An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010). Peng, Z., Zhao, T., Shi, J., Kerr, Y. H., Rodríguez-Fernández, N. J., Yao, P., & Che, T. Scientific Data, 10(1):599, September, 2023. ![An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.
@article{peng_rfi-suppressed_2023,
title = {An {RFI}-suppressed {SMOS} {L}-band multi-angular brightness temperature dataset spanning over a decade (since 2010)},
volume = {10},
issn = {2052-4463},
url = {https://www.nature.com/articles/s41597-023-02499-z},
doi = {10.1038/s41597-023-02499-z},
abstract = {Abstract
The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with
in-situ
measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.},
language = {en},
number = {1},
urldate = {2024-11-15},
journal = {Scientific Data},
author = {Peng, Zhiqing and Zhao, Tianjie and Shi, Jiancheng and Kerr, Yann H. and Rodríguez-Fernández, Nemesio J. and Yao, Panpan and Che, Tao},
month = sep,
year = {2023},
pages = {599},
}
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