table 1.6 below gives a summary of the ASAR modes mostly likely to meet the various needs of the Earth Sciences community.

Table 1.6 ASAR Modes for earth science
Mode Polarisation Swath Remarks
Vegetation maps WS VV or HH Large area cover, multi-temporal
Soil moisture estimation WS VV
Surface motion and subsidence IM VV or HH IS2-5 Using interferometry
Oceanography WS HH
Coastal phenomena AP VV/HH IS2-6
Marine meteorology WS VV
Wind/Wave Models WM VV
Glacier/ice sheet motion IM HH or VV IS3-6
Ice sheet extent and melt areas WS HH or VV
Snow climatology WS and GM HH or VV
Wetlands WS VV
Sea Ice WS and GM HH Commercial Applications

The growing availability of Earth observation data is encouraging increased involvement and investment by value-added organisations and end-users. These include government departments with responsibility for agriculture, the environment, pollution control, meteorology, coastal protection, transport (especially coastal, marine and ice), fisheries and hazard management.

The following applications are considered to be the major areas for commercial exploitation of ASAR data:

  • Ship Routing through Sea Ice
  • Sea Ice Monitoring
  • Ocean Monitoring
  • Monitoring Oil Slicks
  • Nowcasting of ocean fronts, eddies and current shears
  • Agricultural Monitoring
  • Forestry
  • Hydrology and Water Management
  • Flood Mapping

These commercial ASAR applications are described in more detail in following sections.

Table 1.7 ASAR Modes for commercial applications
Mode Polarisation Swath Remarks
Ship routing in sea ice
Sea ice extent WS HH
Ocean monitoring
Surface features AP HH/HV IS2-6
Oil slicks WS VV
Ship detection APorIM HH/HVHH IS2-7, IS5-7
Bathymetry IM HH IS2-5
Agricultural monitoring
Crop area AP VV/VH or VV/HH IS4
Crop condition IM VV or HH IS2-7
Soil moisture WS VV High repeat
Forest area/type/condition AP VV/VH IS4-6
Hydrology, and water management
Runoff forecasts WS VV High repeat
Flooding WS HH High repeat
Oil and as industry
Geological ma IM HH IS4-7
Natural hazards
Earthquakes/volcanoes /land subsidence IM VV or HH IS1-7 Ocean Applications

The oceans not only provide valuable food and biophysical resources, they also serve as transportation routes, are crucially important in weather system formation and CO2 storage, and are an important link in the Earth's hydrological balance. Understanding ocean dynamics is important for fish stock assessment, ship routing, predicting global circulation consequences of phenomena such as El Niño, forecasting and monitoring storms so as to reduce the impact of disaster on marine navigation, offshore exploration, and coastal settlements. Studies of ocean dynamics include wind and wave retrieval (direction, speed, height) , mesoscale feature identification, bathymetry, water temperature, and ocean productivity.

Ocean feature analysis includes determining current strength and direction, amplitude and direction of surface winds, measuring sea surface temperatures, and exploring the dynamic relationship and influences between ocean and atmosphere. Knowledge of currents, wind speed, tides, storm surges and surface wave height can facilitate ship routing. Sea floor modelling supports waste disposal and resource extraction planning activities.

Ocean circulation patterns can be determined by the examination of mesoscale features such as eddies, and surface gravity waves. This knowledge is used in global climate modelling, pollution monitoring, navigation and forecasting for offshore operations.

Remote sensing offers a number of different methods for acquiring information on the open ocean and coastal region. Scatterometres collect wind speed and direction information, altimeters measure wave height, and identify wind speed. SAR is sensitive to spatially varying surface roughness patterns caused by the interaction of the upper ocean with the atmosphere at the marine boundary layer, and scanning radiometers and microwave sounders collect sea surface temperature data. Buoy-collected information can be combined with remote sensing data to produce image maps displaying such things as hurricane structure with annotated wind direction and strength, and wave height. This information can be useful for offshore engineering activities, operational fisheries surveillance and storm forecast operations.

ASAR provides an option for acquiring information on the open ocean and coastal region. Several new SAR ocean applications can be expected to reach pre-operational or operational status during the lifetime of ENVISAT, notably in the areas of pollution monitoring, ship detection, and ocean feature nowcasting. This information can be useful for offshore engineering activities, operational fisheries surveillance, and storm forecast operations.

Some of the key areas of interest will include the following:

  • Wave Characteristics
  • Ocean Fronts
  • Coastal Dynamics
  • Oil Slicks and ShipTraffic

Wide area coverage is useful for monitoring and surveillance applications including ship traffic, fisheries monitoring, oil spill mapping, and ocean circulation mapping. Intermediate area coverage is useful for monitoring ship traffic, near-shore fisheries activities, oil spill mapping, and inter-tidal feature mapping. Small area coverage is useful for harbour traffic monitoring, aquaculture site location and small spill mapping. Wave Characteristics

For general sea-state information (waves, currents, winds), the data is usually time-sensitive; meaning that the information is only valuable if it is received while the conditions exist. ASAR data is expected to play a key role in the study of wave characteristics.

Certain wind speed conditions are necessary in order for the SAR to receive signal information from the ocean surface. At very low wind speeds (2 to 3m/s) the SAR is not sensitive enough to detect the ocean "clutter" and at very high wind speeds (greater than 14 m/s) the ocean clutter masks whatever surface features may be present. The principal scattering mechanism for ocean surface imaging is Bragg scattering, whereby the short waves on the ocean surface create spatially varying surface patterns. The backscatter intensity is a function of the incidence angle and radar wavelength, as well as the sea state conditions at the time of imaging. The surface waves that lead to Bragg scattering are roughly equivalent to the wavelength used by ASAR and RADARSAT (5.3 cm). These short waves are generally formed in response to the wind stress at the upper ocean layer. Modulation in the short (surface) waves may be caused by long gravity waves, variable wind speed, and surface currents associated with upper ocean processes such as eddies, fronts and internal waves. These variations result in spatially variable surface roughness patterns which are detectable on SAR imagery, as shown in figure1.65 .

Atmospheric Waves
Figure 1.65 Atmospheric Waves (Copyright 1994, European Space Agency)

The SAR data for this image was taken by the European Space Agency's ERS-1 satellite on August 17, 1994. The scene shows the southern coast of Melville Island's Dundas Peninsula (in the Parry Islands of northern Canada), with north pointing about 30 degrees to the right.

Internal waves form at the interfaces between layers of different water density, which are associated with velocity shears (i.e., where the water above and below the interface is either moving in opposite directions or in the same direction at different speeds). Oscillations can occur if the water is displaced vertically resulting in internal waves. Internal waves in general occur on a variety of scales and are widespread phenomena in the oceans. The most important are those associated with tidal oscillations along continental margins. The internal waves are large enough to be detected by satellite imagery. In the image shown below, the internal waves, are manifested on the ocean surface as a repeating curvilinear pattern of dark and light banding, a few kilometres east of the Strait of Gibraltar, where the Atlantic Ocean and Mediterranean Sea meet. Significant amounts of water move into the Mediterranean from the Atlantic during high tide and/or storm surges. (See figure1.66 )

Figure 1.66 ERS 1 scene of internal waves: Strait of Gibraltar ( ESA 1992)

One ASAR study being proposed by Dr. Olga Lavrova, a senior scientist at the Space Research Institute Russian Academy of Sciences in Russia, plans to investigate circulation processes in the ocean and atmosphere (transformation of speed field, energy and momentum transfer) for the case of a stratified flow running against natural obstacles. The study will employ theoretical and experimental investigation of the spatial and temporal structure and dynamics of waves, vortexes, and vortex streets that emerge behind small islands, capes, rocks, and underwater rapids in the presence of currents in the ocean, and due to air flows on the shore and islands in the atmosphere. In addition to the study of forms and parameters of these lee structures, their relation to the speed of the run-against flow, the stratification of the media and the morphometry of the obstacle will be considered. This paves the way for the estimation of flow speed and media density stratification from space.

This project envisages development of numerical models based on the classical hydrodynamic theory of stratified fluid running against an obstacle. Process hydrodynamic characteristics retrieved from ASAR images will serve as input parameters for the models. The models will be used to retrieve current characteristics in ocean and wind fields in atmosphere above ocean from remote sensing data. Experimental tests based on the models will allow to observe in time and space the stages of circulation processes around natural obstacles to flows. Ocean Fronts

There is increasing interest in the maritime community in high-precision nowcasting of ocean fronts, eddies and current shears. Important application areas could be: piloting of large transport ships, fisheries and fish farming, sea floor operations and autonomous underwater vehicles, acoustic sensors and acoustic communication. Also, ASAR imagery, together with data from other ENVISAT instruments such as MERIS and AATSR, will significantly enhance the nowcasting of ocean features in coastal waters.

