Progress in Hyperspectral Remote Sensing of Terrestrial Chlorophyll Content

Information on the amount and spatial distribution of canopy chlorophyll content is of importance for the study of vegetation productivity and health, nutrient cycling, crop stress and crop yield, and most recently, for driving ecosystem simulation models from local to global scales. The amount of chlorophyll can be estimated using remotely sensed estimates of the wavelength location of the `red edge'. This feature marks the boundary between high absorption in the red and high reflectance in the near infrared region of a vegetation reflectance spectrum and is visible in `hyperspectral' spectra. Such spectra can be collected using laboratory (e.g., Perstorp NIRSystem 6500 spectrometer), field (e.g., Geophysical Environmental Research IRIS Mark IV), airborne (e.g., Airborne Visible/Infrared Imaging Spectrometer, AVIRIS) and more recently, spaceborne imaging spectrometers (e.g., MEdium Resolution Imaging Spectrometer, MERIS). The position of the red edge can be estimated (i) directly using methods such as maximum of first derivative spectra, linear interpolation, curve fitting and Lagrangian interpolation or (ii) indirectly using vegetation indices such as the MERIS Terrestrial Chlorophyll Index (MTCI). The former requires continuous spectral data recorded in narrow spectral bands and is therefore, limited to local scale applications, whereas the latter uses discontinuous spectral data of the type recorded by a speceborne spectrometer and can therefore used for global scale applications. Over the past few decades the remote sensing of chlorophyll content has evolved from the development of empirical relationships between chlorophyll content and spectral reflectance in individual wavebands to the production of an operational product; weekly global terrestrial chlorophyll content maps derived from MERIS data. This paper will summarise the techniques and data used to estimate the chlorophyll content of vegetation and discuss some regional and global scale applications of such information.