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Modified Dubois Model for Estimating Soil Moisture with Dual Polarized SAR Data |
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| Authors: | S Srinivasa Rao S Dinesh kumar S N Das M S S Nagaraju M V Venugopal Prashant Rajankar Pankaj Laghate M Sivaprasad Reddy A K Joshi J R Sharma |
| Institution: | 1. Regional Centres, National Remote Sensing Centre (NRSC), Hyderabad, India 7. Regional Centres, National Remote Sensing Centre/Indian Space Research Organisation, Hyderabad, 500037, India 2. Regional Remote Sensing Centre-Central/NRSC, Nagpur, India 3. National Bureau of Soil Survey and Land Use Planning, Nagpur, India 4. Central Institute for Cotton Research, Nagpur, India 5. Maharashtra Remote Sensing Applications Centre (MRSAC), Nagpur, India 6. Soil and Land Use Survey of India (SLUSI), Nagpur, India |
| Abstract: | This paper discusses a new methodology to estimate soil moisture in agriculture region using SAR data with the use of HH and HV polarization. In this study the semi empirical model derived by Dubois et al. (IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926, 1995) was modified to work using σ HH instead of two like polarization equations σHH, σVV so that soil moisture can be obtained for the larger area frequently. The field derived roughness correlated with the cross polarization ratio (HV/HH) to replace the one unknown parameter ‘s’ in the Dubois model and hence the dielectric constant was derived by inverting the Dubois model equation (HH). The Topp et al. (Water Resources Research, 16(3), 574–582, 1980) model was used to retrieve soil moisture using the dielectric constant. The mid incidence angle was used to overcome the incident angle effect and it worked successfully to the larger extent. The result is realistic overall, especially where surface has less variation in the roughness and vegetation since the penetration capability of C-band is limited when plant grows hence model valid in the initial period of cultivation. The derived model is having good scope for soil moisture monitoring with the present availability of Indian RISAT data. |
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