Open ocean applications include the study of large-scale ocean features manifested at the ocean surface by the interaction of wind-driven currents with the marine boundary layer. The principle scattering mechanism for ocean surface imaging is Bragg scattering, whereby the short waves create spatially varying surface patterns. The backscatter intensity is a function of the incidence angle and radar/wavelength, as well as the wind and wave condition at the time of imaging. For RADARSAT (5.3 cm wavelength), the surface waves that lead to Bragg scattering are roughly equivalent to its wavelength. These short waves are generally formed in response to the wind stress at the marine boundary layer. Modulation in the short waves may be caused by long gravity waves, variable wind speed, and surface currents associated with upper ocean processes such as eddies, fronts, and internal waves. These variations result in spatially variable surface roughness pattern which is imaged by the SAR. Coastal Dynamics

Radarsat Image of Coastal Region (courtesy Radarsat International )
Figure 1.67 RADARSAT image of coastal region (courtesy Radarsat International)

Coastlines are environmentally sensitive interfaces between the ocean and the land, and respond to changes brought about by economic development and changing land-use patterns. Often coastlines are biologically diverse inter-tidal zones and can also be highly urbanised. With over 60% of the world's population living close to the ocean, the coastal zone is a region subject to increasing stress from human activity. Government agencies concerned with the impact of human activities in this region need new data sources with which to monitor such diverse changes as coastal erosion, loss of natural habitat, urbanisation, effluents and offshore pollution. Many of the dynamics of the open ocean and changes in the coastal region can be mapped and monitored using remote sensing techniques.

Coastal zone monitoring implies observation of the interaction of oceanographic and atmospheric phenomena with human activities in the near-shore region. The key issues include the delineation of the coastline, defining areas of erosion and sedimentation, mapping the inter-tidal vegetation, and identifying areas of human settlement and accompanying activities. The coastal zone is an environmentally sensitive region subject to increasing stress from economic development, and government agencies concerned with the impact of human activities in the near-shore region are looking for new data sources with which to monitor this region.

An excellent coastal zone application of radar is aquaculture site monitoring. These man-made structures provide higher signal returns than the surrounding water.

The main areas of interest in the coastal zone are changes in sea level and in suspended sediment, carbon, and nutrients. Activities are being undertaken, at a range of scales, using diverse data sets for ocean measurements, land use, vegetation and coastal morphology. There are numerous local, national, regional, and international programmes involved in the coastal zone. Major programmes include the International Oceanographic Commission, the MAST programme organised by the EC, and the IGBP Land-Ocean Interactions in the Coastal Zone (LOICZ) programme to determine how changes in the Earth's system are affecting coastal zones and altering their role in global cycles.

ASAR data will certainly be used within the range of activities in the coastal zone. Examples of current use of SAR data in the coastal zone include: topographic maps of tidal flats, sea bed topography, sediment distribution in The Netherlands, an inter-tidal digital terrain model of the Wash in the UK, and coastal erosion in French Guiana.

The availability of multi-polarised data and data at different incidence angles, or at a specific incidence angle, should improve the accuracy and quality of products for many applications. The Wide Swath (WS) and Global Monitoring (GM) Modes will provide data that is not currently available, for applications requiring large area coverage.

For example, a new conceptual scheme in coastal research being proposed by Dr. Francis Gohin, a Physical Oceanographer at IFREMER in France, is to deploy optical instruments, combined with airborne and spaceborne spectral and SAR imagers like ASAR, to provide an up-to-date means of observing the narrow bands of red tides. By integrating colour data obtained from aircraft and satellites in classical data sets, a 3-D numerical model will provide estimation of the chlorophyll content and the suspended matter concentration on the continental shelf of the Bay of Biscay. Remote sensing methods can be used in the validation of such models. In return, these models help to include passive remote sensing data, poorly sampled in time because of clouds, in a regular set of simulations.

In addition, a study being proposed by researcher Samuray Elitas M.Sc. of the TUBITAK Marmara Research centre in Turkey, envisions ASAR data being used to analyse coastal regions there. As ASAR and multicolour MERIS images for the project area arrive, the most recent and/or simultaneous pollution mapping of the Marmara Sea will be evaluated by relating other geographical information data like bathymetrical information and land usage information. Consequently, geographical information systems for the Marmara marine environment, supported by ASAR and MERIS images will be established as a whole database.

The effects of bathymetry are visible in near-shore regions under light wind conditions. Small incidence angles are better suited to imaging inter-tidal features such as mudflats, shoals and sandbars.

Large incidence angles provide a larger radar backscatter contrast which improves the discrimination of the water/land boundary. The smooth surface of a water body acts as a specular reflector in contrast to the diffuse scattering which occurs over land. Open water surfaces will appear dark in comparison to the brighter returns from land. Shoreline detection and the identification of areas of erosion or sedimentation can be improved by acquiring multi-temporal data with different look directions (e.g., ascending or descending). Oil Slicks and ShipTraffic

Radarsat Image of Coastal Oil Spill, Wales ( Courtesy CCRS )
Figure 1.68 RADARSAT image of coastal oil spill, Wales (Courtesy CCRS)

Oil spills can destroy marine life as well as damage habitat for land animals and humans. The majority of marine oil spills result from ships emptying their billage tanks before or after entering port. Large area oil spills result from tanker ruptures or collisions with reefs, rocky shoals, or other ships. These spills are usually spectacular in the extent of their environmental damage and generate wide spread media coverage. Routine surveillance of shipping routes and coastal areas is necessary to enforce maritime pollution laws and identify offenders.

Remote sensing offers the advantage of being able to observe events in remote and often inaccessible areas. For example, oil spills from ruptured pipelines may go unchecked for a period of time because of uncertainty of the exact location of the spill, and limited knowledge of the extent of the spill. Remote sensing can be used to both detect and monitor spills.

For ocean spills, remote sensing data can provide information on the rate and direction of oil movement through multi-temporal imaging and input to drift prediction modelling, and may assist in targeting cleanup and control efforts. Remote sensing devices used include infrared video and photography from airborne platforms, thermal infrared imaging, airborne laser fluourosensors, airborne and spaceborne optical sensors, as well as airborne and spaceborne SAR. SAR sensors have an advantage over optical sensors in that they can provide data under poor weather conditions and during darkness. Users of remotely sensed data for oil spill applications include the Coast Guard, national environmental protection agencies and departments, oil companies, shipping industry, insurance industry, fishing industry, national departments of fisheries and oceans, and departments of defence.

Oil slicks and natural surfactants are imaged through the localised suppression of Bragg scale waves. Under calm conditions, natural surfactants may form over large areas of the ocean, along current boundaries, and in areas of upwelling. The accumulation of natural surfactants at these boundaries can delineate the general circulation pattern and are visible on the radar image as curvilinear features with a darker tone than the surrounding ocean. Oil spills also have a darker tone with respect to the surrounding ocean background. The detection of an oil spill is strongly dependent upon the wind speed. At wind speeds greater than 10 m/s, the slick will be broken up and dispersed, making it difficult to detect. Another factor that can play a role in the successful detection of an oil spill is the difficulty in distinguishing between a natural surfactant and an oil spill. Multi-temporal data and ancillary information can help to discriminate between the two phenomena. Wind shadows near land, regions of low wind speed, and grease ice can also be mistaken for oil spills and ancillary data (or an experienced user) is necessary to distinguish between these features and a spill.

Oil companies are now actively using ERS SAR imagery in their search for new oil fields (oil seepage from the ocean floor is an important indicator). The ASAR Wide Swath Mode in VV polarisation will be a unique instrument for detection of oil slicks on the ocean surface, offering a very good combination of wide coverage and radiometric quality. The fourfold increase in coverage capability compared to ERS will make routine services feasible also at lower latitudes.

Small incidence angles are optimum for oil spill detection. Detection will also depend on the spill size, sea state conditions and image resolution.

At the Norwegian Computing Centre, senior research scientist Anne Solberg proposes to modify algorithms for automatic detection of oil spills originally developed for ERS SAR images, for use with ASAR images. Methods have been developed for automatic detection of oil spills in ERS images as part of the Norwegian oil spill project and the European Union project ENVISYS. ASAR Wide Swath data will have a different pixel size and a different radiometric resolution than the ERS SAR images, and these will be incorporated in the detection and classification algorithms. The new project will use ASAR data from four test sites with a high probability of observing oil slicks: the North Sea, the English Channel, and two sites in the Mediterranean.

The image shown in figure1.69 below, taken over the "Flemish Cap," an area in the Atlantic Ocean south east of the coast of Newfoundland Canada, shows two natural slicks (A) and five ships. Two of the ships can be identified to the east of the slicks and three are clustered to the south. Wakes are clearly visible behind the three ships at the bottom of the image. This information can be used to determine their speed and direction of travel.

Radarsat Image of Flemish Cap, East Coast of Canada ( courtesy of CCRS )
Figure 1.69 RADARSAT image of 'Flemish Cap,' east coast of Canada (courtesy of CCRS)

With larger incidence angles, the ocean background clutter effects are reduced, improving the detection of ships, coastline and ice edges. For example, a ship is a bright point target against the ocean background clutter and can be detected using image thresholding techniques. However, as the ocean clutter increases with increasing wind speeds, ship detection becomes more difficult. At wind speeds greater than 10 m/s it is difficult to detect small fishing vessels. This relationship with wind speed is a critical factor for ship detection as well as oil spill mapping and feature detection. As the wind speeds increase, the radar cross-section of the ocean increases, reducing the contract between the feature of interest and the surrounding ocean.

