GNSS-R-Based wildfire detection: a novel and accurate method

In recent years, global forests have faced frequent wildfires due to climate change, leading to significant ecological and economic losses. Traditional fire monitoring technologies like MODIS and VIIRS are often hindered by cloud cover and smoke. This study explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for fire monitoring in complex environments. By analyzing cumulative burned areas in southeastern Brazil, a new fire detection method combining GNSS-R with Convolutional Neural Networks (CNN) is proposed to improve accuracy and response time. Results show high agreement between predicted and actual fire areas, with CNN outperforming Random Forest (RF) by 3.3% in prediction accuracy and achieving a 17.4% increase in model fitting. Error analysis indicates no systematic errors, with CNN exhibiting lower fire detection errors and better spatial alignment with actual conditions than RF, which tends to overestimate. This study provides a novel approach to fire detection using GNSS-R, with significant implications for fire prediction and management strategies.