Hyperspectral Soilmoisture Dataset

Hyperspectral benchmark dataset on soil moisture.
https://github.com/felixriese/hyperspectral-soilmoisture-dataset

Category: Natural Resources
Sub Category: Soil and Land

Keywords

benchmark dataset field-campaigns hyperspectral soil-moisture

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Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017.

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Hyperspectral benchmark dataset on soil moisture

Hyperspectral and soil-moisture data from a lysimeter field campaign based on a soil sample. Karlsruhe (Germany), 2017.

Abbreviation: KarLy (Karlsruhe Lysimeter)

License: CC BY 4.0

Authors:

Affiliation: Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing (Link)

Citation: see Citation and bibliography.bib.

Example script: example.ipynb

Description

This dataset was measured in a five-day field campaign in May 2017 in Karlsruhe, Germany. An undisturbed soil sample is the centerpiece of the measurement setup. The soil sample consists of bare soil without any vegetation and was taken in the area near Waldbronn, Germany.

The following sensors were deployed:

  • Cubert UHD 285 hyperspectral snapshot camera recording 50 by 50 images with 125 spectral bands ranging from 450 nm to 950 nm and a spectral resolution of 4 nm.
  • TRIME-PICO time-domain reflectometry (TDR) sensor in a depth of 2 cm measuring the soil moisture in percent.

The raw sensor data was processed with the Hyperspectral Processing Scripts for the HydReSGeo Dataset beforehand.

Variables

  • datetime: date and time (CEST) of the measurement
  • soil_moisture: soil moisture in %
  • soil_temperature: soil temperature in °C
  • 454, 458, … 946, 950: hyperspectral bands in nm

Citation

The bibtex file including both references is available in bibliography.bib.

Paper

Felix M. Riese and Sina Keller, “Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 6151-6154. (Link)

@inproceedings{riese2018introducing,
    author = {Riese, Felix~M. and Keller, Sina},
    title = {{Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data}},
    booktitle = {IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium},
    year = {2018},
    month = {July},
    address = {Valencia, Spain},
    doi = {10.1109/IGARSS.2018.8517812},
    ISSN = {2153-7003},
    pages = {6151--6154},
}

Code

Felix M. Riese and Sina Keller, "Hyperspectral benchmark dataset on soil moisture", Dataset, Zenodo, 2018. (Link)

@misc{riesekeller2018,
    author = {Riese, Felix~M. and Keller, Sina},
    title = {Hyperspectral benchmark dataset on soil moisture},
    year = {2018},
    DOI = {10.5281/zenodo.1227837},
    publisher = {Zenodo},
    howpublished = {\href{https://doi.org/10.5281/zenodo.1227837}{doi.org/10.5281/zenodo.1227837}}
}

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite both the article from preferred-citation and the software itself."
authors:
  - family-names: Riese
    given-names: Felix M.
    orcid: https://orcid.org/0000-0003-0596-9585
  - family-names: Keller
    given-names: Sina
    orcid: https://orcid.org/0000-0002-7710-5316
title: "Hyperspectral benchmark dataset on soil moisture"
version: 1.0.3
doi: "10.5281/zenodo.1227837"
date-released: 2019-01-03
repository-code: https://github.com/felixriese/hyperspectral-soilmoisture-dataset
license: BSD-3-Clause
preferred-citation:
  authors:
    - family-names: Riese
      given-names: Felix M.
    - family-names: Keller
      given-names: Sina
  title: "Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data"
  collection-title: "IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium"
  collection-type: proceedings
  conference:
    name: "IGARSS 2018"
  year: 2018
  doi: "10.1109/IGARSS.2018.8517812"
  start: 6151
  end: 6154

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Dependencies

environment.yml conda
  • matplotlib
  • numpy
  • pandas
  • scikit-learn

Score: 4.543294782270004