Full text: Resource and environmental monitoring (A)

  
IAPRS & SIS, Vol.34, Part 7, "Resource and Environmental Monitoring", Hyderabad, India, 2002 
  
Figure 1. Methodology for runoff estimation 
The information extracted from the remote sensing data is in 
the form of Digital Numbers, which needs to be transformed in 
the real parametric values by using models. Furthermore the 
radiance values of the different land cover features have to be 
further corrected which may not be real because of the 
atmospheric effects. The estimation of parametric values using 
active and microwave remote sensing is dependent on the 
dielectric constant, the roughness of the surface (Van Oevelan, 
2000) and state of the atmosphere. In most images from active 
radar systems for e.g. the most obvious features are those 
associated with the topography and the roughness of the 
surface, such as different vegetation covers. Many radar 
backscattering inversion techniques have been practised for 
estimation of surface soil moisture (Van Oevelen and 
Hoekman, 1999) (Fig.2). Geographical Information system is 
increasingly being used to store catchment data and interact 
with distributed hydrological models in setting up model and 
runs the displaying the results. The information stored in GIS 
(e.g. soil type, vegetation type) may also require an 
interpretative model before being useful in hydrological 
modeling (Beven, K.J., 2002). 
4. EXPECTED RESULTS 
The present study will help to evaluate the following outputs: 
I. Estimating effective parameteric values of soil 
moisture using microwave data 
2. Hydrological behaviour of the watershed. 
3. Factors leading to érosion 
4. Runoff estimation 
  
     
Badscatter 
Coefficient 
oe. 
       
   
DEM or DTM 
Iiirgriiiprirprrret 
ei 
i 
i 
Database/GIS 
Other RS data | 
   
    
   
Ini 
-NDVI 
-Pol. Scatter. Class 
-Ground tutVGIS 
  
   
    
-Clo 
ILAMEN UE EC ENS 
ud model || 
-Rad. transfer | 
   
-Autocorrelaticn length i 
-RMS hight difference |} 
    
  
   
Dielectic 
properties 
  
  
   
   
Tm 
-Semi-empirical approaches |i 
Wang and Schmugge (1980) | 
Dobson et al., (1095) 
Hallikainen et al.. (19855 
       
Figure 2. Steps in the inversion of soil moisture from 
microwave backscatter measurements (Van Oevelan, 2000) 
5. REFERENCES 
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744 
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Band, L. E. and I. D. Moore, 1995. Scale: landscape attributes 
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Band, L. E., D. L. Peterson, R. Nemani, and S. W. Running, 
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Barrow C.J., 1991. Land degradation. Development and 
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Beven, KJ. 1993. Prophecy, reality and uncertainty in 
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Beven, K.J, 1997. Topmodel: a critique. Hydrological 
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Beven, K.J., 2002. Rainfall-Runoff Modelling The Primer. John 
Wiley and Sons Ltd, U.K 
Beven K.J., Kirkby M.J., 1979. A physically based variable 
contributing area model of a basin hydrology. 
Hydrological Science Bulletin, 24, 43-69. 
De Roo A.PJ., 1993. Modelling surface runoff and Soil 
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Information Sytems. Validity and Applicability of the 
ANSWERS’ Model in Two catchments in the Loess 
Area of South Limberg (The Netherlands) and One in 
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Entekhabi, D., H. Nakamura, and E. G. Njoku, 1994. Solving 
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Eppink, L.A.A.J. and L. Stroosnijder, 1995. Processes and 
models in soil and water conservation, Lecture notes, 
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Famiglietti, J. S. and E. F. Wood, 1991. Evapotranspiration and 
runoff for large land areas: land surface hydrology for 
atmospheric general circulation models. Surv. 
Geophys., 12: 179-204. 
Freer, J. Beven, KJ. and Ambroise, B. 1996. Bayesian 
estimation of uncertainty in runoff prediction and the 
value of data: an application of the GLUE approach. 
Wat. Resour. Res. In press. 
Jong de, S.M., 1994. Applications of Reflective Remote 
Sensing for Land degradation Studies in a 
Mediterranean Environment, University of Utrecht, 
Utrecht, The Netherlands
	        
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