Particle size effects on soil reflectance explained by an analytical radiative transfer model

Morteza Sadeghi, Ebrahim Babaeian, Markus Tuller, Scott B. Jones

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Experimental evidence points to an intimate link between soil reflectance, R, and particle/aggregate diameter, D. Based on this strong correlation, various statistical methods for remote and proximal sensing of soil texture and hydraulic properties have been developed. In this paper, we derive a more fundamental and physically-based analytical radiative transfer model that yields a closed-form functional R(D) relationship for dry soils. Despite several simplifying assumptions, the proposed model shows good agreement with measured spectral reflectance (350–2500 nm) data of six soils covering a broad range of textures, colors, and mineralogies. The proposed S-shaped R(D) function resembles cumulative particle and pore size distributions as well as the soil water characteristic function. These analogies may potentially lead to new avenues for developing novel physical models for extracting important soil properties from remotely sensed reflectance data.

Original languageEnglish (US)
Pages (from-to)375-386
Number of pages12
JournalRemote Sensing of Environment
Volume210
DOIs
StatePublished - Jun 1 2018

Bibliographical note

Funding Information:
The authors gratefully acknowledge funding from National Science Foundation (NSF) grant no. 1521469 . Additional support was provided by the Utah Agricultural Experiment Station ( UTAO1189 ), Utah State University , Logan, Utah 84322-4810 , approved as UAES journal paper no. 9038.

Funding Information:
The authors gratefully acknowledge funding from National Science Foundation (NSF) grant no. 1521469. Additional support was provided by the Utah Agricultural Experiment Station (UTAO1189), Utah State University, Logan, Utah 84322-4810, approved as UAES journal paper no. 9038.

Publisher Copyright:
© 2018 Elsevier Inc.

Keywords

  • Optical remote sensing
  • Physically-based model
  • Soil particle size
  • Spectral reflectance
  • Texture

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