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United States Department of Agriculture

Agricultural Research Service

Title: Remote sensing chlorophyll content of leaves and canopies using red, green and blue wavebands

item Hunt, Earle - Ray
item Daughtry, Craig
item Eitel, Jan
item Long, Daniel - Dan

Submitted to: American Society of Agronomy
Publication Type: Abstract Only
Publication Acceptance Date: 8/3/2009
Publication Date: 11/1/2009
Citation: Hunt, E.R., Daughtry, C.S., Eitel, J.U., Long, D.S. 2009. Remote sensing chlorophyll content of leaves and canopies using red, green and blue wavebands [abstract]. American Society of Agronomy. 2009 CDROM.

Interpretive Summary:

Technical Abstract: Chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to crop nitrogen content, allowing determination of fertilizer requirements over large areas. There are numerous indices of chlorophyll content from hyperspectral sensors, from ground based to satellite based; however, sensors based on digital cameras with broad spectral bands of blue, green and red wavelengths may provide an inexpensive alternative. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three points: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the center wavelength and R is the reflectance of the red (r), green (g), and blue (b) wavebands. Based on determinants, one formula is TGI = -0.5[('r – 'b)(Rr – Rg) – ('r – 'g)(Rr – Rb)]; other formulae can be derived using a different order of points. TGI was moderately correlated with chlorophyll content using a variety of leaf and plot hyperspectral reflectance datasets; indices using the red-edge (700-730 nm) had higher correlations. When the data were averaged into broad overlapping wavebands, TGI had higher correlations than other indices. Simulations using a canopy reflectance model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, which can be overcome using very high spatial resolution. TGI may be able to provide timely, useful data for crop nitrogen management and can be acquired by inexpensive digital cameras placed on ground platforms, small aircraft or unmanned airborne systems.

Last Modified: 06/24/2017
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