TY - RPRT
AU - Siham El Garroussi
AU - Patricia de Rosnay
AU - Sebastien Garrigues
AU - David Fairbairn
AU - Joe McNorton
AU - Francesca Di Giuseppe
AB - This deliverable outlines the methodology and results of Task 2 of Fuelity project, which focused on integrating Earth Observation data into the ECMWF land data assimilation system (LDAS) to constrain live fuel moisture content (LFMC) estimates. The aim is to support a high-resolution, observation-informed global LFMC reanalysis. Enhancements to ECMWF’s ecLand system include targeted modifications to the Simplified Extended Kalman Filter to assimilate L-band Vegetation Optical Depth, Solar-Induced Fluorescence, and ASCAT backscatter data, thereby improving estimates of Leaf Area Index and soil moisture, which underpin LFMC. A machine learning-based post-assimilation bias correction further refines the LAI inputs. Evaluation shows improved seasonal vegetation dynamics and realistic LFMC spatial and temporal patterns. The system successfully captured canopy loss during the 2021 Evia wildfire, demonstrating its potential for operational fire monitoring and risk assessment.
BT - ESA Contract Report
CY - Reading
DA - 07/2025
DO - 10.21957/0bb27c6524
M1 - 4000144712/24/I-DT-bgh
M3 - ESA Contract Report
N2 - This deliverable outlines the methodology and results of Task 2 of Fuelity project, which focused on integrating Earth Observation data into the ECMWF land data assimilation system (LDAS) to constrain live fuel moisture content (LFMC) estimates. The aim is to support a high-resolution, observation-informed global LFMC reanalysis. Enhancements to ECMWF’s ecLand system include targeted modifications to the Simplified Extended Kalman Filter to assimilate L-band Vegetation Optical Depth, Solar-Induced Fluorescence, and ASCAT backscatter data, thereby improving estimates of Leaf Area Index and soil moisture, which underpin LFMC. A machine learning-based post-assimilation bias correction further refines the LAI inputs. Evaluation shows improved seasonal vegetation dynamics and realistic LFMC spatial and temporal patterns. The system successfully captured canopy loss during the 2021 Evia wildfire, demonstrating its potential for operational fire monitoring and risk assessment.
PB - ECMWF
PP - Reading
PY - 2025
T2 - ESA Contract Report
TI - Advancing global live fuel moisture mapping through multi-sensor data assimilation
UR -  
ER -