in June 10-13, 
s show higher 
oam, and clay 
  
  
98°15" 580000m E. 59 oo' 600000m E. *50'W 
     
'N u0000/ BE 
38 70000m N. 
®353°N 
‘N WO0009BE N.SSebE 
3860000n N. 
7 
N.0S.PE 
98°15’W 0000 4 00* 97°50°W 
UTM Zone 14N Clarke 1866 (NAD 27) 580000 x 59 600000n E. 
  
  
  
   
= Sand : Loam Quarries/Urban 
N | Loamy Fine Sand SS Silt Loam Gypsum 
DZ Fine Sandy Loam THAT Clay Loam Water 
  
  
Figure 7. Map of soil texture for the Little Washita Watershed. Source: MAIDS database. 
experiment. This is an important observation hydraulic conductivity. The long term potential 
from the hydrologic research perspective, in that of the observations of the present study are 
it demonstrates that the temporal changes in soil derivation of soil properties on a regional and 
moisture can be related to soil hydraulic continental scale from space borne remote 
properties. The relative rate of change of soil sensing platforms for input into mesoscale 
moisture can, therefore, be employed as an models and global circulation models. 
indicator of the soil type; i.e., under similar 
conditions, a sandy soil dried (decrease in soil 
moisture) more rapidly than a clay soil. 5. CONCLUSIONS 
Transition areas between sandy and clayey soils 
are characterized by strong hydraulic gradients 
(very close contours in figure 6), which Moisture content in the surface layer of 
identifies hydrologically active areas where soil the soil is important for hydrologic research. 
moisture movement occurs. Microwave remote sensing was employed to 
obtain spatial and multi-temporal soil moisture 
data for the Little Washita watershed. As part of 
Above observations have a significant the Washita'92 airborne campaign during June 
potential to employ remotely sensed soil 10-18, 1992, the ESTAR instrument was flown 
moisture data organized in a GIS to derive soil each day (except June 15, 1992, the crew rest 
properties. Ahuja et al. (1993) established that day) which provided multi-temporal brightness 
the two-day drainage data can be related to the temperature data at a spatial resolution of 200 m 
saturated hydraulic conductivity. Therefore, x 200 m. The brightness temperature data were 
remotely sensed soil moisture data obtained at a converted into soil moisture information. The 
temporal resolution of two days can be used to data sets were georeferenced in a raster-based 
generate soil hydraulic conductivity (Mattikalli GIS to monitor and quantify spatial and 
et al., 1995b). On these lines, further research temporal variability of surface soil moisture. 
has been carried out to estimate sub-surface soil Analysis of soil moisture changes and soils data 
properties from remotely sensed observations. In reveals a direct relationship between changes in 
this research, a state-of-the-art hydrologic model soil moisture and soil texture. This observation 
and a GIS have been employed to carryout soil leads to the estimation of soil hydraulic 
moisture simulation studies (Mattikalli et al., properties using temporal soil moisture data. 
1995c). Strong relationships have been The present research demonstrates that a GIS is a 
developed between the changes in surface (0-5 valuable tool to establish relationship between 
cm) soil moisture and the sub-surface (for temporal changes in remotely sensed surface soil 
various depths from 0 to 60 cm) saturated moisture and soil properties. Further extensive 
51