DEPT OF CIVIL ENGINEERING, TEXAS A&M UNIVERSITY MAY 03, 2004

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DEPT OF CIVIL ENGINEERING, TEXAS A&M UNIVERSITY MAY 03, 2004 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 A&M UNIVERSITY MAY 03, 2004 ABSTRACT Soil moisture is a natural variable of the earth’s 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 RAINFALL JUNE 29 TB JUNE 30 JULY 01 JULY 02 JULY 03 DRAWDOWN PERIOD ( SOIL DRIES OVER THIS PERIOD ) BRIGHTNESS TEMPERATURE (TB) PERCENT SAND Soil moisture = f (Brightness Temperature TB) FUTURE APPLICATIONS COMPARISON - SAND & CLAY 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 (GCM’s) which are used to predict climate