Combining the Optical and Microwave Remote Sensing Indices for Soil Moisture Assessment: An Empirical Study

  • Vidhya, R., Adayapalam, S., Ramakrishnan R


Radar and Optical sensor integration combines traditional space-borne optical data from the visible and infrared wavelengths with the longer wavelengths of radar to improve land cover classification and the potential of land use classification. Optical data gives the details of vegetation and microwave data on the other hand provide the texture and terrain characteristics. The indicators of vegetation stress in optical include NDVI analysis and texture and Dielectric Constant of microwave. The LISS III data for optical and EnviSAT ASAR data for backscattering coefficient and dielectric constant were used. Soil Moisture plays an important role in the interactions between the land surface and the atmosphere. Soil moisture is a highly variable component in land surface hydrology and plays a critical role in agriculture and hydrometeorology and has been found not easy to map with only optical data. Therefore, a study to fit a mathematical equation to relate soil moisture condition and the micro wave parameters in a part of Cumbum valley in south west Tamil Nadu. The optical vegetation stress indicators were studied along with the micro wave parameters were analyzed to judge upon the vegetation stress. The present work is to have synergetic use of the bands from both optical and microwave data which are quite complementary in nature. The optical and microwave data are processed using separate tools. An index called Soil Moisture Index function was generated for the estimation of the wet and dry nature of the soil. The NDVI image was generated and it was cross verified with land cover of the study area. As the NDVI is a consistent parameter of vegetation stress, this image was used as the base for the soil moisture equation. The dielectric constant generated from the back scatter image was referenced with the NDVI image. From the generated back scatter and dielectric constant values, soil moisture index was estimated. Regression and correlation analysis was carried out for the prediction of the values and the error estimation of the generated empirical relation of BSC and DC with respect to the SMI. The result showed a good agreement of dielectric constant with soil moisture index yielding R2=0.958 and for backscatter and soil moisture index yielding R2=0.694 for the generated SMI relation. A map was generated to indicate the soil moisture across space. It is observed that an empirical relation between the dielectric constant and the soil moisture stress is possible to draw by correlation and the study has to be perfected with more critical field observed data.