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Hyperspectral remote sensing is frequently used to monitor chlorophyll content, an important characteristic for assessing photosynthetic ability, health and defence against a variety of degenerative diseases. To obtain hyperspectral data, field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have been widely used. The development of an affordable hyperspectral remote sensing system would be advantageous. Highly sensitive, affordable and cost-effective finger-tip size spectrometers have recently been released. In this study we investigate the potential of hyperspectral data obtained from such a compact spectrometer (C12880MA-10, Hamamatsu Photonics) for estimating chlorophyll content in  Zizania latifolia . We also tested the efficacy of five pre-processing techniques (first derivative reflectance, continuum-removal transformation, de-trending, multiplicative scatter correction and standard normal variate) in conjunction with five machine learning algorithms.

Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in Zizania latifolia