Soil Moisture Estimation Using Thermal Inertia: Potential and Sensitivity to Data Conditions

Abstract

hermal inertia retrieval using a thermal infrared remote sensing technique has been examined as a possible method for estimating soil moisture. This method is an application of the theory that thermal inertia highly correlates with soil water content. This study shows a method for retrieving thermal inertia from a heat budget model of the earth?s surface using radiative surface temperatures, insolation, and meteorological data observed in field experiments. In bare to sparsely vegetated areas, this method has the potential to estimate subsurface soil moisture with a precision of ±3%?4% of the daily volumetric soil moisture content at a significance level of 5%, which is enough to roughly classify thermal inertia estimates into a few levels of soil moisture (e.g., wet, middle, and dry). The analysis also includes an examination of the practical performance of the thermal inertia estimation according to the temporal resolution of the data, assuming the use of satellite and routine meteorological data. It is found that the following combination of data can achieve the precision given above: radiative surface temperature from geostationary/multiple polar orbiting satellites, insolation retrieved from geostationary satellite data, and routine meteorological data.