Soil moisture (SM) is widely used in analyzing the interaction of ground and atmosphere. It has applications in many disciplines including but not limited to weather forecasting and hydrological modeling. Measuring SM is difficult. It has been traditionally carried out by time-consuming field work consisting of direct ground-based soil sampling. Remote sensing seems promising for estimation of SM, because of its unique data gathering specifications. Among different sensors in remote sensing, synthetic aperture radar (SAR) sensors have attracted considerable attention for SM estimation because of their high resolution, independence from weather conditions, and sensitivity to changes in soil dielectric constant, which can be used to quantify SM.
The relationship between the observations of a SAR system and the dielectric constant of soil is usually described using a mathematical model known as surface scattering model. A class of information which can be utilized in post-processing the outputs of these models to improve their performance include the information about the spatial variability of SM. Analyzing the spatial variability of SM can help in calibrating the results of the scattering models. A model may be established for predicting the difference between the outputs of a scattering model and the field-measured SM using a set of concurrent SAR data and ground measurements, which may be generalized to SAR data acquired on other dates.
In this thesis, the spatial variability of SM estimated by the Integral Equation Model (IEM) is analyzed by the STRAIN multifractal model which is a multi-resolution tool. The IEM is selected because of its superior inversion pattern which is necessary for multifractal analysis. We propose a simple calibration model for improving the quality of the results of the IEM based on the relationship between the parameters of the multifractal model and ground measurements of SM.
The results of the experiments in this study show that, the proposed calibration model is, to some extent, robust when considering SAR images acquired on different dates, and can usually improve the agreement between ground measurements of SM and SM estimated by the IEM.