Ship detection is a good example of the operational role of radar. A wide range of ship sizes may be detected under a variety of sea-state conditions. Radar can infer ship size, and if a wake is present, its speed and direction of travel. It should be noted that an HH polarisation is less sensitive to wake detection and, in studies to date, wakes are infrequently detected. Potential users of this information include agencies who monitor ship traffic, authorities responsible for sovereignty and fisheries surveillance, as well as customs and excise agencies charged with stopping illegal smuggling activities. (See figure1.70 )

Ship Wake
Figure 1.70 Ship Wake. ESA image courtesy of the Alaska SAR Facility (copyright ESA)

Large incidence angles are optimum for ship target detection. Detection depends on ship size and type, heading with respect to look angles, and sea state conditions at the time of imaging. Land Applications

Figure 1.71 Multi-temporal, artificially coloured image ERS-1 SAR image, Germany-Darmstadt and Odenwald, Nov.11, 1991 (Copyright ESA 1991)

Although the terms land cover and land use are often used interchangeably, their actual meanings are quite distinct. Land cover refers to the surface cover on the ground, whether vegetation, urban infrastructure, water, bare soil or other. Identifying, delineating and mapping land cover is important for global monitoring studies, resource management, and planning activities. Identification of land cover establishes the baseline from which monitoring activities (change detection) can be performed, and provides the ground cover information for baseline thematic maps.

Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture. Land use applications involve both baseline mapping and subsequent monitoring, since timely information is required to know what current quantity of land is in what type of use and to identify the land use changes from year to year. This knowledge will help develop strategies to balance conservation, conflicting uses, and developmental pressures. Issues driving land use studies include the removal or disturbance of productive land, urban encroachment, and depletion of forests.

It is important to distinguish this difference between land cover and land use, and the information that can be ascertained from each. The properties measured with remote sensing techniques relate to land cover, from which land use can be inferred, particularly with ancillary data or a priori knowledge.

Resource managers involved in parks, oil, timber, and mining companies, are concerned with both land use and land cover, as are local resource inventory or natural resource agencies. Changes in land cover will be examined by environmental monitoring researchers, conservation authorities, and departments of municipal affairs, with interests varying from tax assessment to reconnaissance vegetation mapping. Governments are also concerned with the general protection of national resources, and become involved in publicly sensitive activities involving land use conflicts.

C-band Synthetic Aperture Radar (SAR) satellites sensors, such as ERS-1, ERS-2 and ASAR, allow the agricultural industry to acquire imagery anytime, using microwave energy to penetrate darkness, clouds, rain or haze ( See "Scientific Background" 1.1.2. ). As such, they have become an invaluable source of information in mapping the aftermath of natural disasters like hail storms, floods and hurricanes.

As a result of observing the land surface with the ERS Synthetic Aperture Radar (SAR) sensors a large number of land applications has emerged, several based on important developments which have been made in the field of SAR Interferometry . SAR data are being used for agricultural monitoring, forest mapping, geological exploration and flood mapping, while SAR Interferometry measurements of topography and small topographic changes are making major contributions to environmental risk assessment from earthquakes and land subsidence. The Advanced Synthetic Aperture Radar (ASAR) sensor will be used for numerous land-based applications which include:

  • Global Vegetation Monitoring
  • Forestry
  • Geology and Topography
  • Agriculture
  • Natural Hazards
  • Flooding, Hydrology and Water Management
  • Urban Studies Global Vegetation Monitoring

Land cover is defined as the observed physical cover, including vegetation and human constructions, of the Earth's surface.

For many ecological studies, there is a need for current information on the distribution and amount of vegetation. This need has not been fully addressed by a quarter century of spaceborne remote sensing systems operating in the visible and near-infrared region of the electromagnetic spectrum . Collection of visible/near-infrared imagery over ecologically important regions on a continuous basis is often limited by cloud cover, particularly in tropical and boreal biomes. However, radar data can be acquired at any time since imagery acquisition is not hindered by atmospheric conditions or darkness ( See "Scientific Background" 1.1.2. ).

Radar data can be used in applications supporting land cover delineation, base mapping and updating, and environmental monitoring. In these applications, radar data is used as a tool for distinguishing differences in surface roughness, moisture content, and geometric shape associated with different land uses and covers, which in turn allows the delineation and identification of land cover types and related land use and cultural features. By using the backscatter models of crop types, area, height and condition changes, through the application of multitemporal imagery, changes in land use and land cover over time can be assessed.

The image shown in figure1.72 below provides an example of 25 km resolution global backscatter data from the ERS Scatterometer, which contains information on vegetation type, standing biomass and active vegetation. In comparison, the Advanced Synthetic Aperture Radar (ASAR) Global Monitoring Mode (GM) will provide the much improved spatial resolution of 1 km, similar to that of AVHRR.

Figure 1.72 Global backscatter data collected by the ERS Scatterometer in August 1993, showing major vegetation types and morphological units (i.e. tropical rain forest, savannah, deserts, mountain ranges, tundra). Acknowledgement: E. Mougin, P. Frison, CESR, Toulouse; Y. Kerr, LERTS/CESBIO, Toulouse, France.

One of the the main aims of global vegetation mapping is to characterise the function of biomes within the climate change models, and to quantify the extent to which changes in global vegetation distribution may affect the climate. Climate data are the main inputs for these models. When radar data are acquired for the quantitative study of land use and land cover, it is important that data are calibrated, since this allows image brightness values to be more directly related to target backscatter. When radar data are acquired over a number of time periods, for the purposes of monitoring change for land use and land cover applications, it is again important that data are calibrated. Image calibration for change detection ensures that any change in the image are a result of a change in the target and not from a change in the sensor. ASAR products will be used, along with other remote sensing data, for initialising, parameterising, and calibrating models, on a global and regional scale, and for the monitoring of changes in vegetation type and status. ASAR data and products developed from the Global Monitoring (GM) and Wide Swath Modes (WS) are of particular interest. Forestry

Accurate and consistent mapping is essential for the successful management of forests. Global forests provide invaluable benefits and resources, both of an ecological and economic nature, to the world's population. Not only to the forests provide a supply of fuel and building material, but they also retain soil, regulate run-off, minimise the siltation of water, and provide fruits, nuts, tree extracts and medicinal plants. As the large proportion of the Earth's surface is occupied by forests, which are continually changing, a management tool is required that is capable both of covering large areas of the Earth's surface, as well as revisiting those areas with some frequency.

The ability of spaceborne radar sensors to image the globe in a short period of time, unimpaired by such atmospheric effects as light rain and cloud, is well known.( See section entitled "Scientific Background" 1.1.2. ). As with other applications, this ability is well-suited to forestry applications, as a large proportion of the world's forests are found in tropical areas where there is cloud cover for much of the year or at high latitudes where there are long periods of darkness during the winter months. By using multi-temporal analysis and interferometric techniques, ERS data are beginning to be used in operational mapping programmes.

The image shown in figure1.73 below gives an indication of the deforestation that is occurring in the Brazil.

Figure 1.73 Brazil Deforestation. This artificially coloured ERS-1 SAR image, covering an area 75 km x 75 km, shows the Teles Pires river in Brazil (Mato Grosso State). A regular pattern of deforestation is clearly seen in the rectangular patches of destroyed forest extending over areas as large as 20 kilometres ( copyright ESA 1992 )

Radar is particularly effective for the delineation of burned areas because of its sensitivity to differences in structure and moisture content. ( See "Scientific Background" 1.1.2. ). Several months after a forest fire, burnt forest areas dry out and leaves and small twigs drop from the trees which results in a contrast in structure and moisture content between burnt and unaffected areas. On radar imagery, this produces a contrast in backscatter, where the burnt areas appear darker than the unaffected forested areas.

The ability of C-band radar to discriminate between some forest types has been shown in a range of forest environments. In areas of mixed deciduous and coniferous forests, separation of general forest type is accomplished through the use of multitemporal data; the leaf-on and leaf-off conditions of deciduous species results in a seasonal change in backscatter which contrasts with that of the coniferous species which do not experience leaf-off. It is important to note that in order to acquire useful information on general forest type, using radar satellites, stands will have to be large and of uniform composition. Species differentiation is more difficult in general at microwave frequencies then with optical data because of overlap in signatures between species.

The higher incidence angles and dual polarisation data from ASAR will improve the potential for forestry applications. Use of low incidence angles enhances the sensitivity to biomass, whereas the use of high incidence angles improves mapping of deforestation. Dual polarisation will improve discrimination of forest types.

The graphs in figure1.74 below show how backscatter varies with VV, HH and HV polarisations across a range of incidence angles, for Sitka Spruce and Upland Pasture, and how these measurements have been used in a decomposition model to determine the relative contribution of surface, double-bounce and volume scattering mechanisms.( See "Scattering Mechanisms"1.7 in the section entitled "Scientific Background" 1.1.2. ).

