A Unified Multisensor Machine-Learning Framework for Live Fuel Moisture Content Retrieval

Live fuel moisture content (LFMC) influences vegetation flammability, but field measurements are too sparse to describe its variation over large areas. We present, to our knowledge, the first LFMC framework that combines observations from multiple optical satellite sensors within the same trained models. More than two decades of GlobeLFMC 2.0 measurements are matched to Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Landsat 8/9, Sentinel-2 and Sentinel-3. We screen the vegetation surrounding sampling locations and adjust reflectance for differences between sensors before calculating vegetation indices. Weather, topography and time of year provide additional information. Separate models are trained for grasses, shrubs and trees. We test prediction on dates not used for training at sites where other dates contribute to training. At each site, records using the same satellite image are kept in either the training or the test set. The final Random Forest models achieve R2 values of 0.715, 0.693 and 0.691, respectively. Models trained on the combined archive give higher R2 and lower root mean square error than models trained on one satellite product in all 18 product–vegetation comparisons on identical test observations. Paired 95% confidence intervals support the improvement in 16 comparisons. Testing at sites excluded from training gives lower accuracy. The method produces LFMC maps at the selected satellite product’s resolution, from 20–30 m to 300–500 m.