Fuel Moisture Content Estimation: A Land-surface Modelling Approach Applied to African Savannas

摘要

Considerable uncertainty exists in modelling the fire regime in Africa. This study attempted to reduce this uncertainty by improving the prediction of one of the most important fire modelling variables: fuel moisture content (FMC). The approach taken here was to construct a surface dryness index to represent FMC based on the ratio between remotely sensed Normalised Difference Vegetation Index (NDVI) data forced into a leading land-surface model, and model simulated land-surface temperature (LST). The findings presented here indicate that to adequately predict FMC over continental scales at high temporal resolution, biophysical modelling combined with satellite data represents a viable option.