In the two upper graphs shown below polarisation and incidence angle effects on backscattering are shown, and in the bottom two graphs relative contributions of surface, double-bounce and volume scattering are shown.

Figure 1.74 Backscatter of Sitka Spruce and Upland Pasture at Llyn Brianne, Wales. (Acknowledgement: Luckman and Baker, 1995).

image image

The multipolarisation, multi-swath modes of ASAR ( see section entitled "Principles of Measurement" 1.1.3. ) offer a greatly enhanced capability for forest assessment over any previous spaceborne SAR mission. New research being proposed brings together expertise in SAR data, software, ecology, and forestry, as well as a long history of ground data. The application of ASAR to the quantitative assessment of forest characteristics such as yield, species, and phenology will be demonstrated for tropical, boreal and temperate forests. Geology and Topography

Traditionally geological information for mapping at local and regional scales has been acquired through field work by qualified geoscientists. Optical remote sensing from air- and spaceborne platforms provide a synopic view of the terrain which allows geological information to be collected over a larger region. Geological information from optical sensors are however, hindered by cloud cover, identification of features restricted by illumination conditions, and the delineation of geological structure dependent on the angle and elevation of the sun. Radar remote sensing has proven to be an effective tool for the extraction of geological information, unhindered by external illumination and weather conditions. ( See "Scientific Background" 1.1.2. ).

The side-looking configuration of spaceborne Synthetic Aperture Radar (SARs) highlights relief, which assists in topographic mapping for terrain analysis. When relief information provided by radar is combined with optical data, an image is created that provides valuable terrain information. The use of shallow incidence angles produces a shading effect or shadowing which can emphasise even subtle slopes in the landscape. These are often attributable to underlying geological units and structures.

The image in figure1.75 below, shows excellent imaging of faults and contrasts, using enhanced ERS-1 (SAR) imagery.

Figure 1.75 ERS-1 SAR enhanced and enlarged image of Penzhinskaya Guba, North Kamchatka, Russia. ( Image created by Dr. William Harbert of the Geophysics Department, University of Pittsburgh )

Geological mapping with SAR data has become well established and a number of organisations offer a commercial service for mapping structure, but the effect of layover in hilly terrain prevents widespread use of the data. ASAR Image Mode (IM) products, with high incidence angles will be of particular interest to reduce terrain distortions. The Alternating Polarisation Mode (AP) products may be of value for texture analysis in arid areas. Wide Swath Mode (WS) products will be useful for looking at regional and continental geological structures.

Other remotely sensed data can be integrated with SAR data to provide additional information for an imaged area, thus creating an enhanced image map for interpretation. Another valuable combination is with the varied data sets generated by modern geophysical surveying, which is widespread in mineral exploration. Combined with radar imagery, it provides a means of correlating, or at least locating, inferred subsurface mineral horizons, structural features, or lithologies with respect to surface relief.

In the creation of the image shown in figure1.76 below, topographic phase recovery from stacked ERS SAR interferometry and a low resolution Digital Elevation Model (DEM) were used.

Figure 1.76 Interferogram created by using 25 ERS SAR images from an area of Southern California containing 2700 m of relief; compared with the topography measured by the Shuttle Radar Topography Mission. Fringes from two major earthquakes and a seismic slip on the San Andreas Fault are clearly isolated. ( Image created by David T. Sandwell and Lydie Sichoix at the Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, La Jolla, CA

Radar has also proven to be a promising tool for mineral and hydrocarbon exploration. Certain types of mineralization and hydrocarbon resources are often associated with specific geological structures thus, the mapping of these structures when topographically expressed can assist in the identification of areas of high mineral and hydrocarbon potential. Often radar data are merged with geophysical data. The radar data allows the delineation of topographically expressed structures, while the geophysical data aids in the identification of strong magnetic signature. Resulting composites show the correlation between geological structure and magnetic anomalies which aid in the planning of detailed ground surveys. Agriculture

The use of Earth-observation satellite data can provide agriculture and its related industries, such as food and insurance, with cost-effective methods for wide-scale and localised monitoring of crop output and condition factors. Optical and radar satellite imagery can be used as a tool for recording important information that is needed before and during the growing season. The information derived from this imagery can be of particular importance to individuals and organisations in the agriculture industry including farmers, agricultural co-operatives, agribusiness and food companies.

Earth-observation data can provide:

  • Soil moisture detection and monitoring
  • Crop studies and acreage determination
  • Hail, flood, and hurricane damage mapping

The ERS programme has demonstrated the ability of satellite radars, independently of weather conditions, to identify crops, detect soil moisture and monitor seasonal land cover changes. Multi-temporal techniques are used, which involve the collection and analysis of SAR data on a series of different dates over the period of interest. Interferometric coherence has also been used to improve land cover discrimination.

Soil Moisture and Detection:

Soil moisture variability is an important factor in many agricultural business processes. It is a valuable input into crop yield prediction models and helps in the determination of plant stress zones for the management of agricultural inputs. Radar imagery can be used to create moisture maps and for irrigation management, as well as to monitor the effectiveness of central pivot irrigation systems. A further use in this area involves developing moisture level base maps for monitoring regions of high flood probability.

The ASAR Wide Swath (WS) and Global Monitoring (GM) Modes will be of most interest to soil moisture and large area vegetation mapping.
Crop studies and acreage determination

Satellite imagery is a valuable tool in determining varying levels of crop vigour within fields or agricultural management zones. Plant stress can be monitored and growth inputs applied in a more timely and efficient manner when the different areas of stress are available in a single image to the farm manager. Satellite imagery can therefore increase the efficiency of crop scouting practices by more precisely targeting areas that need to be examined or tested. Imagery can be used to produce vigour maps that can be linked to geospatial information and allow the farmer to determine the relative health of all planted areas at one time.

An increasing aspect of precision farming is the management of agricultural zones within fields. Soil type, crop vigour, and irrigation levels, as revealed by Earth-observation data, can be delivered to the customer and input into field management GIS to aid in the efficient application of fertiliser and other agricultural input chemicals.

ERS data are now being used operationally within major European programmes concerned with agricultural statistics (MARS STAT) and the control of agricultural subsidies (MARS PAC). Within MARS STAT the use of ERS data has improved the estimates of crop area early in the crop growing season. ERS data are used as a substitute for optical data in the MARS PAC control activity when cloudy conditions are encountered at key times during the crop growing season.

Figure1.77 below shows the use of multi-temporal ERS data for mapping the areas using different rice cropping systems in the Mekong Delta of Vietnam.

Figure 1.77 Map of different rice cropping systems in the Mekong Delta, derived from classification of seven ERS SAR images acquired over the period May to December 1996. (Acknowledgement: Balababa et al. 1997).


ASAR Image Mode (IM) offers continuity for agricultural applications, enhanced by the ability of variable incidence angles. The Alternating Polarisation Mode (AP) will greatly improve crop classification. Natural Hazards

A number of areas of study relating to natural hazards will utilise ASAR data. These include:

Volcanic Activity:

The Afar triangle, located in the Republic of Djibouti of East Africa and shown in the image below, is the only place on Earth where the mid-ocean ridge is visible as a rift in an arid desert. It is thus a uniquely privileged site for long-term study by radar interferometry.

Figure 1.78 ERS-2 Image of the Afar Triangle in East Africa

The area shown above has experienced damage in this century by volcanic eruptions and large earthquakes. To evaluate the risk of such events in the future, it is essential to understand the geophysical signatures recorded in the topographic relief and the deformation field. The ASAR instrument can measure both of these signals with SAR interferometry . The imaging geometry, surface conditions, and climate are optimal for SAR interferometry.


To study seismically active fault systems, it is important to measure both the long-term rate of deformation averaged over several seismic cycles and the short-term deformation associated with the seismic activity along individual faults. The first type of measurement requires accurate topographic maps to quantify the cumulative displacement of Quaternary surfaces and geomorphic structures, such as alluvial fans or glacial moraines. The second type of measurements requires the capacity of estimating subtle displacements of the ground at the millimetre level of precision over short time intervals. With the advent of spaceborne radar systems, such as ERS-1, ERS-2, and now ASAR, the technique of SAR interferometry is becoming a new tool for active tectonics by providing both mm-precision surface change maps spanning periods of days to years and m-precision, high resolution topographic maps for measuring crustal strain accumulated over longer periods of time.

A strong earthquake shook northwestern Turkey, levelling buildings, and cutting power and phone lines. The quake had a magnitude of 7.8, making it nearly as powerful as the 7.9-magnitude San Francisco quake, which killed 700 people in 1906. The digital elevation model (DEM) of this area, shown below, was produced by interferometric analysis of synthetic aperture radar (SAR) data from the ESA ERS-1 and ERS-2 satellites . Pairs of SAR images from tandem acquisitions, with temporal separation of one day, were processed separately to produce a height map in ground range coordinates. Then the height maps were projected into UTM coordinates. Final elevations in the DEM are the average of the overlapping SAR interferometric height maps.

