Live fuel moisture mapping in unburned chaparral areas of Southern California with MODIS and VIIRS

Live fuel moisture (LFM) is a critical determinant of wildfire behavior, especially in Southern California chaparral, yet spatially continuous and near-real-time estimates remain limited by sparse field measurements, and spatial transferability challenges. In addition, the transition from MODIS to VIIRS requires evaluation of the continuity of long-term satellite-based LFM monitoring. This study developed a framework using 2003–2022 Globe-LFMC 2.0 dataset that first compared MODIS-based multiple linear regression and random forest models, then applied bias correction, and transferred the framework to VIIRS to assess cross-sensor continuity. This framework was further extended for near-real-time application by integrating analog-year phenology estimation and survival-based dry-down timing estimation. For MODIS, random forest achieved higher accuracy with the full dataset, but its performance declined substantially under leave-one-county-out spatial cross-validation and showed reduced ability to capture low and fire-disturbed LFM values. Multiple linear regression showed more stable performance between full-dataset evaluation (R 2 = 0.63, RMSE = 12.35%) and spatial cross-validation (R 2 = 0.58, RMSE = 12.43%), indicating greater spatial transferability. MODIS models provided higher predictive skill overall than VIIRS, whereas the M-band-only VIIRS configuration produced the highest VIIRS performance and reproduced major seasonal and spatial LFM patterns. Independent validation using 2023–2024 Fire Environment Mapping System (FEMS) dataset showed that LFM thresholds of 85–90% provided the most balanced classification of elevated-risk conditions for both MODIS and VIIRS. This study shows that an interpretable, phenology-informed satellite framework can support spatially transferable and temporally consistent LFM monitoring for chaparral fire risk assessment.