Estimation and Prediction of Soil Moisture from Multi-temporal Radar Measurements Based on the Kalman Filter Technique

Abstract

Monitoring soil moisture changes is of importance in many applications. Studies show that radar has great potential to provide useful near surface moisture mapping. By making use of the temporal correlation of the moisture changes, estimation and prediction might be achieved with satisfying accuracy. The temporal effects, when appropriately modeled, can be taken into account by means of the Kalman filter theory. The two equations in the Kalman filter, namely, process equation and measurement equation, are used simultaneously to estimate moisture content from radar measurements and to predict the moisture changes over time. The relationship of the moisture content and radar scattering coefficient is built into the measurement equation, while the dry-up (or wet-down) of soil moisture in multi-temporal states was modeled into the process equation. Validation of the presented approach is verified through numerical simulations of radar measurements over the bare soil surfaces. Simulation results indicate that the estimation and prediction of the mutli-temporal moisture content can be improved when the dry-up or wet-down process is properly modeled.