Fire has a vast inuence on the climatic balance, and the Global Climate Observing System (GCOS) considersit an Essential Climate Variable (ECV). Remote sensing data is a powerful source of information for burnedarea detection and thus for estimating greenhouse gases (GHGs) emissions from res. Currently, most burnedarea 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 ontemporal backscatter coe cient to understand their suitability for burned area detection. The analysis wascarried out using the random forests machine learning classi er, which provides a rank for each independentvariable used as input. Depending on land cover type, soil moisture, and topographic conditions, remarkabledi erences were observed between the temporal backscatter based indices.
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