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
Last synced: about 3 hours ago
JSON representation
Repository metadata
Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017.
- Host: GitHub
- URL: https://github.com/felixriese/hyperspectral-soilmoisture-dataset
- Owner: felixriese
- License: cc-by-4.0
- Created: 2018-04-24T07:52:04.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-09-15T20:07:46.000Z (over 3 years ago)
- Last Synced: 2025-04-27T13:02:02.898Z (3 days ago)
- Topics: benchmark, dataset, field-campaigns, hyperspectral, soil-moisture
- Language: Jupyter Notebook
- Homepage: https://doi.org/10.5281/zenodo.1227837
- Size: 737 KB
- Stars: 47
- Watchers: 2
- Forks: 13
- Open Issues: 0
- Releases: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
README.md
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
Owner metadata
- Name: Dr. Felix Riese
- Login: felixriese
- Email:
- Kind: user
- Description: Ph.D. & MBA | Head of Product | Physicist with 9+ Years in Data Science and Machine Learning | First-Principles Thinking
- Website: felixriese.de
- Location: Munich, Germany
- Twitter:
- Company: @Peter-Park-Systems-GmbH
- Icon url: https://avatars.githubusercontent.com/u/17081863?u=4ceafc223f4a4b639ac9518491af4df0b8485df7&v=4
- Repositories: 17
- Last ynced at: 2024-06-11T15:39:01.250Z
- Profile URL: https://github.com/felixriese
GitHub Events
Total
- Watch event: 3
- Fork event: 1
Last Year
- Watch event: 3
- Fork event: 1
Committers metadata
Last synced: 8 days ago
Total Commits: 17
Total Committers: 2
Avg Commits per committer: 8.5
Development Distribution Score (DDS): 0.412
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
Felix M. Riese | f****e@k****u | 10 |
Felix M. Riese | m****l@f****e | 7 |
Committer domains:
- felixriese.de: 1
- kit.edu: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
Top Pull Request Authors
Top Issue Labels
Top Pull Request Labels
Dependencies
- matplotlib
- numpy
- pandas
- scikit-learn
Score: 4.543294782270004