Zhao, Daixin und Heidler, Konrad und Asgarimehr, Milad und Albrecht, Conrad M und Wickert, Jens und Zhu, Xiao Xiang und Mou, Lichao (2025) Multimodal GNSS-R self-supervised learning as a generalist Earth surface monitor. International Journal of Applied Earth Observation and Geoinformation, 142, Seite 104658. Elsevier. doi: 10.1016/j.jag.2025.104658. ISSN 1569-8432.
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Kurzfassung
The increasing frequency of climate extremes and natural disasters demands rapid and scalable Earth surface scans for effective action. Emerging as a novel remote sensing technique, spaceborne global navigation satellite system reflectometry (GNSS-R) plays an increasingly vital role in monitoring Earth’s surface parameters. Recent studies leverage the growing volume of GNSS-R measurements with data-driven approaches to enhance retrieval products over both ocean and land. Yet, these models are typically trained using supervised learning, which requires extensive feature engineering and application-specific annotations. To address these limitations, we propose the first GNSS-R self-supervised learning framework as a generalist Earth surface monitor (GEM). Our model is pretrained on multimodal observables, i.e., delay-Doppler maps (DDMs) and auxiliary parametric data, to learn cross-modal representations from GNSS-R data. To validate the effectiveness of the proposed approach, we fine-tune the pretrained model on various downstream retrieval tasks, including ocean wind speed retrieval, surface soil moisture estimation, and vegetation water content prediction. The results demonstrate that our framework generalizes well across these tasks, providing a versatile solution for GNSS-R-based Earth surface monitoring and facilitating further exploration of novel use cases.
elib-URL des Eintrags: | https://elib.dlr.de/214762/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | Multimodal GNSS-R self-supervised learning as a generalist Earth surface monitor | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | 2025 | ||||||||||||||||||||||||||||||||
Erschienen in: | International Journal of Applied Earth Observation and Geoinformation | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 142 | ||||||||||||||||||||||||||||||||
DOI: | 10.1016/j.jag.2025.104658 | ||||||||||||||||||||||||||||||||
Seitenbereich: | Seite 104658 | ||||||||||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||||||||||
ISSN: | 1569-8432 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | climate action, CYGNSS, Earth observation, Foundation model, GNSS reflectometry, Self-supervised learning | ||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz, R - Optische Fernerkundung, R - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Albrecht, Conrad M | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 15 Jul 2025 12:27 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 06 Aug 2025 11:54 |
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