Publication Date:
2013-09-07
Description:
After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures $(T_{B})$ is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the mission's success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the $T_{B}$ images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the $T_{B}$ incidence angle dependence, is subtracted from the mean $T_{B}$ images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms.
Print ISSN:
0196-2892
Electronic ISSN:
1558-0644
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
