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Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture

Riese, Felix M. 1; Keller, Sina ORCID iD icon 1
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)


In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated soil-moisture (sensor-like) data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusion of hyperspectral and GPR data. In contrast, the combination of simulated, sensor-like soil-moisture values and hyperspectral data achieves the worst regression performance. In conclusion, the estimation of soil moisture with hyperspectral and GPR data engages further investigations.

DOI: 10.1109/WHISPERS.2018.8747076
Zitationen: 7
Zitationen: 6
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2018
Sprache Englisch
Identifikator ISBN: 978-1-7281-1581-8
ISSN: 2158-6276
KITopen-ID: 1000082164
Erschienen in 9th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (Whispers 2018), Amsterdam, NL, September 23-26, 2018
Veranstaltung 9th Workshop on Hyperspectral Image and Signal Processing (2018), Amsterdam, Niederlande, 23.09.2018 – 26.09.2018
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Externe Relationen Siehe auch
Schlagwörter Hyperspectral data; ground penetrating radar; soil moisture; machine learning; regression; simulation
Nachgewiesen in Dimensions
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