Mapping live fuel moisture content and flammability for continental Australia using optical remote sensing

We present the first continental-scale methodology for estimating Live Fuel Moisture Content (FMC) and flammability in Australia using satellite observations. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 363 observations at 33 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r ² =0.57, RMSE=40%) but without site-specific calibration. Logistic regression models were developed to predict a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest, obtaining performance metrics of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.