研究目的
To study whether it is possible to reduce the uncertainties in the SMOS brightness temperature and to develop a potential postprocessing approach that will improve the data quality and soil moisture retrieval.
研究成果
The proposed two-step regression approach can reduce the uncertainty introduced in the SMOS observations by RFI and aliasing issues. The refined brightness temperatures are more consistent with theoretical expectations and allow for implementation of various soil moisture algorithms. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS.
研究不足
The number of observations is reduced since insufficient data would lead to a poor fit at the edges of SMOS track.
1：Experimental Design and Method Selection:
A two-step regression approach was proposed for the refinement of the SMOS L1c brightness temperature data to improve soil moisture retrieval over land. The first regression step utilizes a quadratic function to determine the value of the first Stokes parameter at zenith angle. This value is then used in the second regression step, in which a mixed objective function is optimized to represent the multiangular feature of microwave emissions observed by SMOS at both H and V polarizations.
2：Sample Selection and Data Sources:
SMOS L1c brightness temperature data were used.
3：List of Experimental Equipment and Materials:
SMOS satellite data, WindSat data, and theoretical radiative transfer models.
4：Experimental Procedures and Operational Workflow:
The approach was tested over several selected DGG nodes to evaluate the performance under various observation conditions. Brightness temperature data at six DGG nodes were extracted and processed.
5：Data Analysis Methods:
The robustness of the two-step regression approach was verified with random simulations of brightness temperatures from the radiative transfer model.
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