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surface water shown as black versus flooded areas 
at different time intervals can be displayed in 
different colors. The RGB image was able to show 
the flood progress. RADARSAT images were the 
only images available for the flooded areas with 
such a short time lag after the flood onset. It was the 
only sensor able to repeat images in very short time 
intervals. 
Radar sensors are the only ones which can 
penetrate clouds, fog, and smog. Moravia was 
covered by clouds nearly the whole time, and thus 
no optical data were available. Scanner data were 
used for land use determination in cases where the 
situation on radar images were not clear. SPOT data 
showed clearly agricultural areas with new and 
permanent crops as they were from May 5 and May 
18. TM was useful in areas out of SPOT images. 
These land use maps were used for creating the 
model of the erosion susceptibility. 
6. DISCUSSION 
Any automated classification especialy applied for 
radar images and performed without an additional 
visual interpretation can result in erroneous 
information. For example radar shadow found in 
mountainous areas can have pixel values the same 
as a smooth water surface. A post-classification 
modification must be applied. 
Visual interpretation was necessary for 
forest areas and urban regions on single images. 
Single images display only surface water bodies 
existing at the time of image capture. It is not 
possible to distinguish permanent water bodies from 
flooded areas. In contrast, image pairs from the 
moment of flood and before or after flood enable 
one to discriminate permanent and flooded areas. 
This task can be easily performed by creating color 
composites. 
Images from after a flood can be useful in 
cases when no images from the flood itself are 
available. Higher soil moisture as a consequence of 
flooding causes a higher backscatter, and thus can 
be interpreted as brighter regions on the post-flood 
image. To decide whether a brighter backscatter 
value is due to high moisture requires information 
about the locality and to be able to exclude surface 
roughness which can be related to field activities 
such as ploughing, and to the previous flood in case 
of coarse sediments. Incidence angle are another 
phenomenon which must be taken into account. 
Steeper incidence angle emphasize soil moisture 
influence of radar reflection. In contrast, shallower 
incidence angles are more influenced by surface 
roughness. 
Scanner data did not bring any real flood 
information due to their weather sensitivity and real 
atmospheric situation during the flood that did not 
make possible to image the flooded area during the 
flood. 
References: 
RADARSAT Illuminated. (1995) User Guide, 
RADARSAT International. 
Ahern, F. J. (1995) Fundamental concepts of 
imaging radar: basic level (unpublished 
manual), Canada Centre for Remote Sensing, 
Ottawa, Canada. 
Leconte, R. and Pultz, T. J. (1991) Evaluation of 
the potential of RADARSAT for flood 
mapping using simulated satellite imagery, 
Canadian Journal of Remote Sensing, 3, 241 - 
249. 
Ulaby, F. T. et al (1974), Radar measurement of 
soil moisture content, JEEE Transaction on 
Antennas and Propagation, 2, 257 - 265. 
Brown, R. J. et al (1993), Potential applications of 
RADARSAT data to agriculture and 
hydrology, Canadian Journal of Remote 
Sensing, 4, 317 - 329. 
Engman, E. T. and Chauhan, N. (1995), Status of 
microwave soil moisture measurements with 
remote sensing, Remote Sensing of 
Environment, 51, 189 - 198. 
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 
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