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Data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) have been used to classify snow-free soils in the northern Great Plains as either frozen or thawed. The technique is based on differing sensitivities among SMMR radiobrightness frequencies to liquid moisture and volume scattering in the upper few millimeters of bare soil. The SMMR is no longer active. A current near-equivalent is the Special Sensor Microwave/Imager (SSM/I). We demonstrate that SSM/I radiobrightnesses also exhibit differential sensitivities to liquid water and volume scattering in frozen soil despite their higher frequencies. We find that the best classification discriminants for SSM/I data are a combination of the 37-GHz V-poi radio-brightnesses and the 19-to-37-GHz V-pol spectral gradients. We also examine the sensitivity of the classification to atmospheric emission and absorption and find little effect.

We compare ground-based and the special sensor microwave/imager (SSM/I) brightness temperatures at 19 and 37 GHz in the Northern and the Southern Great Plains. The comparison was conducted to examine season-related differences in plot-scale and satellite footprint-scale brightness temperatures at these frequencies. The ground-based observations were from the three Radiobrightness Energy Balance Experiments (REBEXs), viz., REBEX-1, REBEX-4, and REBEX-5. REBEX-1 and REBEX-4 were conducted near Sioux Falls, SD, in fall and winter 1992--93, and in summer 1996, respectively. REBEX-5 was conducted near Lamont, OK, during summer 1997 as part of the Southern Great Plains Hydrology Experiment-1997 (SGP'97). The instantaneous fields of view (FOV) of the ground-based radiometers were only a few meters compared to those of the SSM/I, which were several tens of kilometers. The REBEX and the SSM/I brightness temperatures are moderately correlated at both the 19 and 37 GHz. They match well during winter when there was uniform snow cover over the SSM/I footprint. During spring, summer, and fall, REBEX brightness temperatures at the grass-site were on average 18 K higher than the SSM/I brightness temperatures because the SSM/I footprint included nearby agricultural fields in summer and predominantly bare soil in fall and spring. During summer, REBEX-4 brightness temperatures at the bare soil site were on average 10 K cooler than the SSM/I brightness temperatures. In effect, the REBEX grass and bare soil brightness temperatures bracket the SSM/I observations with the SSM/I brightness temperatures lying closest to those of the bare soil. Index Terms--Microwave radiometry, radiobrightness, scaling, soil moisture.

We estimate the magnitude of the beam-filling error due to soil-type heterogeneity in the determination of sensor-footprint average soil moisture ([bar][[Theta].sub.f]) retrieved from remote L-band radiometer measurements. Sets of randomly chosen soils are given uniform initial wetness and are subjected to atmospheric drying over 15 days in a numerical model. Results indicate that soil heterogeneity contributes less than 0.7% volumetric soil-moisture error ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]). Index Terms--Microwave radiometry, moisture models, soil coupled heat, soil moisture, spatial heterogeneity.

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.