Soil moisture mapping for different land-use patterns of lower Bhavani river basin using vegetative index and land surface temperature

Soil moisture is the significant hydrologic factor deals with energy balance between the land and the atmosphere. Since the observation of soil moisture at point scale is infrequent and expensive, remote sensing determines the distribution of soil moisture in large scale. In this study, remote sensing techniques have been used to calculate the soil moisture index in the lower Bhavani river basin, Tamil Nadu, India. Landsat 8 satellite data were used for deriving soil moisture in reference with land surface temperature (LST) and normalized difference vegetative index (NDVI). The derived soil moisture was compared to the in situ soil moisture measurements, which were taken at 83 sites at the field level. When a linear regression model was fit between in situ observations and derived soil moisture, a high coefficient of determination (R2) value of 0.83 was found which can be efficiently used for the moisture estimation across greater areas. Since the land-use patterns influences the LST and soil moisture, the variations of these parameters in each land-use classes were studied using independent t test and found that LST demonstrated statistical non-significance (p > 0.05) for each of the studied groups, indicating that each land-use classes temperature were similar, whereas soil moisture in water bodies versus fallow land (p = 0.019), built-ups versus water bodies (p = 0.023), forest versus fallow land (p = 0.018), vegetation versus built-ups (p = 0.028), and built-ups versus forest (p = 0.011) has statistical significance value, which indicates that soil moisture between these compared classes was not similar.