Research on Improved Thermal Inertia Model for Retrieving Soil Moisture

Abstract

Soil moisture is one of the most important land environmental variables, relative to land surface climatology, hydrology, and ecology. A method to estimate soil moisture content from optical and thermal spectral in-formation of ASTER imagery based on thermal inertia is presented in this paper. Compared to models published previously, four improvements have been made: (1) as a key component of soil surface energy balance, the series two-layer is applied to solving soil latent and sensible heat flux in the better-covered vegetation area. And the Shuttleworth and Wallace (S-W) ET model is used to simulate soil latent flux; (2) because component temperature inversion is still an ill-posed problem, genetic inverse algorithm (GIA) is used to realize retrieval of component temperature; (3) in order to extend the scope of the thermal inertia model, B in the equation is derived from mechanism; (4) to eliminate partly atmospheric and the surface structure influence, the improved thermal inertia was normalized to fulfill the inversion of soil moisture. Taking YingKe green land in china for example, field experiment were carried out to validate the developed model. The method successfully estimated better-covered vegetation region surface soil moisture with an average error of 0.067. This model provides a new way of thinking about remote sensing thermal inertia methods to acquire regional-scale soil moisture.