Figure 1.79 Izmit Earthquake digital Elevatio Model from ERS SAR Interferometry ERS data ESA (1995,1996,1999),DEM Eric Fielding, Oxford (1999) Shaded relief image of interferometric DEM from three descending and one ascending ERS SAR Tandem pairs, with GTOPO30 filling in gaps.

In the ERS-1 SAR interferometric map below, created at the Jet Propulsion Laboratories (JPL), the post seismic surface movements following the Landers 1992 earthquake are clearly evident Ref. [1.29 ] .

Figure 1.80 ERS-1 Interferometric map, Landers 1992 earthquake. Images used were from Sep. 27, 1992 to Jan 23, 1996. Map created by JPL.

The Interferometric SAR data revealed several centimetres of post-seismic rebound in step-overs of the 1992 break with a characteristic decay time of 0.7 years. Such a rebound can be explained by shallow crustal fluid flow associated with the dissipation of pore pressure gradients caused by co-seismic stress changes.

In figure1.81 below, SAR images spanning three different time intervals in the three years following the earthquake were combined. In the left panel the interferogram covers 41 days after the event, starting on 7 August 1992. The most striking features are the localised strain along three sections of the 1992 surface rupture where the rupture changed direction or jumped to another fault branch and formed two pull-apart structures and a compressive jog (boxes in left panel). The observed displacement in the fault step-overs is consistent with surface uplift in the pull-apart structures and subsidence in the compressive jog, i.e., opposed to the direction of the co-seismic movements, which is shown in the panel on the right.

Figure 1.81 ERS-1 3 pass interferogram of Landers earthquake (JPL 1996)

Land Subsidence:

Subsidence is the sinking of the Earth's surface in response to geological or man-induced causes. This surface displacement is shown by the image below, created from ERS-1 SAR data by the Jet Propulsion Laboratory (JPL). In this image, which shows the location of existing and future Global Positioning System (GPS) stations of the Southern California Integrated GPS Network (SCIGN), the colours of the radar image represent the change in range due to surface displacement toward the satellite antenna, which is illuminating the area from the east with an incidence angle of 23 the vertical. The black lines are mapped as active faults. One full colour cycle represents 5.6 cm of range change between the dates of acquisition of the radar data (20 October, 1993 - 22 December, 1995). Grey areas within the radar swath are zones where the radar correlation is lost due to steep slopes and seasonal change of the vegetation.

Figure 1.82 ERS-1 SAR 3-pass interferogram showing subsidence in Panoma California. Image created by JPL

Although surface displacement in this region is primarily due to the tectonic activity, as shown in the above image by the concentric rings visible along the western edge of the SAR swath, other features visible on the image are related to human activity such as water and oil withdrawal. Regions of ground subsidence include the Pomona (P) area (water), the Beverly Hills (BH) oil field (oil) and localised spots in the San Pedro and Long Beach airport (LBA) area (probably oil industry activity). Noticeable surface uplift is observed in the Santa Fe Springs oil field (SFS) and east of Santa Ana (SA). Surface uplift in these areas may result from the recharge of aquifers or oil fields with water, or from the poro-elastic response of the ground subsequent to water or oil withdrawal.

The relationship between the sinking of the earth's surface and how this is displayed by the SAR interferogram is depicted in figure1.83 below.

Figure 1.83 Relationship between ERS-1 SAR Interferogram and Panoma Subsidence. Image generated by JPL

In the interferogram, the colours depict the displacement of the ground along the radar line of sight (23 degree off vertical) having occurred between October 20, 1993 and December 22, 1995. The shaded view in the centre is the radar phase field displayed with 20,000 vertical exaggeration. The top view gives a street map wrapped on the phase field. Flooding, Hydrology and Water Management

Floods are among the most frequent of all natural hazards. They are extreme events that are usually sudden and short-lived, and
can cause considerable economic loss due to damage to buildings, destruction of infrastructure as well as the loss of human
lives. One of the biggest problems during such an emergency is to obtain an overall view of the phenomenon, with a clear idea
of the extent of the flooded area, and to predict the likely developments.

Figure 1.84 Extensive flooding near Oxford, UK. (Courtesy Institute of Hydrology, UK)

ERS SAR products, such as those created by ERS-1 and ERS-2, as well as ASAR, can be used in the event of flooding to permit immediate assessments of the areas at risk and aid decision-making on relief and cleanup operations. Products derived from archived SAR data may provide accurate spatial information on the extent of previous flood events. This is being used for management planning for preventative measures in areas where flooding occurs regularly. ERS SAR data can serve as up-to-date information in the absence of conventional optical satellite or other data. This is often the case in bad weather conditions which accompany flooding events.

The low resolution ERS SAR images shown below, which were obtained from the Rapid Information Dissemination System (RAIDS) of the UK, gives an indication of how SAR imagery can be used to monitor a floods progression.

image image image
Figure 1.85 Reduces Resolution images ( 100 km x 100 km. pixel size 400m ) of the River Maas in the Netherlands shown before (left) and during (right) flooding (Imagery 1994-95 ESA)

The image below gives an example of the hybrid change detection procedure used to detect human induced land cover changes in Southwest Florida Ref. [1.35 ] . The work carried out at the Environmental Research Institute of Michigan, Duke University, was to advance techniques for monitoring and predicting changes in the hydrologic condition of regional scale wetland ecosystems in the south Florida region. The objectives were to integrate satellite remote sensing data with hydrologic models and field data to improve capabilities for monitoring and understanding processes controlling surface water flow in Florida wetlands. It was found that the herbaceous wetlands in their study area exhibited a wide range of conditions, from dry to saturated soils to flooded, which are readily detected by the C-Band ERS-2 SAR sensor.

Figure 1.86 Vegetation and hydropattern dynamics of Big Cypress National Preserve PCA - Unsupervised classification of 1997 - 1998 ERS-2 SAR imagery. ( Image courtesy of Environmental Research Institute of Michigan, Duke University)

ASAR will add a new wealth of data for such research around the world. Urban Studies

The interrelated issues of urban sprawl, traffic congestion, noise, and air pollution are major socio-economic problems faced by most major cities. Though it is desirable to know about the storage of heat in a particular city and how that amount of heat changes from day to night and from one day to the next, the information is difficult to obtain because of the complex three-dimensional structure of the urban surface and the variety of materials involved. Structures, pavements, vegetation and the ground itself must all be taken into account. Large bodies of water nearby are a further complication.

Figure 1.87 Mean image computed from five ERS SAR images, of the city of Nantes. The island is the island of Beaulieu. The Loire river appears in grey, crossed by several bridges in bright. In dark are the major roads, and the airport in the lower left corner. (copyright ESA 1994)

In the above image the importance of the relative direction of the target with respect to the radial direction of the radar wave clearly impacts the results. If the relative direction is perpendicular to the radial direction, then this object is clearly visible, even if it is flat such as a road or a railway track within a flat area. If the object is orientated in the same direction than the radar wave, then it is not visible. This is the case for the central station of Nantes and of the large railway complex in the western part of the Beaulieu island. However if the same object is surrounded by e. g. buildings reflecting the radar signal, it will be perceived because of the created contrast.

The direct analysis of the ASAR measurements, with many different polarisations and viewing angles, allows the recognition of different urban environments. The interferometric analysis of the same data, acquired in more than one passage, can be used for the 3D characterisation of a zone. References

Ref 1.29
Peltzer, Gilles, "Crustal Deformation Studies Using SAR Interferometry", Jet Propulsion Labratories at the California Institute of Technology and the Earth and Space Science Division of the University of California Los Angeles.

Ref 1.30
ASAR Science Advisory Group, Editor R.A.Harris, European Space Agency 1998, "ASAR Science and Applications", ESA SP-1225

Ref 1.31
Sandwell, David T., and Sichoix, L., "Topographic Phase Recovery From Stacked ERS Interferometry and a Low Resolution Digital Elevation Model", Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, La Jolla, CA. Submitted to Journal of Geophysical Research, March 1, 2000

Ref 1.32
Harbert, Dr. W., "Geological Interpretation of North Kamchatka, Russia: Constrains from Synthetic Aperture Radar, Digital Elevation Models and Digital Residual Magnetics", Geophysics Department, University of Pittsburgh. Abstract published in Earth Observation Magazine, September 1992, pp. 45-47.

Ref 1.33
Paillou, P.,"Arid Sub-Surface Imaging using Radar Techniques", Observatoire Astronomique de Bordeaux, BP 89, 33270 Floirac, France, web:

Ref 1.34
Wright, T., Fielding, E., Parsons, B., "The 17 August 1999, Izmit Earthquake Displacements and Topography from SAR Interferometry" ,

Ref 1.35
Kasischke,E.S., et. al, "Monitoring Regional-Scale Hydrologic Processes in the South Florida Ecosystem", Environmental Research Institute of Michigan, Duke University, US., January 31, 2000. Sea Ice Applications

There is a wide range of sea ice data applications in the marine community and across a number of industrial sectors. Industries and agencies operating in ice-infested waters include commercial shippers, offshore oil exploration companies, fisheries, military, regulatory agencies, and research institutions. Other industrial sectors use ice information when designing ship hulls and offshore drilling platforms, to perform risk assessment, and to establish insurance premiums.

