Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

Kicheol Lee, Jeong Jun Park () and Gigwon Hong ()
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Kicheol Lee: Corporate Affiliated Research Institute, UCI Tech, 313, Inha-ro, Michuhol-gu, Incheon 22012, Korea
Jeong Jun Park: Incheon Disaster Prevention Research Center, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea
Gigwon Hong: Department of Civil Engineering, Halla University, 28 Halladae-gil, Wonju-si 26404, Korea

Sustainability, 2022, vol. 14, issue 17, 1-15

Abstract: With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model.

Keywords: water content measurement; automation; hyperspectral information (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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