Satellite-Based Wildfire Monitoring Across the Fire Lifecycle Using Artificial Intelligence: Trends, Challenges, and Future Directions

Wildfires are complex disasters that extend beyond vegetation combustion, producing cascading impacts on the atmosphere, soils, hydrology, ecosystems, and human society.Their risks and impacts are being amplified by climate change, highlighting the need to understand and monitor wildfires not as isolated events but as full-cycle phenomena that encompass pre-fire, active-fire, and post-fire stages.This study systematically reviews wildfire research published over the past decade that has used satellite remote sensing and artificial intelligence (AI) and synthesizes trends in data use and methodology from a lifecycle perspective.Relevant literature was retrieved from the Scopus database using search terms related to wildfire, satellite observations, and AI.The selected studies were classified into pre-fire, active-fire, and post-fire stages.They were compared in terms of study region, input data, reference data, input representation, and algorithmic approach.The results show that relevant studies have increased rapidly in recent years, with clear stage-specific differences in research objectives.Pre-fire studies mainly focus on wildfire occurrence prediction and risk assessment.Active-fire studies emphasize active fire and smoke detection, as well as the monitoring of fire fronts and spread dynamics.Post-fire studies concentrate on burned-area mapping, burn severity assessment, damage evaluation, and vegetation recovery analysis.Geographically, the literature is strongly concentrated in North America and Asia, with a substantial proportion of studies focused on a small number of countries, including the United States, China, Australia, Canada, Brazil, and South Korea.Although single-country studies still dominate, multi-regional and globalscale studies have increased in recent years.In terms of data use, optical satellite imagery remains the most widely used source, while the integration of thermal infrared, synthetic aperture radar, meteorological, topographic, and land-cover data has become increasingly common.Methodologically, machine learning and deep learning approaches increasingly supplement or replace conventional index-based and rulebased methods, with model architectures and input representations differing by wildfire stage and research objective.Nevertheless, regional bias in training data, data imbalance, limited model interpretability, and restricted cross-regional generalizability remain major challenges.Future wildfire monitoring research should therefore move toward integrated lifecycle frameworks that combine multi-source data fusion, regional scalability, physically informed learning, and interpretable AI.