The sea ice markets include agencies in the USA, Canada, Japan, Russia, Norway, and the Baltic countries. SAR data are combined with other sources of data to optimise sea ice forecasts, and are also transmitted directly to ice breakers and ships operating near the ice edge.

Ice information is required at continental or global scales. As described by Manore et al. (1991), the requirement for these scales comes largely from the global ice studies community, which has interests in:

  • monitoring global sea ice extent as an indicator of climatic change
  • providing better parameterizations of sea ice in ocean-ice-atmosphere global circulation models
  • better understanding of the processes of heat and mass exchange between the ocean, ice, and the atmosphere

Radar data can aid in areas of study such as ice concentration determination, ice type classification, ice feature identification, ice motion monitoring, and iceberg tracking. Information on total ice concentration, location of the ice edge, ice type and thickness, ice topography, the presence and location of leads, ice pressure, state of ice decay, and iceberg and ice island location can be derived directly from radar imagery.

Note that HH polarisation differentiates between open water and ice better than VV polarisation. Also note that the appearance of a given ice feature may differ significantly in wet versus dry conditions. When surface meltwater is on the ice or ocean spray is on the surface at the ice edge, feature brightness may differ from that encounter in dry conditions. When wet surface conditions prevail, it is useful to have a reference image acquired before the onset of melt conditions.

RADARSAT SAR Image With Sea Ice Deformation
Figure 1.88 RADARSAT SAR Image With Sea Ice Deformation (SAR image is Copyright CSA 1998. Grid produced by the Radarsat Geophysical Processor System)

The above is a RADARSAT ScanSAR Wide B geocoded image of sea ice in the Beaufort Sea on January 11, 1998, that has been block-averaged (10 x 10) to 1000 metre pixel size.

RGPS Lagrangian Ice Motion Visualization
Figure 1.89 RGPS Lagrangian Ice Motion Visualization( Using data produced by the Radarsat Geophysical Processor System )

The above image is an early attempt to visualize the RGPS Ice Motion product. RGPS is the Radarsat Geophysical Processor System under development by the Polar Remote Sensing Group at JPL. The visualization is an ASF Science Division project directed by Dr. Mitchell Roth, UAF Professor of Computer Science, with Richard Guse, CS graduate student. The data is derived from arctic snapshot data of an area in the Beaufort Sea acquired by ASF in November of 1996. This datatake is cycle 15 based on 143 ScanSAR B images. Ice motion is visualized using a colourwheel (shown below.) The direction is given by colour (1-1 correspondence with the colourwheel) & the magnitude of its movement is given as the saturation (white being slow and full colour being fast, not necessarily 1-1).

Colour Wheel
Figure 1.90 Colour Wheel

This visualization is a 'composite' of vectors in 3-space, the z-coordinate (out of the screen) represents the time the vector is computed with more recent vectors on top.

Sea Ice Motion Derived from Satellite Imagery

Sea ice motion data are utilized both in fundamental scientific research and for practical every-day purposes. Ice motion data help scientists in many research endeavors such as: analyzing the polar regions' latent heat advection; determining the local oceanic surface stresses; passively tracing currents; tracking new areas of open water (formed by ice divergence and shear); and understanding the effects of icebreaker vessels. In addition to furthering this fundamental research, the data are also used to help forecast weather and ice conditions, evaluate possible hazards, and contribute to the overall understanding of environmental impacts in the Arctic region.

Traditionally there have been many obstacles in acquiring sea ice motion data. Sea ice is by nature located in remote, inaccessible regions, and the ice extends over a large area. Though ice motion can be determined by placing sensors directly onto ice floes, the cost of installing and maintaining many field instruments is prohibitively high. It is similarly prohibitive for scientists to stay in the field and monitor the sea ice motion themselves! One can readily see the benefits of studying sea ice remotely, from satellites.

There are also complications with monitoring sea ice from space, however. Cloudy skies hide the sea ice from some sensors, and long winter nights thwart the ability to image polar regions with visible light. The research community therefore turned to the Synthetic Aperture Radar (SAR) as a viable sea ice imager. Since a SAR emits its own signals (similar to a camera's flash), it works during the day or night. The radar signals also penetrate through cloud cover, a characteristic which is especially valuable to oceanographers. Scientists determined that a satellite-borne SAR could routinely and reliably image large regions of sea ice.

The SAR-carrying ERS-1 satellite, launched in July 1991, provided many scientists the chance to study sea ice with SAR. The Geophysical Processor System (GPS), an effort led by Ron Kwok of JPL, was designed to facilitate this research. The GPS generated sea ice motion, sea ice classifications, and ocean wave spectra data from ASF's collection of ERS-1 SAR imagery. Scientists could then use the derived geophysical information for their various research projects.

Melville Island Canada
Figure 1.91 Melville Island Canada Copyright 1992, European Space Agency

This image above was derived from SAR data obtained by the European Space Agency's ERS-1 satellite on October 11, 1992. This scene shows a portion of Melville Island, way up in northern Canada (108 W, 75 N), and should be of particular interest to you geologists. The image covers about 65 km by 55 km. North points approximately 33 degrees counter-clockwise from the top of this scene, and the spacecraft was descending "down" the right side of the image, so the scene's radar illumination is from the right. Sea Ice Applications - Ice Sheet Dynamics

Radar is sensitive to the physical properties of sea ice (salinity, microstructure, and surface roughness) that vary with ice concentration, type, age and thickness. Elements that are employed to visually discriminate ice types in SAR imagery include the structural characteristics of floes, such as the shape and presence of ridges, fractures, and melt ponds. The ability to discriminate ice types is highly dependent on the geographic region and the season of imaging because different ice types exist in different regions and at different times of the year. The most promising season is winter when the snowpack is dry and cold. During the drier months of winter the surface texture is the primary key for discrimination. In the warmer months, when free water is present, the differences between old and first-year ice become more difficult to discriminate, in particular when the overlying snow pack is saturated and the differences in backscatter are significantly reduced. Classification ambiguity can be reduced by consulting the past history of ice features in a particular region, ice climatology, and current meteorological conditions (Ramsay et al, 1993).

The identification of specific structural features within the sea ice is also important for navigation because they pose hazards to vessels. The identification of leads and floes is used to optimise route selection, and RADARSAT's sensitivity to surface roughness and its fine spatial resolution permit the detection of ice ridges and their orientation.

The image shown in figure1.92 , available through the Alaska SAR Facility Ref. [1.37 ] , shows an example of grease ice.

Grease Ice (Copyright 1991, European Space Agency)
Figure 1.92 Grease Ice(Copyright 1991, European Space Agency)

The SAR data for this image was taken by the European Space Agency's ERS-1 satellite on October 2, 1991. The centre location of the complete image is 73.58 N, 166.60 E, north of Siberia in the East Siberian Sea. North points approximately 30 degrees counter clockwise from the top of this scene.

In addition to the detection of ice structures, knowledge of the state of decay or growth of sea ice is important to infer its strength. During periods of decay when temperatures are above freezing, the ice is relatively weak and can be more easily traversed by an ice-strengthened vessel. However, during an extended period of ice growth when temperatures fall below freezing, the same ice could be impassable. Ongoing monitoring of sea ice to identify the onset of melt and freeze-up may aid in route selection (Ramsay et al, 1993).

Another factor that can impede the progress of ice-strengthened vessels is the continual movement of the sea ice in response to wind and ocean currents, and the pressure that is created within the ice floes. Because these movements are continual, monitoring must be an ongoing activity, requiring frequent and sequenced imaging.

Icebergs of all sizes, including those less than 5 m in size (referred to as bergy bits), are of interest to shipping and offshore operators because of the hazard they pose to ships and structures such as oil platforms. Steep incidence angles and the low-resolution of reconnaissance-scale imagery, pose problems in detecting icebergs in pack ice and in open water. In open water, the sea conditions and orientation of larger icebergs with respect to the SAR will determine whether they can be detected. In pack ice, the signature of an iceberg is often lost in the backscatter from the surrounding sea ice.

Areas of glaciology to which ASAR contributes include: monitoring the ice extent and the boundaries of ice sheets, and mapping the motion of ice sheets and glaciers. figure1.93 provides a simple example of the use of ERS data for mapping changes in the extent of the Larsen Ice Shelf, Antarctica. There are many similar examples. SAR data is still being used on an irregular basis when something interesting happens, rather than as a monitoring tool. Interferometry and correlation measurements which show movement of the ice sheets are very important to this work. ASAR will provide valuable continuity in the supply of data started with ERS.

