A linear physically-based model for remote sensing of soil moisture using short wave infrared bands

Morteza Sadeghi, Scott B. Jones, William D. Philpot

Research output: Contribution to journalArticlepeer-review

172 Scopus citations


Technological advances in satellite remote sensing have offered a variety of techniques for estimating surface soil water content as a key variable in numerous environmental studies. Optical methods are particularly valuable for remote sensing of soil moisture since reflected solar radiation is the strongest passive signal available to satellites and thus observations at optical wavelengths are capable of providing high spatial resolution data. Since remote sensors do not measure soil water content directly, mathematical algorithms that describe the connection between the measured signal and surface water content must be derived. Here, we present a physically-based soil moisture retrieval model in the solar domain (350-2500. nm) that is based on the Kubelka-Munk two-flux radiative transfer theory. The model is designed to describe diffuse reflectance from a uniform, optically thick, absorbing and scattering medium. The theory suggests a linear relationship between a transformed reflectance and soil water content in the short wave infrared bands (e.g. band 7 of Landsat and MODIS satellites) providing an easy-to-use algorithm in these bands. Accuracy of this model was tested and preliminarily verified using laboratory-measured spectral reflectance data of different soils. Further studies on potentials and challenges of this model for large-scale application using optical satellites data remain a topic of ongoing research.

Original languageEnglish (US)
Pages (from-to)66-76
Number of pages11
JournalRemote Sensing of Environment
StatePublished - Jul 1 2015
Externally publishedYes

Bibliographical note

Funding Information:
Financial support was provided by Utah State University Office of Research and Graduate Studies Research Catalyst (RC) Grant and by the Utah Agricultural Experiment Station , Utah State University, Logan, Utah 84322-4810 , approved as journal paper no. 8746. We also express our appreciation to Michael Whiting and David Lobell for sharing their valuable data.

Publisher Copyright:
© 2015 Elsevier Inc.

Copyright 2015 Elsevier B.V., All rights reserved.


  • Absorption
  • Reflectance
  • Remote sensing
  • Scattering
  • Soil moisture


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