Spectral Reflectance
Spectral Reflectance - Related Videos
13:59
Reflection and Refraction
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2023
Source: Derek Wilson, Asantha Cooray, PhD, Department of Physics & Astronomy, School of Physical Sciences, University of California, Irvine, CA Light travels at different speeds depending on the material through which it is propagating. When light travels from one material to another, it will either slow down or speed up. In order to conserve energy and momentum, the light must change the direction in which it propagates. This bending of light is known as refraction. Some fraction of the...
04:16
Using Spectral Reflectometry to Determine Myelinated Axon Diameters in a Fixed Mouse Brain Slice
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2025
Source: Kwon, J., et al., Spectral Reflectometric Microscopy on Myelinated Axons In Situ. J. Vis. Exp. (2018) This video demonstrates the use of spectral reflectometry with a hyperspectral confocal microscope to analyze interference patterns and determine myelinated axon diameters in fixed brain tissue slices.
11:21
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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Cited by 24 •
2013
Photoacoustic ophthalmology (PAOM), an optical-absorption-based imaging modality, provides the complementary evaluation of the retina to the currently available ophthalmic imaging technologies. We report the using of PAOM integrated with spectral-domain optical coherence tomography (SD-OCT) for simultaneous multimodal retinal imaging in rats.
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Cited by 30 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
11:37
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
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Cited by 36 •
2017
An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.
FAQs
Pigments shape spectral reflectance by absorbing particular wavelengths rather than reflecting them equally. This selective absorption creates wavelength-specific differences that can be associated with photosynthetic pigments and other biological traits. Detectors capture these differences across the spectrum, allowing researchers to connect a measured pattern with organism composition or condition without relying solely on visible appearance.
Scattering from cellular surfaces, tissues, and other biological structures changes how much radiation returns to a detector at each wavelength. Unlike pigment absorption, this contribution reflects physical organization as well as composition. Consequently, two materials with different structures may produce different signatures even when their pigment content is similar, supporting tissue and surface characterization.
A useful signature is interpreted by comparing wavelength-dependent differences with biological traits, rather than treating one reflectance value as a complete assessment. The relationship can reveal patterns linked to plant health, photosynthetic pigments, water stress, vegetation cover, animal coloration, or tissue properties. This makes spectral reflectance informative for both individual organisms and ecosystems.
Measurements begin when a detector records reflected radiation across wavelengths from a biological material or scene. The resulting wavelength pattern can be collected with laboratory instruments, imaging systems, or remote-sensing platforms. Researchers then use differences in the recorded signal to assess relevant traits or conditions, such as pigment status, water stress, tissue properties, or vegetation cover.
For plants, spectral reflectance supports noninvasive assessment of health, photosynthetic pigments, water stress, and vegetation cover. Its value comes from linking measured wavelength patterns with these biological attributes rather than removing or sampling the tissue. Imaging and remote-sensing platforms can extend observations beyond a single specimen, making the approach relevant to vegetation studies.
Beyond vegetation, measurements can investigate animal coloration and tissue properties, showing that the same wavelength-based approach applies to different biological materials. Reflectance patterns may be examined as characteristic signatures of surface or tissue differences. This broadens its use from organism-level studies to ecosystem monitoring when vegetation cover is also evaluated.