The determination of leaf chlorophyll content is a common procedure for plant scientists. One might think that procedures should evolve that produce more accurate results with more simple protocols and thereby gain wide acceptance –‘If you build a better mousetrap, the world will beat a path to your door’. Unfortunately, the quest for an improved chlorophyll assessment method is not that simple. Richardson et al. (see pp. 185–194 in this issue), report on a comprehensive assessment of two chlorophyll meters and eight algorithmic analyses of reflectance data.
The ‘gold-standard’ for chlorophyll determination remains extraction of the pigments into a solvent such as acetone or dimenthyl sulphoxide (DMSO), followed by spectrophotometric analysis. Recently, some ‘chlorophyll meters’ have become commercially available that permit scientists rapidly to monitor the chlorophyll content of leaves with a hand held device measuring absorbance of light from two diodes emitting different wavelengths. However, there has been an even greater interest in being able to discern chlorophyll content in leaves using reflectance – the ability accurately to predict the chlorophyll content of a canopy based on the reflectance spectrum would be valuable in the analysis of remote sensing data. Unfortunately, there have been a plethora of channel ratios and algorithms proposed for determining chlorophyll contents from reflectance data, but little consensus on their relative merits.
In the work of Richardson et al., data were taken from a set of 100 paper birch (Betula papyrifera) leaves with chlorophyll contents varying over two orders of magnitude as determined by the spectrophotometric method. The 10 sets of replicated data from each leaf were then subjected to robust statistical analyses to assess the efficacy, as well as the extent and nature of the errors associated with each method.
As might be expected, some of the algorithms for analysis of reflectance data provide a very strong correlation with leaf chlorophyll content, whereas others much poorer correlations and much greater errors. In general, the method having the best correlation with chlorophyll a and total chlorophyll was the Normalized Difference Index (Gitelson & Merzlyak, 1994) based on two reflectance channels, 705 nm and 750 nm. The two chlorophyll meters were ranked in the middle of the pack, limited by their use of 650 nm light that is strongly absorbed by chlorophyll, causing increasing error with higher chlorophyll concentrations within the range normally encountered in leaves. The major unresolved aspect from this work is whether the reflectance methods found to be highly correlated with chlorophyll in birch leaves will be equally reliable with leaves from different species with different thickness and anatomical design. This is probably less of a concern for the absorbency-based instruments, but remains to be established for the reflectance-based methods.
Surprised to learn that determining chlorophyll content is more complex than you thought? Imagine my surprise in looking up the ‘mousetrap’ quote from Ralph Waldo Emerson: ‘If a man can write a better book, preach a better sermon, or make a better mouse-trap, than his neighbour, though he build his house in the woods, the world will make a beaten path to his door’.