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We present a biophysically based, one-dimensional hydrology/radiobrightness (1dH/R) model for prairie grassland that is subject to solar heating, radiant heating and cooling, precipitation, and sensible and latent heat exchanges with the atmosphere. The 1dH/R model consists of two modules, a one-dimensional hydrology (1dH) module that estimates the temperature and moisture profiles of the soil and the canopy and microwave emission module that predicts radiobrightness (R). We validate the 1dH/R model by comparing its predictions with data from a field experiment. The model was forced by meteorological and sky radiance data from our Radiobrightness Energy Balance Experiment (REBEX-1) on prairie grassland near Sioux Falls, SD, during the fall and winter of 1992-1993. Model predictions were compared with 995 consecutive REBEX-1 observations over a 14-day period in October. Average errors (predicted-measured) for canopy temperature are 1.1 K with a variance of 3.72 [K.sup.2], for soil temperatures at 2-, 4-, 8-, 16-, 32-, and 64-cm depths are 2 K with a variance of 4 [K.sup.2], and for H-polarized brightnesses are 0.06 K with a variance of 1.30 [K.sup.2] at 19 GHz and 6.01 K with a variance of 6.04 [K.sup.2] at 37 GHz. The model overestimates the 37.GHz brightness because we have not included scatter darkening within the vegetation canopy in the model. We use the 1dH/R model to simulate a 60-day dry-down of prairie grassland in summer. For grass with a column density of 3.7 kg/[m.sup.2] and soil with an initially uniform moisture content of 38% by volume, the upper 5 mm of soil dries to 27% by the end of the simulation. The corresponding L-band brightness increases from an initial 143 K to a final 163 K. In contrast, none of the special sensor microwave/imager (SSM/I) radiobrightnesses nor the radiobrightness thermal inertia (RTI) technique, either at L-band or at any SSM/I frequency, exhibits significant sensitivity to the soil dry-down.

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. We demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered 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 radiobrightnesses. Index Terms - Data assimilation, Kalman filter, microwave radiometry, soil coupled heat and moisture models, soil moisture.