Information depth of NIR/SWIR soil reflectance spectroscopy

Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical m...

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Vydáno v:	Remote sensing of environment Ročník 256; s. 112315
Hlavní autoři:	Norouzi, Sarem, Sadeghi, Morteza, Liaghat, Abdolmajid, Tuller, Markus, Jones, Scott B., Ebrahimian, Hamed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Inc 01.04.2021 Elsevier BV
Témata:	Clay soils cost effectiveness environment Information depth Light penetration Light reflection Mathematical models Near infrared radiation Optical properties Optical remote sensing Organic matter Particle size Particle size distribution Reflectance reflectance spectroscopy Remote sensing Remote sensing techniques Sandy soils Sensing techniques Short wave radiation Size distribution Soil properties Soil reflectance spectrum Spectroscopy Spectrum analysis texture Wavelengths Particle size distribution Information depth Soil reflectance spectrum Optical remote sensing
ISSN:	0034-4257, 1879-0704
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Abstract	Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical model that links the particle size distribution and soil reflectance in the near infrared (NIR) and shortwave infrared (SWIR) bands of the electromagnetic spectrum is introduced. The model enables the partitioning of measured reflectance spectra into surface and volume (subsurface) contributions, thereby yielding insights about the soil information depth. The model simulations indicate that the surface reflectance contribution to the total reflectance is significantly higher than the volume reflectance contribution for a broad range of soils that vastly differ in texture, mineralogical composition and organic matter contents. The ratio of volume to total reflectance is higher for sandy soils than for clayey soils, especially at longer optical wavelengths, but the ratio rarely exceeds 15%. Therefore, the light reflection from dry soils is predominantly a surface phenomenon and the information depth in most soils rarely exceeds 1 mm. The results of this study reveal an intimate physical relationship between soil reflectance and the particle size distribution in the NIR/SWIR range, which opens a potential new avenue for retrieval of the particle size distribution from remotely sensed reflectance via a universal process-based approach. •A new model links the particle size distribution and soil NIR/SWIR reflectance.•It partitions reflectance spectra into surface and volume contributions.•It shows that light reflectance from dry soils is dominantly a surface phenomenon.•The volume/total reflectance ratio is higher for coarse than for fine soils.•The model opens new avenues for remote sensing of soil hydraulic properties.
AbstractList	Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical model that links the particle size distribution and soil reflectance in the near infrared (NIR) and shortwave infrared (SWIR) bands of the electromagnetic spectrum is introduced. The model enables the partitioning of measured reflectance spectra into surface and volume (subsurface) contributions, thereby yielding insights about the soil information depth. The model simulations indicate that the surface reflectance contribution to the total reflectance is significantly higher than the volume reflectance contribution for a broad range of soils that vastly differ in texture, mineralogical composition and organic matter contents. The ratio of volume to total reflectance is higher for sandy soils than for clayey soils, especially at longer optical wavelengths, but the ratio rarely exceeds 15%. Therefore, the light reflection from dry soils is predominantly a surface phenomenon and the information depth in most soils rarely exceeds 1 mm. The results of this study reveal an intimate physical relationship between soil reflectance and the particle size distribution in the NIR/SWIR range, which opens a potential new avenue for retrieval of the particle size distribution from remotely sensed reflectance via a universal process-based approach. Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical model that links the particle size distribution and soil reflectance in the near infrared (NIR) and shortwave infrared (SWIR) bands of the electromagnetic spectrum is introduced. The model enables the partitioning of measured reflectance spectra into surface and volume (subsurface) contributions, thereby yielding insights about the soil information depth. The model simulations indicate that the surface reflectance contribution to the total reflectance is significantly higher than the volume reflectance contribution for a broad range of soils that vastly differ in texture, mineralogical composition and organic matter contents. The ratio of volume to total reflectance is higher for sandy soils than for clayey soils, especially at longer optical wavelengths, but the ratio rarely exceeds 15%. Therefore, the light reflection from dry soils is predominantly a surface phenomenon and the information depth in most soils rarely exceeds 1 mm. The results of this study reveal an intimate physical relationship between soil reflectance and the particle size distribution in the NIR/SWIR range, which opens a potential new avenue for retrieval of the particle size distribution from remotely sensed reflectance via a universal process-based approach. •A new model links the particle size distribution and soil NIR/SWIR reflectance.•It partitions reflectance spectra into surface and volume contributions.•It shows that light reflectance from dry soils is dominantly a surface phenomenon.•The volume/total reflectance ratio is higher for coarse than for fine soils.•The model opens new avenues for remote sensing of soil hydraulic properties.
ArticleNumber	112315
Author	Sadeghi, Morteza Jones, Scott B. Liaghat, Abdolmajid Tuller, Markus Norouzi, Sarem Ebrahimian, Hamed
Author_xml	– sequence: 1 givenname: Sarem surname: Norouzi fullname: Norouzi, Sarem organization: Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran – sequence: 2 givenname: Morteza surname: Sadeghi fullname: Sadeghi, Morteza organization: Department of Plants, Soils and Climate, Utah State University, Logan, UT, USA – sequence: 3 givenname: Abdolmajid surname: Liaghat fullname: Liaghat, Abdolmajid organization: Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran – sequence: 4 givenname: Markus surname: Tuller fullname: Tuller, Markus organization: Department of Environmental Science, The University of Arizona, Tucson, AZ, USA – sequence: 5 givenname: Scott B. surname: Jones fullname: Jones, Scott B. organization: Department of Plants, Soils and Climate, Utah State University, Logan, UT, USA – sequence: 6 givenname: Hamed surname: Ebrahimian fullname: Ebrahimian, Hamed email: ebrahimian@ut.ac.ir organization: Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran
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Snippet	Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the...
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SubjectTerms	Clay soils cost effectiveness environment Information depth Light penetration Light reflection Mathematical models Near infrared radiation Optical properties Optical remote sensing Organic matter Particle size Particle size distribution Reflectance reflectance spectroscopy Remote sensing Remote sensing techniques Sandy soils Sensing techniques Short wave radiation Size distribution Soil properties Soil reflectance spectrum Spectroscopy Spectrum analysis texture Wavelengths
Title	Information depth of NIR/SWIR soil reflectance spectroscopy
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