The penetration capability of longer radar wavelengths is much better than that of
shorter wavelengths and thus potentially provides information on the vegetation
volume. But shorter wavelengths provide more information about the top of the
vegetation canopy. In the absence of any vegetation cover, microwaves can also
penetrate soil, particularly when the soil is dry. The penetration is very much a
function of wavelength. Longer wavelengths penetrate dry soil significantly, i.e. in
magnitudes of centimeter, decimeter and even meter: the degree of penetration for
shorter wavelengths is comparatively low, i.e. in the millimeter range, but nevertheless larger than the penetration of optical sensors.
The third reason for using radar systems from the fact that the information
extracted from RADAR images is unique in its own right. The information content
of radar imagery differs from the information content of optical imagery and may
therefore be complementary. Consider the example of a vegetation canopy: the
content of imagery taken in the visible or infrared regions of the spectrum is largely
determined by the molecular resonance in the surface layer. However, the content
of a radar image of the same object would be determined by the geometric
properties, or structure and the moisture content of the surface as well as the volume
of the vegetation canopy. The combined analysis of both data sets renders more
useful information and results than the interpretation of one data set alone.
The following imaging characteristics set microwave remote sensing systems,
particularly synthetic aperture radars (SARs), apart from the familiar sensor systems such as multispectral scanners:
– Radar is sensitive to surface roughness, moisture, electrical properties and
motion within the illuminated scene;
– Radar instruments can be designed to record phase and polarization characteristics of the reflected microwave energy;
– Radar imagery shows relief displacement such as layover as a result of the slant
range viewing geometry;
– SAR imagery displays speckle, or image ‘noise’ because of the coherent nature
of the system.
These are important characteristics that will provide the radar with a different
frame of reference for analyzing remote sensing data. A person familiar with aerial
photography or multispectral image interpretation may find it relatively easy to
identify objects on radar imagery by virtue of their size and shape alone, for
example agricultural field patterns. However, the analysis of radar image tone
and texture of these fields requires an understanding of the backscattering properties, of radar image formation and of the processing techniques available for
radar data.
In addition, the recent technology of RADAR is Interferometric synthetic aperture radar (InSAR) remote sensing data. InSAR remote sensing data (Fig. 14.1) can
be used in various applications of environment and earth sciences including natural
hazards. For example: the Bam earthquake in Iran (Saraf et al. 2008; Fielding
et al. 2009) occurred in December 26, 2003 with 6.6 magnitude has been studied
(Amani et al. 2013) using InSAR data (Fig. 14.2).
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283
shorter wavelengths and thus potentially provides information on the vegetation
volume. But shorter wavelengths provide more information about the top of the
vegetation canopy. In the absence of any vegetation cover, microwaves can also
penetrate soil, particularly when the soil is dry. The penetration is very much a
function of wavelength. Longer wavelengths penetrate dry soil significantly, i.e. in
magnitudes of centimeter, decimeter and even meter: the degree of penetration for
shorter wavelengths is comparatively low, i.e. in the millimeter range, but nevertheless larger than the penetration of optical sensors.
The third reason for using radar systems from the fact that the information
extracted from RADAR images is unique in its own right. The information content
of radar imagery differs from the information content of optical imagery and may
therefore be complementary. Consider the example of a vegetation canopy: the
content of imagery taken in the visible or infrared regions of the spectrum is largely
determined by the molecular resonance in the surface layer. However, the content
of a radar image of the same object would be determined by the geometric
properties, or structure and the moisture content of the surface as well as the volume
of the vegetation canopy. The combined analysis of both data sets renders more
useful information and results than the interpretation of one data set alone.
The following imaging characteristics set microwave remote sensing systems,
particularly synthetic aperture radars (SARs), apart from the familiar sensor systems such as multispectral scanners:
– Radar is sensitive to surface roughness, moisture, electrical properties and
motion within the illuminated scene;
– Radar instruments can be designed to record phase and polarization characteristics of the reflected microwave energy;
– Radar imagery shows relief displacement such as layover as a result of the slant
range viewing geometry;
– SAR imagery displays speckle, or image ‘noise’ because of the coherent nature
of the system.
These are important characteristics that will provide the radar with a different
frame of reference for analyzing remote sensing data. A person familiar with aerial
photography or multispectral image interpretation may find it relatively easy to
identify objects on radar imagery by virtue of their size and shape alone, for
example agricultural field patterns. However, the analysis of radar image tone
and texture of these fields requires an understanding of the backscattering properties, of radar image formation and of the processing techniques available for
radar data.
In addition, the recent technology of RADAR is Interferometric synthetic aperture radar (InSAR) remote sensing data. InSAR remote sensing data (Fig. 14.1) can
be used in various applications of environment and earth sciences including natural
hazards. For example: the Bam earthquake in Iran (Saraf et al. 2008; Fielding
et al. 2009) occurred in December 26, 2003 with 6.6 magnitude has been studied
(Amani et al. 2013) using InSAR data (Fig. 14.2).
14 Digital Processing of SAR Data and Image Analysis Techniques
283