IEEE Geoscience and Remote Sensing Magazine - March 2013 - 37

VIII. Summary and Outlook
Summary
This paper provides a tutorial on Synthetic Aperture
Radar systems with a focus on the basic theory, imaging
techniques, applications and future technologies. Looking back 30 years ago, the remote sensing community

(a)

(b)

was postulating statements about "the promise of radar
remote sensing." While at that time the Landsat series
of satellites was already operational and widely used for
many applications, SAR remote sensing was just in its
infancy. There was a big expectation about the future of
radar remote sensing. In the 90s a paradigm shift occurred
in the SAR development from technology push to the
user demand pull. We have now entered into a golden
age for spaceborne SAR with more than 10 satellites to
be launched within the next 5 years. Many applications
became mature and are an integral part in operational
services of geo-information products as well as in geoscience and climate research. Spaceborne SAR sensors
have the only technology that can provide all-weather
day-and-night high-resolution information about Earth's
dynamic processes on a global scale. Table 7 summarizes
a compilation of application examples which are classified in the following categories: Land, vegetation, ocean,
sea ice, snow and land ice.
As described in Sections III to V, the research on information retrieval from SAR images clearly shows a trend for
using model-based inversion algorithms. The observation
interval requirements for the SAR sensor vary from hours
to a day (e.g., disaster monitoring), over a day to weeks
(e.g., soil moisture, agriculture) up to years and decades
(e.g., subsidence, eco-systems, climate change). In a general sense, the reliability and robustness of the information retrieval increase with the amount of observables and
information content available in the data. This increase
in the information content can be achieved by several

Table 7. COMPILATION OF INFORMATION EXTRACTION
AND APPLICATION EXAMPLES FOR SAR IMAGERY.
ESSENTIAL CLIMATE VARIABLES (ECV) AS DEFINED BY THE
INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC)
ARE IDENTIFIED WITH "*".
Land

Multi-purpose land surface imagery, soil type, land
cover*, Earth topography (elevation and surface model),
lake levels*, subsidence, landslides, erosion, earthquake
and volcano monitoring, disaster monitoring, mitigation
and assessment, flooding monitoring, coherent change
detection, urban and infrastructure planning, road
traffic monitoring, soil moisture*, wetlands monitoring,
permafrost and seasonally-frozen ground*

Vegetation

Vegetation type, forest biomass*, forest biomass
change, biodiversity, forest profile, forest height,
fire disturbance and monitoring, crop classification,
crop height, crop biomass, deforestation and
forest degradation

Ocean

Multi-purpose ocean imagery, sea state*, ocean
currents*, wind speed and vector over sea surface,
bathymetry at coastal zones, wave height, ocean
wavelength, wave direction, oil spill cover,
ship monitoring

Sea ice

Sea-ice cover and extent, sea-ice type, sea-ice thickness,
iceberg cover and movement, ship route optimization

Snow and
land ice

Snow cover*, ice and glacier cover*, snow melting status
(wet/dry), snow water equivalent, glacier motion and
dynamics, glacier topography

achieved by improved geometric resolution (system bandwidth)
and multi-channel operation (polarimetry). (a) X-band image with
1 m resolution (state of the art of spaceborne X-band sensors in
Spotlight imaging mode); (b) and (c) X-band image with 0.25 cm
resolution without and with a fully polarimetric imaging mode,
respectively (simulation of the data quality to be provided by the
next generation of spaceborne X-band sensors). Imaged area:
Kaufbeuren, Germany. The images in (b) and (c) were acquired by
the F-SAR sensor of DLR [21].
march 2013

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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2013