A new spectrogoniophotometer to measure leaf spectral and directional optical properties

Combes, Didier; Bousquet, Laurent; Jacquemoud, Stéphane; Sinoquet, Hervé; Varlet-Grancher, C.; Moya, I.

doi:10.1016/j.rse.2006.12.007

Cited by 77 publications

(35 citation statements)

References 37 publications

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“…An increase in chlorophyll absorption near the red spectral region (645 nm), followed by a sharp decrease beyond, is accompanied by the respective increase and decrease of the contribution of surface reflected radiation to the leaf albedo and consequently its sensitivity to the leaf interior. This behavior is consistent with observed patterns of leaf scattering; that is, the contribution of the leaf surface reflected radiation to the leaf albedo is small when its diffuse component is large, as in the NIR region, and is large when the diffuse component is small, as in the pigment-absorbing blue and red spectral bands (23,26,28). This result suggests that leaf surface properties have an impact on canopy BRF.…”

Section: Spectral Reflectance Corrected For Forest Structure Effect Issupporting

confidence: 87%

“…This portion of reflected radiation is, in general, but not always, partly polarized and exhibits a weak spectral dependency (22). It does not interact with pigments inside the leaf, but depends on the properties of the leaf surface (22)(23)(24)(25)(26)(27), and therefore conveys no information about the constitution of the leaf tissue. Fresnel reflection is the principal cause of light polarization at the leaf surface (22,23).…”

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confidence: 99%

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Hyperspectral remote sensing of foliar nitrogen content

Knyazikhin

Schull

Stenberg³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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434

351

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A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact-it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.radiative effect | spurious regression | plant ecology | carbon cycle

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Section: Spectral Reflectance Corrected For Forest Structure Effect Issupporting

confidence: 87%

mentioning

confidence: 99%

Hyperspectral remote sensing of foliar nitrogen content

Knyazikhin

Schull

Stenberg³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

434

351

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“…In 2007, Nicholas Gayeski measured the bidirectional scattering distribution function (BSDF) of building materials for different angle and wavelength values by digital camera, whereas the complex data processing decreased the precision of the obtained measured data [12]. Combes developed a new spectro-goniophotometer, while the sample holder is rotated by steps of 20°, which limits the measurement angle range [13]. Jonsson presented a method to determine the bidirectional transmittance distribution function (BTDF) using an integrating sphere, while the measurement uncertainty was not analyzed systematically [14].…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement of spectral bidirectional transmittance distribution function on translucent optical materials

Wang

Zheng

2015

Measurement

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“…Meanwhile, a lot of research works also were done in BRDF measurements [6,[11][12][13][14][15][16][17] . However, the research on BRDF modeling and measurement for rough samples mostly focused on the broadband, and the research on the spectral BRDF is still relatively rare.…”

Section: Introductionmentioning

confidence: 99%

Modeling and validation of spectral BRDF on material surface of space target

et al. 2014

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The modeling and the validation methods of the spectral BRDF on the material surface of space target were presented. First, the microscopic characteristics of the space targets' material surface were analyzed based on fiber-optic spectrometer using to measure the direction reflectivity of the typical materials surface. To determine the material surface of space target is isotropic, atomic force microscopy was used to measure the material surface structure of space target and obtain Gaussian distribution model of microscopic surface element height. Then, the spectral BRDF model based on that the characteristics of the material surface were isotropic and the surface micro-facet with the Gaussian distribution which we obtained was constructed. The model characterizes smooth and rough surface well for describing the material surface of the space target appropriately. Finally, a spectral BRDF measurement platform in a laboratory was set up, which contains tungsten halogen lamp lighting system, fiber optic spectrometer detection system and measuring mechanical systems with controlling the entire experimental measurement and collecting measurement data by computers automatically. Yellow thermal control material and solar cell were measured with the spectral BRDF, which showed the relationship between the reflection angle and BRDF values at three wavelengths in 380nm, 550nm, 780nm, and the difference between theoretical model values and the measured data was evaluated by relative RMS error. Data analysis shows that the relative RMS error is less than 6%, which verified the correctness of the spectral BRDF model.

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A new spectrogoniophotometer to measure leaf spectral and directional optical properties

Cited by 77 publications

References 37 publications

Hyperspectral remote sensing of foliar nitrogen content

Hyperspectral remote sensing of foliar nitrogen content

Automatic measurement of spectral bidirectional transmittance distribution function on translucent optical materials

Modeling and validation of spectral BRDF on material surface of space target

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