Estimating Soil Water Contents from Soil Temperature Measurements by Using an Adaptive Kalman Filter

Abstract

A simple soil heat transfer model is used together with an adaptive Kalman filter to estimate the daily averaged soil volumetric water contents from diurnal variations of the soil temperatures measured at different depths. In this method, the soil water contents are estimated as control variables that regulate the variations of soil temperatures at different depths and make the model nonbiased, while the model system noise covariance matrix is estimated by the covariance-matching technique. The method is tested with soil temperature data collected during 1?31 July 2000 from the Soil Water and Temperature System (SWATS) within the Oklahoma Atmospheric Radiation Measurement (ARM) central facilities at Lamont. The estimated soil water contents are verified against the observed values, and the rms differences are found to be small. Sensitivity tests are performed, showing that the method is reliable and stable.