Can Hydraulic Conductivity of Fluvial Sediments be Informed by Spectral Reflectance?

Li Yao Li, Can Liu, Gengxin Ou, Zhaowei Wang, Jesse Korus, Ran Jiang

Research output: Contribution to journalArticlepeer-review


Abstract: This study explores the statistical relationship between spectral reflectance and hydraulic conductivity (K) of fluvial sediments in two Nebraska rivers. The spectral reflectance curves of sediments are obtained through hyperspectral instruments under controlled conditions. The K values are determined by three different methodologies, grain size analysis, an in-situ permeameter test, and a lab permeameter test. The in-situ permeameter tests calculate vertical K values (Kv), whereas grain size analysis and lab tests and grain size analysis generate non-directional K values. The results show that the lab permeameter tests of repacked sediments present greater hydraulic conductivity values than in-situ tests. The non-directional K values derived from 7 empirical equations, Hazen, Slicher, Terzaghi, Beyer, USBR, Kozeny, and Sauerbrei, correlate well with the in-situ Kv values. Site specific coefficients in 7 equations are developed for the study sites. Correlation analysis is conducted aiming to establish the connection between hydraulic conductivity and spectral reflectance. Inverse trends are found between the reflectance and K values determined by Hazen, Beyer, USBR, and Sauerbrei formulae where particle size distribution is considered to be a key factor. Furthermore, four linear models are developed based on the relationship between grain size derived K and reflectance. The models are used on dried surface channel sediments in the Platte River for predicting K values as a pilot test and proved to be applicable. As direct measurement of hydraulic conductivity can be costly and time-consuming, remote sensing informed hydraulic conductivity of streambed sediments in droughts can be a promising application with further study.

Original languageEnglish (US)
Pages (from-to)846-854
Number of pages9
JournalWater Resources
Issue number5
StatePublished - Sep 1 2020


  • hydraulic conductivity
  • linear regression
  • particle size distribution
  • spectral reflectance

ASJC Scopus subject areas

  • Water Science and Technology


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