Multi-Temporal UAV Images and GeoDatabase Used to Estimate Temporal and Spatial Soil Moisture Content
Researcher ORCID Identifier
L. Sebastian Bryson: https://orcid.org/0000-0003-2350-2241
Batmyagmar Dashbold: https://orcid.org/0000-0002-1197-1668
Download Readme file (3 KB)
Download UAV and GLDAS Satellite Data (303 KB)
Download Python Code for Machine Learning (4.4 MB)
Download Co-Registered tiffs (502.0 MB)
Download Cosi-Corr tiffs (1.1 MB)
Download ArcGIS Input Data files (17 KB)
Download SAR Data Input Files (266 KB)
Download UAV and GLDAS Satellite Data in non-proprietary format (10 KB)
Dataset Creation Date
University of Kentucky Libraries
We used small unmanned aerial vehicle (UAV) with optical digital camera to detect a land movement and to extract soil parameters. Using multi-temporal images in Garrard County, Kentucky, we detected land movement on three pairs of images that were captured one month apart. The multi-temporal images and the result of the movement analysis are available in folders. In addition, vertical displacement analysis is carried out using Differential Interferometry technique (DinSAR) to a pair of Synthetic Aperture Radar (SAR) images. Soil moisture data was estimated using linear regression machine learning model, and the python code and table used as training points are available in this page. Our results indicate that using UAV equipped with an optical digital camera, we can estimate land surface movement, and extract soil parameters such as soil moisture data using the technique presented in this research (https://doi.org/10.13023/etd.2021.369).
Digital Object Identifier (DOI)
This dataset is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided that the dataset creators and source are credited and that changes (if any) are clearly indicated.
Please see the Readme file.
Readme file: Text document (.txt)
UAV and GLDAS satellite data: Microsoft Excel file (.xlsx)
Co-registered tiffs: .zip format
Cosi-Corr tiffs: .zip format
ArcGIS input data files: .zip format
SAR data input files: .zip format
Python code: Jupyter notebook (.ipynb)
UAV and GLDAS satellite data in non-proprietary format: .zip format
Readme file: 4 KB
UAV and GLDAS satellite data: 304 KB
Co-registered tiffs: 502 MB
Cosi-Corr tiffs: 1.1 MB
ArcGIS input data files: 17 KB
SAR data input files: 266 KB
Python code: 4.5 MB
UAV and GLDAS satellite data in non-proprietary format: 11 KB
Garrard County, Kentucky
2020 to 2021
Dashbold, B., 2021. Landslide Site Assessment and Characterization Using Remote Sensing Techniques, UKnowledge Theses and Dissertations--Civil Engineering, https://doi.org/10.13023/etd.2021.369.
Land cover data - 2016 National Land Cover Database (NLCD) product suite: https://www.mrlc.gov/
NASA Global Land Data Assimilation System (GLDAS) soil moisture data were accessed and acquired via the NASA Giovanni EarthDATA tool: https://giovanni.gsfc.nasa.gov/giovanni/
Bryson, L.S., Dashbold, M., 2021. Multi-Temporal UAV Images and GeoDatabase used to Estimate Temporal and Spatial Soil Moisture Content: UKnowledge Civil Engineering Research Data, https://doi.org/10.13023/wzxy-w419.