Mass balance and ice dynamics are the key scientific questions, with the parameters to be derived from ASAR including:

  1. Ice boundaries (every 2 weeks)
  2. Ice export due to calving (every 2 weeks)
  3. Extent of melt zones (2 weeks during summer; Greenland and Antarctic Peninsula)
  4. Snow and ice faces (once a year)
  5. Ice motion by means of feature tracking (once a year
  6. Surface morphology (flow lines, rifts, crevasse zones etc.) (once a year).

Depending on the topography and the requirements for spatial detail, the ice sheets may be separated into the boundary zone (including the ice shelves) and the interior part. Baseline operation for the boundary zones is considered to be the Wide Swath Mode for the measurement of parameters (1), (2) and (3) above. Baseline operation mode for the interior could be the Global Monitoring Mode, for the measurement of parameters (3) and (4). The preferred operation modes for selected zones (ice streams) are Image Modes at higher incidence angles (IS3 to IS7), for measurement of (5) and (6) above with approximately annual repetition of observations.

Radar interferogram of a portion of the Rutford ice stream in Antarctica, based on two ERS-1 images taken six days apart.
Figure 1.93 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.(From Goldstein et al, 1993)

There are a number of key studies in this field being proposed that will utilise ASAR data to a great extent. Some of these are briefly discussed below.

One study, to be conducted by Dr. Massimo Frezzotti at the ENEA Environmental Department in Italy, will focus on using ENVISAT ASAR data to study the XXI century iceberg calving of East Antarctic. The objective is to study the iceberg calving process, to perform monitoring of ice front change, and to evaluate a century behaviour of iceberg calving. Comparisons are to be made using new ASAR images and previous data (aerial photographs, satellite images), taken several years apart (1947 - 65; 1972 - 73; 1988 - 93; 1996) to estimate the XXI century of ice front fluctuation and of iceberg discharge. The study of the dynamics of seaward extension of floating glaciers will allow the investigators to hypothesise the ice ocean interaction.

Another area of research, to be carried out by Dr. Eric Rignot at the Jet Propulsion Laboratory in the United States, will study two related ice sheet dynamics and evolution problems that are addressable with ENVISAT data:

  1. The stability of the onset areas of fast-moving glaciers in Greenland and Antarctica using SAR interferometry.
  2. The time evolution of surface rifting, crevasse development and meltwater production on Antarctica's floating ice shelves using dual polarisation SAR data.

The two research topics are related because fast moving glaciers are strongly influenced by the presence or absence of buttressing ice shelves (Antarctica) or floating ice tongues (north Greenland).

The first topic will attempt at detecting mechanical instabilities (surge) of large ice streams draining polar ice sheets in response to climate change (e.g., enhanced melting at the coast). The second topic will provide new insights into the physical mechanisms (rift propagation, meltwater accumulation) controlling tabular iceberg production in the Antarctic.

At the British Antarctic Survey, Dr. Christopher Doake and associates propose to use ENVISAT data to investigate two important components of the Antarctic Ice Sheet:

  1. the ice streams draining the West Antarctic Ice Sheet (WAIS)
  2. the climatically sensitive ice shelves

The ice streams that drain the WAIS are the key controls on its size and configuration. Dr. Doake will use ASAR brightness data and interferometry to investigate the current dynamics of these ice streams and use velocity fields to calculate ice flux, searching for changes that could indicate a state of imbalance. The WAIS ice streams will then be contrasted with an ice station in East Antarctica, that drains a similar area of ice sheet but has a different dynamical character. Dr. Doake's team has recently shown that the ice shelves along the Antarctic Peninsula are sensitive indicators and integrators of a regional climate change. The ENVISAT data will be used to monitor the predicted changes and to determine the flow regime of the ice shelves.

Yet another study being proposed comes from director Kenneth Jezek at the Byrd Polar Research centre in the United States. This project will concentrate on compiling an ENVISAT SAR mosaic of Antarctica. The acquisitions will be coordinated with planned acquisitions by the Canadian RADARSAT. The intent is to answer important questions about seasonal processes such as calving, melting, coastal polynya formation and the consequences on glacier flow. The possibility of complete, simultaneous interferometric coverage of Antarctica offers the greatest scientific payoff and could revolutionise our understanding of how Antarctica responds to changing global climate.

And Dr. Johnathan Bamber at the Centre for Remote Sensing, University of Bristol in the UK, will use Interferometric ASAR data to provide information on the dynamics and temporal stability of the flow of the ice sheets and Arctic ice masses.

These are but a few of the areas of ice dynamics research in which ASAR data will be of major benefit. Incidence angle is not a critical factor in sea ice monitoring. However, shallow incidence angles are more effective in highlighting surface topography, separating the ice/water boundary, and detecting icebergs.


Ref 1.36
Report of a Workshop Held in Boulder, Colorado: February 3 - 4, 1994, Timothy H. Dixon, Editor , University of Miami, Rosenstiel School of Marine and Atmospheric Science. available at

Ref 1.37
Alaska SAR Facility, web site Sea Ice Applications - Snow Cover

In many areas of the world, the majority of freshwater available for consumption and irrigation results from snowpack runoff. Snow wetness, snow-water equivalent and the aerial extent of the snow cover are the most important parameters in predicting total runoff. Mapping the extent of wet snow is possible using SAR data (Rott et al, 1988) as wet snow produces a low radar return in contrast to dry snow which is essentially transparent at C-band.

The ice sheets of Antarctica and Greenland are the principal stores of fresh water in the Earth's hydrological system and changes in their mass balance affect the mean sea level. Changes in the height of ice sheets can indicate changes in the mass balance. Snow cover, snow accumulation, and ice type information are also required for monitoring, detection of change and process studies at high latitudes and high altitudes.

The presence of snow on the ground has a significant influence on the radiative balance of the Earth's surface and on the heat exchange between the surface and atmosphere. ERS has demonstrated the value of SAR data for mapping snow cover, and the Global Monitoring Mode is of special interest for monitoring the areal extent of snow and the temporal dynamics during the melt period, on a weekly basis for climate research purposes.

Snow mapping is also important for hydrology and water management. Snow cover extent data are required every 2 weeks during the melting season, and the baseline operation in mountainous areas should be Image Mode at high incidence angles (IS4 to IS7).

Scientists Craig Lingle, Carl Benson, and Kristina Ahlnaes, of the Geophysical Institute, use Synthetic Aperture Radar (SAR) imagery to monitor glaciers. At the fall 1996 meeting of the American Geophysical Union (AGU), these researchers presented a poster which detailed how glacier facies (zones) can be examined through satellite SAR imagery. They focused their attention on the Nabesna Glacier, which flows down the slopes of Mt. Wrangell in south-central Alaska.

Glacier Facies on Mt. Wrangell, Alaska Examined With SAR Imagery
Figure 1.94 Glacier facies on Mt. Wrangell, Alaska, examined with SAR imagery. image size was greatly reduced for display here) ( Copyright ERS)

The image above is a multi-temporal image of Mt. Wrangell and Nabesna Glacier derived from midsummer, late-summer, and winter ERS-1 SAR data. Mt. Wrangell is located at left-centre, with Nabesna Glacier flowing to the east (right) before turning north (up). The glacier's wet snow facies (zone) is outlined by the blue region. The pink areas mark the upper extent of summer snow melt.

The SAR imagery (shown in figure1.94 ) is particularly effective at distinguishing between a glacier's characteristic zones. Near its terminus the glacier is covered by rocks which scatter the transmitted radar signals back to the SAR sensor. Consequently, the glacier's terminus appears rather bright in the SAR imagery. Above the terminus lies the ablation area. The relatively smooth ice in this region is specular; it "forward scatters" a significant portion of the radar signals away from the spacecraft. The ablation area is therefore assigned a medium to dark grey image intensity. The light streaks seen in the ablation area correspond to medial moraines: lines of rock debris on the glacier's surface which strongly backscatter radar.

The radar response from the next zone, termed the "wet snow facies," changes dramatically with the seasons. In summer this snow is wet and dense. Small pools of water may form on the glacier's surface. These surface characteristics imply that nearly all of the radar signals are forward scattered away from the spacecraft. As a result, this zone looks very dark in the summer SAR imagery (See figure1.95 .)

ERS-1 SAR image for 23 June 1993.
Figure 1.95 ERS-1 SAR image for June 23, 1993 The wet snow facies (zone) is dramatically outlined as the dark region between the lower ablation area and the bright percolation facies.

In early autumn, however, it's a different story. The wet snow freezes into coarse crystals which cause strong backscatter. To the SAR sensor, the wet snow zone then appears much brighter. Higher still, the "dry snow facies" lies on and near Mt. Wrangell's summit, where the weather is cold and dry. The radar signals pass right through the summit's homogenous, dry snow and are almost entirely absorbed. Since few signals are backscattered from this region, Mt. Wrangell's crater appears dark, as seen in the December SAR image. Due to the stable snow characteristics atop Mt. Wrangell, this area has a consistent radar signature year-round. (See figure1.96 .)

ERS-1 SAR image for 29 September 1993.
Figure 1.96 ERS-1 SAR image for September 29, 1993

In the above image, water in the wet snow facies is re-freezing, resulting in similar radar responses from that zone and the neighbouring ablation area. The upper boundary of melted snow (the dark region below Mt. Wrangell's base) stands out in this early autumn image.

