Fuel moisture content (FMC) is a crucial factor that influences fire behavior, rendering its precise estimation indispensable for effective fire risk assessment and management. However, despite the widespread availability of remotely sensed imagery, which offers valuable insights into live fuel moisture content (LFMC) estimation, it remains a significant challenge, especially given the dynamic nature of live forest fuels.

The aim of this study was to establish a robust method for estimating and monitoring LFMC by employing spatio-temporal modelling with a universal kriging approach, integrating remote sensing data and field measurements. This research was conducted in the Sierra Morena region of Andalusia, Spain, focusing on Cistus ladanifer shrub patches, well-known for their high fire risk. A total of 38 sampling plots were established to monitor LFMC over a 15-month period, with destructive sampling techniques used to determine LFMC in the laboratory.

The universal kriging model was enriched by incorporating variables derived from Sentinel-2 and MODIS products to estimate and validate the moisture content, resulting in an RMSE (Root Mean Squared Error) score of 12%. These findings have practical implications for forest fuel modeling, fire risk evaluation, and operational decision-making concerning fire prevention and management not only in the study area but also in potentially similar regions.