Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers

We used two hyperspectral sensors at two different scales to test their potential to estimate biophysical properties of grazed pastures in Rondônia in the Brazilian Amazon. Using a field spectrometer, ten remotely sensed measurements (i.e., two vegetation indices, four fractions of spectral mixture analysis, and four spectral absorption features) were generated for two grass species, Brachiaria brizantha and Brachiaria decumbens. These measures were compared to above ground biomass, live and senesced biomass, and grass canopy water content. The sample size was 69 samples for field grass biophysical data and grass canopy reflectance. Water absorption measures between 1100 and 1250 nm had the highest correlations with above ground biomass, live biomass and canopy water content, while ligno-cellulose absorption measures between 2045 and 2218 nm were the best for estimating senesced biomass. These results suggest possible improvements on estimating grass measures using spectral 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 pasture characterization was assessed and validated using field spectrometer and CCD camera data. Hyperion-derived NPV fraction provided better estimates of grass surface fraction compared to fractions generated from convolved ETM+/Landsat 7 data and minimized the problem of spectral ambiguity between NPV and Soil. The results suggest possible improvement of the quality of land-cover maps compared to maps made using multispectral sensors for the Amazon region.