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Title: Estimating dry matter content from spectral reflectances for green leaves of different species

Author
item WANG, LINGLI - George Mason University
item QU, JOHN - George Mason University
item HAO, XIANJUN - George Mason University
item Hunt Jr, Earle

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2010
Publication Date: 11/20/2011
Citation: Wang, L., Qu, J.J., Hao, X., Hunt, E.R. 2011. Estimating dry matter content from spectral reflectances for green leaves of different species. International Journal of Remote Sensing. 32(22):7097-7109.

Interpretive Summary: The reflectance spectrum of green fresh leaves from 400 to 2500 nm wavelength is primarily determined by chlorophyll, water, and leaf thickness. The contribution of dry matter to the reflectance spectrum is very small, but for applications such as determining the forest fire fuel load, it is critically important. We collected fresh leaves from a variety of trees, crops and grasses and measured the reflectance spectrum and various biophysical attributes. We also conducted a large number of simulations with the PROSPECT leaf reflectance model. We determined that a vegetation index using reflectances at 1533 and 2264 nm is correlated with leaf dry matter content. More research is required to test this index at the canopy scale.

Technical Abstract: Efficient and accurate detection of the temporal dynamics and spatial variations of leaf dry matter content would help monitor key properties and processes in vegetation and the wider ecosystem. However, leaf water content strongly absorbs at shortwave infrared wavelengths reducing the signal from dry matter. The major objective of this study was to examine relationship between spectral reflectances and the ratio of leaf dry mass to leaf area, across a wide range of species at the leaf scale. A narrow-band, normalized index combining two distinct wavebands centered at 1533 nm and 2264 nm achieved the highest overall performance and discriminatory power compared to either single bands or first derivatives. The normalized index was evaluated using the PROSPECT model, and leaf dry matter contents were retrievable with an R2 about 0.79. This study suggests that spectral reflectance measurements hold promise for the assessment of dry matter content for green leaves. Further investigation needs to be conducted to evaluate the effectiveness of this normalized index at canopy scales.