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Title: Remote Sensing of Chlorophyll Content at Leaf and Canopy Scales using a Visible Band Index

item Hunt Jr, Earle
item Daughtry, Craig
item Long, Daniel
item EITEL, JAN - University Of Idaho

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/11/2011
Publication Date: 5/2/2011
Citation: Hunt, E.R., Daughtry, C.S., Long, D.S., Eitel, J.U. 2011. Remote sensing of chlorophyll content at leaf and canopy scales using a visible band index. Agronomy Journal. 103:1090-1099.

Interpretive Summary: Digital cameras can detect color differences resulting from differences in leaf chlorophyll content; therefore, they may be a useful and low-cost sensor for agricultural nutrient management. The problem addressed in this study is how to quantify changes in leaf color without the index being sensitive to canopy variables such as leaf area index and soil background color. We developed the Triangular Greenness Index, based on the area of a triangle connecting red, green and blue reflectances. We tested the index using computer simulations of leaf and canopy reflectance and data for corn and wheat. There are better indices available from high-cost hyperspectral sensors; however, the Triangular Greenness Index was consistently the best index that uses only red, green and blue bands. Digital camera images acquired with very high spatial resolution can also be used to detect weeds and crop diseases, so that aerial photographs for nutrient management can be used for multiple purposes.

Technical Abstract: Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing.