Literature DB >> 20168666

Asymptotic nature of grass canopy spectral reflectance.

C J Tucker.   

Abstract

The asymptotic nature of grass canopy spectral reflectance has been evaluated from field experimental data collected over the wavelength region of 0.500-1.000 microm at 0.005-microm intervals. The spectral reflectance of green vegetation against a soil background decreases in regions of absorption and increases in regions of minimal or no absorption as the vegetational density increases until a stable or unchanging spectral reflectance, called the asymptotic spectral reflectance, is reached. Results indicated spectral reflectance asymptotes occurred at significantly lower levels of total wet biomass, total dry biomass, dry green biomass, chlorophyll content, and leaf water content in regions of strong pigment absorption (low detectability threshold) than in the photographic ir region where absorption was at a minimum (high detectability threshold). These findings suggested that photographic ir sensors were more suited to remote sensing of moderate to high biomass levels or vegetational density in a grass canopy than were sensors operating in regions of the spectrum where strong absorption occurred.

Entities:  

Year:  1977        PMID: 20168666     DOI: 10.1364/AO.16.001151

Source DB:  PubMed          Journal:  Appl Opt        ISSN: 1559-128X            Impact factor:   1.980


  3 in total

1.  Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation.

Authors:  Gustavo Togeiro de Alckmin; Lammert Kooistra; Richard Rawnsley; Sytze de Bruin; Arko Lucieer
Journal:  Sensors (Basel)       Date:  2020-12-15       Impact factor: 3.576

2.  Aerial high-throughput phenotyping of peanut leaf area index and lateral growth.

Authors:  Sayantan Sarkar; Alexandre-Brice Cazenave; Joseph Oakes; David McCall; Wade Thomason; Lynn Abbott; Maria Balota
Journal:  Sci Rep       Date:  2021-11-04       Impact factor: 4.379

3.  New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass.

Authors:  Loris Vescovo; Georg Wohlfahrt; Manuela Balzarolo; Sebastian Pilloni; Matteo Sottocornola; Mirco Rodeghiero; Damiano Gianelle
Journal:  Int J Remote Sens       Date:  2012-04-10       Impact factor: 3.151

  3 in total

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