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We used two hyperspectral sensors at two different scales to test their potential to estimate biophysical properties of grazed pastures inRond˘nia in the Brazilian Amazon. Using a field spectrometer, ten remotely sensed measurements (i.e., two vegetation indices, four fractions ofspectral mixture analysis, and four spectral absorption features) were generated for two grass species, Brachiaria brizantha and Brachiariadecumbens. These measures were compared to above ground biomass, live and senesced biomass, and grass canopy water content. The samplesize was 69 samples for field grass biophysical data and grass canopy reflectance. Water absorption measures between 1100 and 1250 nm had thehighest correlations with above ground biomass, live biomass and canopy water content, while ligno-cellulose absorption measures between 2045and 2218 nm were the best for estimating senesced biomass. These results suggest possible improvements on estimating grass measures usingspectral absorption features derived from hyperspectral sensors. However, relationships were highly influenced by grass species architecture. B.decumbens, a more homogeneous, low growing species, had higher correlations between remotely sensed measures and biomass than B.brizantha, a more heterogeneous, vertically oriented species. The potential of using the Earth Observing-1 Hyperion data for pasturecharacterization was assessed and validated using field spectrometer and CCD camera data. Hyperion-derived NPV fraction provided betterestimates of grass surface fraction compared to fractions generated from convolved ETM+/Landsat 7 data and minimized the problem of spectralambiguity between NPV and Soil. The results suggest possible improvement of the quality of land-cover maps compared to maps made usingmultispectral sensors for the Amazon region.

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