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Surface temperature is a key component for understanding energy fluxes between the Earth's surface and atmosphere. Accurate retrieval of surface temperature from satellite observations requires proper correction of the thermal channels for atmospheric emission and attenuation. Although the split-window method has offered relatively accurate measurements, this empirical approach requires in situ data and will only perform well if the in situ data are from the same surface type and similar climatology. Single channel correction reduces uncertainty inherent to the split-window method, but requires an accurate radiative transfer model and description of the atmospheric profile. Unfortunately, this method is impractical for operational correction of satellite retrievals due to the size of data sets and computation time required by radiative transfer modeling. We present a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to Moderate Resolution Imaging Spectrometer (MODIS) channels. Comparison with MODTRAN showed a good performance for the parametric model and computation speeds approximately three orders of magnitude faster. Sea surface temperature (SST) calculated using atmospheric correction parameters generated from our model showed consistent results (rmse = 0.49 K) and small bias (-0.45 K) with the MODIS SST product (MYD28). Validation of surface temperatures derived using our model with in situ land and water temperature measurements exhibited accuracy (mean bias < 0.35 K) and low error (rmse < 1 K) for MODIS bands 31 and 32. Finally, an investigation of profile sources and their effect on atmospheric correction offered insight into the application of the parametric model for operational correction of MODIS thermal bands. Index Terms--Parametric modeling, remote sensing, temperature.

The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15 [degrees]C. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 m. [s.sup.-1], the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. For this reason, the use of wind components, which typically exhibit Gaussian error distributions, may be preferred in the retrieval. However, the use of noisy wind components creates WS and SSS biases at low WSs (0.1 psu at 3 m [s.sup.-l]). At a sea surface temperature (SST) of 15 [degrees]C, the retrieved SSS is weakly sensitive to the SST biases, with the SSS bias always lower than 0.3 psu for SST biases ranging from --0.5 [degrees]C to --2 [degrees]C. In DP mode, biases in the vertical total electron content (TEC) of the atmosphere result in SSS biases smaller than 0.2 psu. The pseudofirst Stokes mode is insensitive to TEC. Failure to fully account for sea surface roughness scattering effects in the computation of sky radiation contribution leads to a maximum SSS bias of 0.2 psu in the selected configuration, i.e., a descending orbit over the Northern Pacific in February. To achieve SSS biases that are smaller than 0.2 psu, special care must be taken to correct for biases at low WS and to ensure that the bias on the mean WS (averaged over 200 km x 200 km and ten days) remains smaller than 0.5 m [s.sup.-1]. Index Terms--Microwave radiometry, oceanography, salinity.