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Evaluation of backscatter coefficient temporal indices for burned area mapping

Fire has a vast influence on the climatic balance, and the Global Climate Observing System (GCOS) considers it an Essential Climate Variable (ECV). Remote sensing data is a powerful source of information for burned area detection and thus for estimating greenhouse gases (GHGs) emissions from fires. Currently, most burned area products are based on optical images. However, cloud cover independent Synthetic Aperture Radar (SAR) datasets are increasingly exploited for burned area mapping. This study assessed temporal indices based on temporal backscatter coefficient to understand their suitability for burned area detection. The analysis was carried out using the random forests machine learning classifier, which provides a rank for each independent variable used as input. Depending on land cover type, soil moisture, and topographic conditions, remarkable differences were observed between the temporal backscatter based indices.