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Fusion of hyperspectral and ground penetrating radar to estimate soil moisture [in press]

Riese, Felix M.; Keller, Sina

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.

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator 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
Verlag IEEE
Bemerkung zur Veröffentlichung
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