Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Deep Learning in Spaceborne GNSS Reflectometry: Correcting Precipitation Effects on Wind Speed Products

Xiao, T., Arnold, C., Zhao, D., Mou, L., Wickert, J., Asgarimehr, M. (2024 online): Deep Learning in Spaceborne GNSS Reflectometry: Correcting Precipitation Effects on Wind Speed Products. - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
https://doi.org/10.1109/JSTARS.2024.3453999

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Xiao, Tianqi1, 2, Autor              
Arnold, Caroline3, Autor
Zhao, Daixin3, Autor
Mou, Lichao3, Autor
Wickert, J.1, Autor              
Asgarimehr, Milad1, Autor              
Affiliations:
11.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146025              
2Submitting Corresponding Author, Deutsches GeoForschungsZentrum, ou_5026390              
3External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Deep learning techniques have shown the capability in GNSS reflectometry (GNSS-R) for retrieving geographical parameters based on GNSS-R observations. Recent studies have proved that such data-driven approaches can significantly improve the quality of ocean surface wind speed products retrieved from Delay-Doppler Maps (DDMs). However, based on the theoretical knowledge, several known error sources are associated with bias in the deep learning model estimations. Rain splashing on the ocean affects the surface roughness of the ocean, altering the scattering pattern of GNSS signals and consequently bringing in considerable bias in wind speed estimations. Correction of such bias is challenging because of its nonlinear dependency on different environmental and technical parameters. Deep learning has the potential to learn such trends from corresponding environmental parameters and correct the associated biases. Therefore, we investigate how deep learning-based data fusion using precipitation data can correct the rain effect and improve wind speed estimations. Our proposed fusion model outperforms both the baseline model and the operational Minimum Variance Estimator (MVE) method on unseen dataset. The root mean square error (RMSE) of our fusion model is 3.3% better than the baseline model and 30% better than the MVE method. For samples affected by rain, our fusion model also shows superior performance compared to the baseline model. Specifically, the retrieval RMSE of the fusion model is improved by 1.9% overall, with a 3.6% improvement in the low wind speed range ( < 4 m/s) and a 17% improvement in the high wind speed range ( > 16 m/s).

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2024-09-03
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1109/JSTARS.2024.3453999
OATYPE: Gold Open Access
GFZPOF: p4 T1 Atmosphere
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Genre der Quelle: Zeitschrift, SCI, Scopus, oa ab 2020
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals213
Publisher: Institute of Electrical and Electronics Engineers (IEEE)