Soil moisture images for the Southern Great Plains Hydrology Experiment (SGP99) were derived from airborne passive microwave measurements obtained from the electronically scanned thinned array radiometer (ESTAR) and the polarimetric scanning radiometer (PSR) at L-band and C-band, respectively. In order to mimic operational products, a robust retrieval method is chosen and the corrections for vegetation and surface roughness are based on average values from the literature. Validation against field measurements results in root-mean-square errors and biases of 2.9% and 4.2% in volumetric soil moisture for the ESTAR data set and 4.6% and 6.0% for the PSR product. For this sparsely vegetated experiment region, the quality of the C-band derived soil moisture is therefore comparable to the corresponding L-band product. The accuracies of the operational data sets, namely, the European Centre for Medium-Range Weather Forecasts (ECMWF) 40 year reanalysis product (ERA40) and the European Remote Sensing Satellite (ERS) scatterometer derived soil moisture, are quantified based on the high-resolution PSR soil moisture images for the SGP99 region. The ERA40 reanalysis comprises soil moisture data for four soil layers at the T159 spectral resolution. The top 7 cm layer soil moisture product is in reasonable agreement with the C-band derived soil moisture data sets. Temporal and spatial patterns are well represented, but a significant wet bias of up to 14.4% (ERA40) is present in dry conditions. In order to evaluate the quality of the operational data sets on a longer temporal timescale, in situ soil moisture measurements at the Little Washita National Oceanic and Atmospheric Administration/Atmospheric Turbulence and Diffusion Division (NOAA/ATDD) site are used to analyze time series for June 1997 to December 1998. The temporal evolution of soil moisture is captured reasonably well by the ERA40 product and the ERS scatterometer derived surface soil moisture data set. RMS errors were found to be 5.6% and 5.7%, respectively. The study shows that passive microwave remote sensing has the potential to improve operational products.