Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #261345

Title: A visible band index for remote sensing leaf chlorophyll content at the canopy scale

item Hunt Jr, Earle
item Doraiswamy, Paul
item McMurtrey Iii, James
item Daughtry, Craig
item PERRY, EILEEN - Department Of Primary Industries
item AKHMEDOV, BAKHYT - Science Systems, Inc

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/27/2012
Publication Date: 4/21/2013
Citation: Hunt, E.R., Doraiswamy, P.C., McMurtrey III, J.E., Daughtry, C.S., Perry, E.M., Akhmedov, B. 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation. 21:102-112.

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 chlorophyll content 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 airborne hyperspectral remote sensing data acquired during a nitrogen application experiment with irrigated corn in Nebraska. Nitrogen fertilization treatments were 12%, 40%, 70%, 100% and 130% of the optimum N amount based on yield goals. A variety of vegetation and chlorophyll indices were tested, and the Triangular Greenness Index was consistently among the better indices. When the blue, green and red bands were averaged to match the wavelengths recorded by a digital camera, TGI was not affected but other indices were. More work remains to be done, but TGI is a promising index for nitrogen management.

Technical Abstract: Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll: TGI = -0.5((670 - 480)(R670 – R550) – (670-550)(R670 – R480)) , where 670, 550 and 480 are the wavelengths (nm) and R670, R550 and R480 are the reflectances at those wavelengths, respectively. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated corn in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper data were acquired along with leaf chlorophyll meter and other data. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Thematic Mapper, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at LAI > 2, TGI was only affected by leaf chlorophyll content. Estimates of chlorophyll content were slightly better using narrow bands of AVRIS and indices based on chlorophyll absorption features. However, TGI may be a good index for crop nitrogen management where high-spatial-resolution, low-cost sensors obtain data for pure leaf pixels earlier in the growing season.