Hyperspectral Processing Scripts for the HydReSGeo Dataset

This repository includes the processing scripts of the HydReSGeo dataset for the hyperspectral, LWIR, and soil moisture data.

License

3-Clause BSD license

Author

Felix M. Riese

Requirements

Python 3 with these packages

Citation

see Citation and in the bibtex file

Documentation

Documentation

Sensors

• Hyperspectral sensors: Cubert UHD 285 (VNIR), FLIR Tau2 640 (LWIR), ASD FieldSpec 4 Sensors (VNIR & SWIR)
• Hydrological sensor: IMKO Pico32 (TDR)

Exemplary notebooks

• Example_Plots.ipynb
• Process_HydReSGeo_Dataset.ipynb

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Citation

Code:

[1] F. M. Riese, "Hyperspectral Processing Scripts for HydReSGeo Dataset," Zenodo, 2020. DOI:10.5281/zenodo.3706418

@misc{riese2020hyperspectral,
    author = {Riese, Felix~M.},
    title = {{Hyperspectral Processing Scripts for the HydReSGeo Dataset}},
    year = {2020},
    DOI = {10.5281/zenodo.3706418},
    publisher = {Zenodo},
    howpublished = {\href{https://doi.org/10.5281/zenodo.3706418}{doi.org/10.5281/zenodo.3706418}}
}

Dataset:

[2] S. Keller, F. M. Riese, N. Allroggen, and C. Jackisch, "HydReSGeo: Field experiment dataset of surface-subsurface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques," GFZ Data Services, 2020. DOI:10.5880/fidgeo.2020.015

@misc{keller2020hydresgeo,
    author = {Keller, Sina and Riese, Felix~M. and Allroggen, Niklas and
              Jackisch, Conrad},
    title = {{HydReSGeo: Field experiment dataset of surface-subsurface
              infiltration dynamics acquired by hydrological, remote
              sensing, and geophysical measurement techniques}},
    year = {2020},
    publisher = {GFZ Data Services},
    DOI = {10.5880/fidgeo.2020.015},
}

Code is Supplementary Material to

[3] S. Keller, F. M. Riese, N. Allroggen, C. Jackisch, and S. Hinz, “Modeling subsurface soil moisture based on hyperspectral data: First results of a multilateral field campaign,” in Tagungsband der 37. Wissenschaftlich-Technische Jahrestagung der DGPF e.V., vol. 27, Munich, Germany, 2018, pp. 34–48. Link

[4] S. Keller, F. M. Riese, J. Stötzer, P. M. Maier, and S. Hinz, “Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-1, pp. 101–108, 2018. DOI:10.5194/isprs-annals-IV-1-101-2018

[5] F. M. Riese and S. Keller, “Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture,” in 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, 2018, pp. 1–5. DOI:10.1109/WHISPERS.2018.8747076

[6] S. Keller, Fusion hyperspektraler, LWIR- und Bodenradar-Daten mit maschinellen Lernverfahren zur Bodenfeuchteschätzung, 5th ed. Wichmann, Berlin, 2019, p. 217–250.

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To do:

• [ ] Include plots with masks and bars into the documentation
• [ ] Speed-up the script by opening dataframes only once
• [ ] Describe data from rs/hyp/ and rs/lwir/ in the documentation