Live fuel moisture and chlorophyll content plays an important role in predicting wildfire ignition and propagation probabilities. We combine geometric and radiative transfer models to characterize the spectral properties of vegetation regions that are observed in visible through short-wave infrared (VNIR/SWIR) hyperspectral images over a wide range of conditions. Leaf spectral reflectance models that depend on moisture and chlorophyll content are combined with MODTRAN atmospheric models to predict sensor radiance spectra. These spectra are used as input to machine learning methods to generate algorithms for estimating fuel characteristics. We demonstrate properties of the models and algorithms using a database of model remote sensing data. We show that this approach provides accurate estimates of vegetation moisture and chlorophyll content.
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