Uncertainties analysis for normalized soil moisture model based on the combination of optical and thermal infrared data

Surface soil moisture (SSM) is an important variable in environmental studies and land surface system research. Remote sensing techniques provide a direct and convenient means to estimate SWC on a regional scale. Land surface temperature (LST) and vegetation index (VI) can be employed to construct a feature space that represents surface dry and wet conditions. A normalized soil water content model was developed to obtain comparable SWC using normalized LST (T*) and VI. The quantitative relationship among SWC, LST and VI was shown by a quadratic polynomial equation. In this study, a uncertainty sensitivity analysis for the input parameters and other factors that might affect the established model was conducted. T* and Fractional vegetation cover (FVC) are key inputs, and the uncertainties introduced by them are necessarily required for the model. The results showed that LST affects the estimation results enormously. The estimation error is approximately 0.05 m3/m3 when the LST with 1K uncertainties. And the FVC has a weak influence on the soil moisture estimation. The estimation error will be 0.03 m3/m3 when the FVC has an error of 0.2. For targeted analysis, LST changes from -0.2k to 0.2K and FVC has an error of 0.2. The largest error of estimation can reach 0.03 m3/m3 when the two variables have the biggest uncertainties.