IDENTIFYING SOIL TYPES USING SOIL BRIGHTNESS TEMPERATURE DATA OBTAINED BY REMOTE SENSING

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IDENTIFYING SOIL TYPES USING SOIL BRIGHTNESS
TEMPERATURE DATA OBTAINED BY REMOTE
SENSING SUBMITTED BY UDAY SANT CVEN 689
APPLICATIONS OF GIS TO CIVIL ENGINEERING
INSTRUCTOR DR.FRANCISCO OLIVERA DEPT OF CIVIL
ENGINEERING, TEXAS AM UNIVERSITY MAY 03, 2004


ABSTRACT Soil moisture is a natural variable of
the earths surface and the most important data
of a watershed. The temporal and spatial
distribution of soil moisture is affected by
relations between soil, vegetation, topography
and environment. Remote sensing is capable of
measuring soil moisture across a wide area
instead of at discrete point locations that are
associated with ground measurements. Radar
backscatter response is affected by soil
moisture, in addition to topography, surface
roughness and amount and type of vegetative
cover. Keeping the latter elements static,
multitemporal radar images can show the change in
soil moisture over time. Using GIS the emphasis
here would be to confirm that the obtained
temporal resolutions which show a different
change of surface soil moisture for various days
can identify soil types. This concept could then
be extended for larger scales in land-use
management and hydrology.
METHODOLOGY
RESULTS
ZONAL STATISTICS ( ARCGIS 8.3 )
SPATIAL AND TEMPORAL GRAPHS DEPICTING SOIL
DRYING RATES OVER A DRAWDOWN PERIOD
MEAN
TB

STUDY AREA
PERCENT SAND
BRIGHTNESS TEMPERATURE (TB)
Soil moisture f (Brightness Temperature TB)
COMPARISON - SAND CLAY
FUTURE APPLICATIONS

• CONCLUSIONS
• Each soil has a different rate of change of
  surface soil moisture
• Percentage sand holds a good correlation with
  changes in Tb (R2 approx 0.7)
• The same type of relationship could not be
  observed for percentage clay
• The strong relationships observed do confirm that
  temporal changes in brightness temperature can be
  used to identify soil types
• The regression equations can be utilized to
  observe the spatial variability on larger scales
• Can be used as input in Global Circulation Models
  (GCMs) which are used to predict climate