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Hyperspectral techniques to extract LAI from medium resolution MERIS superspectral data

Francis Canisius(1), Richard Fernandes(1) and Raymond Soffer(1)

(1) Canada Center for Remote sensing (CCRS), 588, Booth st, Ottawa, Ontario K1A 0Y7, Canada


Leaf Area Index (LAI), which is a key parameter controlling biophysical processes of the vegetation canopy, can be estimated using different approaches such as destructive sampling, optical ground-based measurements and estimation from satellite images. The estimation of LAI from satellite imagery, especially from low/moderate resolution optical remote sensing data having limited spectral sampling in the visible to shortwave infrared wavelength range, would have a significant benefit to the study of regional land surface processes. The Medium Resolution Imaging Spectrometer (MERIS) is unique among such sensors in that it provides a relatively high spectral sampling with 15 bands at 260m resolution within the VNIR spectral range. We use this capability to estimate continuous spectra to derive several narrow-band vegetation indices, including red-edge parameters, which have proven effective for LAI retrieval using hyperspectral data. The indices derived from the interpolated MEIRS sampled spectra are first evaluated by comparison to similar indices derived directly from standard hyperspectral signatures. We then apply both model based and empirical algorithms, calibrated with in-situ LAI data, to retrieve LAI from MERIS imagery acquired over agricultural and forest targets near Ottawa, Canada. Correlations between ground LAI and LAI values derived from interpolated MERIS hyperspectral data produced encouraging results.

Key words: MERIS, LAI, hyperspectral, Vegetation index, red edge


Workshop presentation

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry