Spectrum Index for Estimating Ground Water Content Using Hyperspectral Information

Quality control considerably affects road stability and operability and is directly linked to the underlying ground compaction. The degree of compaction is largely determined by water content, which is typically measured at the actual construction site. However, conventional methods for measuring wa...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2022-11, Vol.14 (21), p.14318
Main Authors: Lee, Kicheol, Kim, Ki Sung, Park, Jeongjun, Hong, Gigwon
Format: Article
Language:English
Subjects:
Cameras
Compaction
Construction sites
Drones
Energy
Experiments
Groundwater
Highway construction
Imaging systems
Measurement
Measurement methods
Methods
Moisture content
Quality control
Reflectance
Remote sensing
Road construction
Roads & highways
Spectrum analysis
Sustainability
Water content
Water, Underground
Wave reflection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Quality control considerably affects road stability and operability and is directly linked to the underlying ground compaction. The degree of compaction is largely determined by water content, which is typically measured at the actual construction site. However, conventional methods for measuring water content do not capture entire construction sites efficiently. Therefore, this study aimed to apply remote sensing of hyperspectral information to efficiently measure the groundwater content of large areas. A water content prediction equation was developed through an indoor experiment. The experimental samples comprised 0–40% (10% increase) of fine contents added to standard sand. As high water content is not required in road construction, 0–15% (1% increase) of water content was added. The test results were normalized, the internal and external environments were controlled for precise results, and a wavelength–reflection curve was derived for each test case. Data variability analyses were performed, and the appropriate wavelength for water content reflection, as well as reflectance, was determined and converted into a spectrum index. Finally, various fitting models were applied to the corresponding spectrum index for water content prediction. Reliable results were obtained with the reflectance corresponding to a wavelength of 720 nm applied as the spectrum index.
ISSN:2071-1050
2071-1050
DOI:10.3390/su142114318