Tests of sequential data assimilation for retrieving profile soil moistureand temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and tem perature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure use s a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through ti me, The model's coupled soil moisture and heat fluxes are constrained by mi crometeorology boundary conditions drawn from observations or atmospheric m odeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covaria nce estimates against an a priori estimate of radiometric measurement error . The Kalman filter compares predicted and observed radiobrightnesses direc tly, so no inverse algorithm relating brightness to physical parameters is required. We demonstrate Kalman filter model effectiveness using field obse rvations and a simulation study. An observed 1 m soil state profile is reco vered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study , we gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobr ightnesses.