Derivative analysis of spectral data was used as a technique to study the variation of optically active constituents (OACs) of water, using field data and hyperspectral imagery of EO-1 Hyperion orbital sensor. The Hyperion image used in this study was acquired on June 23, 2005, at the end of the high water period for the Amazon River. A field campaign was carried out between June 23 and 29, 2005 to collect spectral and limnological in situ data. The image was pre-processed to remove stripes of abnormal pixels and converted from radiance to surface reflectance values, thus, correcting the effects of atmospheric absorption and scattering. A correlation analysis was carried out to examine the association of the spectral reflectance and its first derivative to the concentrations of OACs. Better results were obtained after spectra differentiation, which helped to reduce the influence of undesirable effects, coming from different sources of radiance, on the measurements of water surface reflectance taken at both data acquisition levels. Through empirical regression fits, considering the Hyperion dataset, the first spectral derivative at 711 nm explained 86% of the variation of suspended inorganic sediment concentration (µg.l-1), and the first derivative at 691 nm explained 73% of the variation of chlorophyll-a concentration (µg.l-1). The regression relations were nonlinear because, generally, the water masses that mix in the Amazon floodplain become optically complex. The hyperspectral derivative analysis demonstrated potential for mapping the composition of these waters.
Hyperspectral remote sensing; first derivative; optically active constituents; Hyperion; Amazon