The dramatic differences between the dark wet snow facies and the bright percolation facies during summer, and between the bright wet snow facies and the relatively dark ablation area in winter, allow researchers to map the extent of a glacier's facies. This information is very important because it is closely related to mass balance: the amount of new snow accumulated versus the amount of meltwater lost. Shifts in facies' boundaries are also indications of climate change; glaciers react significantly to small variations in climate.

ERS-1 SAR image for 30 December 1992.
Figure 1.97 ERS-1 SAR image for December 30, 1992

Mt. Wrangell's crater (left-centre) contains the dry snow facies, where radar signals are primarily absorbed. The arrow marks the late-summer snow line; above this boundary, snow remains on the glacier's surface throughout the year.

A project was undertaken at the Alaska SAR Facility, by visiting scientist Kim Partington, to try and determine if both the ablation/wet snow and wet snow/percolation boundaries could be seen in a single image, if that image was the result of combining imagery from various seasons into one year.

A blue colour scheme was applied to the December image, red to the June image, and green to the September image. The three colour images were then merged. If the red, green, and blue values were all of similar strength, the result was simply a shade of grey. This would imply no change in radar response between the seasons. The percolation facies on Wrangell's upper volcanic cone is white; this zone backscatters brightly year-round. The darker radar signature from the dry snow facies within the crater is similarly stable throughout the year. Blue patches indicate that the corresponding regions backscattered the radar most brightly in winter. The wet snow zone, which has low radar response in the summer but backscatters strongly in the winter, is therefore coloured blue. The pink regions were least bright in late summer and hence mark the upper extent of snow melt. The green area surrounding Mt. Wrangell's summit, implying brightest radar backscatter in September, is believed to be caused by the development of hoar frost at that time of year.

Multi-temporal image which combines the June, September, and December SAR images into one product.
Figure 1.98 Multi-temporal image which combines the June, September, and December SAR images into one product. Nabesna Glacier's wet snow facies is represented by the blue region. The upper boundary of the pink region marks the highest extent of summer snow melt.

ASAR data will provide scientists with a valuable new tool in their investigations into snow cover studies. One such study being proposed by Dr. Jan-Gunnar Winther, head of the Antarctic Section of the Norwegian Polar Institute, is to use ASAR and MERIS data for studies of snow distribution, and glacier characteristics, and to evaluate how these sensors can improve our present use of satellite data for studies of how the cryosphere responds to climate change, as well as for applications within hydropower production management. In particular, the goal is to determine:

  • how snow distribution on Svalbard affects regional climate
  • snow distribution in mountainous areas of Norway, for updating hydrological models used for management of hydropower production

as well as to monitor glaciers on Svalbard for studies of mass balance, surge mechanisms, calving and sensitivity to climate change.


Ref 1.38
Alaska SAR Facility web site: Sea Ice Applications - Sea Ice Mapping

Radar data can be used in applications supporting regional sea ice mapping and monitoring, iceberg monitoring, as well as marine transport and fisheries support. In the remote and extensive areas affected by sea ice, radar remote sensing has been able to provide comprehensive, timely, and accurate information.

Active microwave remote sensing instruments are particularly effective for sea ice mapping because of their all-weather, day-and-night and high-resolution imaging capability. As well, to be an effective information tool, the imagery must be captured on a regular basis if imaging in Arctic regions.

Sea ice research is currently concentrating on increasing our understanding of the processes operating in local areas and on continuous monitoring to identify seasonal changes. Sea ice models, used to estimate surface flux and understand process behaviour, require information on ice extent, concentration, and leads at varying resolutions appropriate to the use of Image (IM) and Wide Swath (WS) Mode data. Monthly, seasonal, and annual products are required. Areas of special interest are monitored continuously, while other areas require data only during times of fieldwork. Some large-scale monitoring is undertaken for which a large swath width is required.

The most important features of interest to potential users are ice concentration and delineation of ice edges. Information regarding the location of the ice edge is vital, since most ships are not ice-strengthened and any contact with ice is potentially dangerous. The discrimination between new ice and water is also required.

ERS-1 SAR Image showing Sea Ice Signatures, Fall Freeze-Up, Beaufort Sea ( image courtesy of Alaska SAR Facility )
Figure 1.99 ERS-1 SAR image showing sea ice signatures, fall freeze-up, Beaufort Sea (image courtesy of Alaska SAR Facility)

ASAR offers data similar to both ERS SAR and RADARSAT, both of which are being used extensively for sea ice research. Both Wide Swath and Image Modes will provide valuable data.

Some ambiguity in the interpretation of newly formed ice and smooth water may occur under calm conditions because the water surface is very similar to that of new ice. In general, the new sea ice, although a relatively flat surface, is "rougher" than a calm water surface (because of the presence of ridges and rafting) and is therefore distinguishable on radar imagery. Generally, the images are interpreted with the use of ancillary data such as meteorological records, recent and historical ice records, bathymetry, and data on ocean and coastal currents and winds.

The identification and mapping of ice types is another feature of interest to navigators. Ice-type definitions are based on the age of the sea ice, which is directly correlated to its thickness and strength. Of greatest importance to navigators is the differentiation between new, first year and multi-year ice. Because of their strength and hardness, multi-year sea ice and icebergs are significant hazards to ships and offshore structures (Ramsay et al, 1993).

There are several cooperative projects collecting and analysing sea ice information. For example, the Arctic Climate System Study (ACSYS), a regional project within the World Climate Research Programme (WCRP), is a ten-year programme that began in 1994. Its objectives are to improve understanding of the processes within the Arctic Ocean, including assembling a basin-wide climatological database of sea ice extent and concentration (from satellite observations) and of ice thickness and motion (using underwater sonars) and drifting ice buoys.

Another investigation, being led by Dr. Son V. Nghiem, at the Jet Propulsion Laboratory in the United States, will develop new algorithms using ENVISAT ASAR and MERIS for sea ice mapping, iceberg tracking, and studying sea ice surface thermal effects. ENVISAT-RADARSAT synergy will be used to enhance sea ice operational applications. The approach is to use ASAR data at different polarisations to classify open ocean and ice for ice mapping, lead detection, and iceberg tracking.

To study sea ice surface thermal effects under clouds, ASAR is used to detect surface temperature change and map melt pond areas under both clear sky and cloudy conditions with MERIS for cloud detection. ASAR Wide Swath imagery is combined with the RADARSAT ScanSAR product to provide a near daily revisit at high latitude with added polarisation diversity to improve ice mapping ambiguities. Sea Ice Application - Ship Routing through Sea Ice

Satellite SAR data is particularly useful for mapping the extent and type of sea ice for ship routing due to the availability of data in all-weather conditions and during darkness. There can be significant benefits in improving the efficiency of offshore operations. Since the launch of ERS-1, systems have been developed which send information and images directly to ice breakers and ice-strengthened ships, and images are also sent to Ice Centres that produce forecasts of ice conditions.

Radarsat Image of the Gulf of St Lawrence, Eastern Canada ( image courtesy of CCRS )
Figure 1.100 RADARSAT image of the Gulf of St Lawrence, Eastern Canada (image courtesy of CCRS)

Figure1.100 above shows a number of different ice types present in the Gulf of St Lawrence in Canada. The Synthetic Aperture Radar (SAR) is sensitive to variations in the salinity, surface roughness and surface wetness of ice.

This type of imagery is very useful to agencies for locating, monitoring and evaluating the movement of sea ice. The ability to determine ice type and monitor ice motion are extremely important to ship navigation. Based on this kind of information, navigators can determine the path of least resistance and plan the ships' routes through the ice. The area indicated by (A) depicts first year ice floes. Rough "brash ice", indicated as (B) and pressure ridges (C) are also clearly visible.

One-to-three day coverage is required for ice forecasting and ship routing. The temporal repeat time is of greater importance than a high spatial resolution and current use of ERS SAR and RADARSAT data has shown that 100 m data will meet the product specification. RADARSAT or ASAR Wide Swath Mode is the obvious choice in order to have the wide area coverage.

Ship navigation requires information at a scale which is useful when supporting ship routing decisions over regions of 1 to 50 km. High-resolution data is required to identify individual ice features, so that in case of an emergency, immediate ship routing decisions can be made. Specific features of interest on acquired imagery include the size and thickness (or type) of ice floes, ice ridges, leads, and icebergs. For navigation, coverage is also required for areas of 100 to 2000 km in extent, at spatial resolutions of 1 to 50 km. At this scale, broad ice conditions can be assessed and ship operators can identify the overall least costly or least hazardous route. The information requirements for these applications include ice edge location, ice concentration and type determination, and lead detection (open areas or cracks in the ice).

Small-scale (large area) imagery provides regional mapping capability for sea ice concentration, ice edge location, ice motion tracking, and ice type classification. This information can be useful for tactical navigation depending on the actual ice environment. Intermediate-scale imagery provides detail for medium-scale mapping and tactical navigation support.

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry