The Impact of Radiometric Terrain Normalization (γ⁰) on Burned Area Mapping Accuracy Using Sentinel-1 data

Wildfires are increasingly destructive events, threatening ecosystems and human infrastructure while contributing significantly to carbon emissions. Accurate and timely burned area mapping is therefore essential for effective mitigation and recovery. Optical satellite sensors are often hindered by clouds and smoke, making Synthetic Aperture Radar (SAR) sensors like Sentinel-1, with their all-weather capability, a crucial tool for monitoring. However, SAR backscatter is significantly influenced by topography, which can distort signals and hinder accurate detection.

This study evaluates the impact of angular-based radiometric terrain normalization (RTN) on burned area mapping using Sentinel-1 SAR data and the Normalized Radar Burn Ratio (NRBR) index. We compare the performance of NRBR calculated with standard sigma nought (σ⁰) and with gamma nought (γ⁰) corrected via an angular-based RTN model implemented in Google Earth Engine. A U-Net deep learning model was used to delineate burned areas in Portugal and California. Results show that NRBR without RTN achieved better accuracy in Portugal, suggesting potential overcorrection effects in moderate terrain. In California, RTN slightly improved overall accuracy and reduced commission errors, although omission errors remained high. These findings indicate that while RTN enhances radiometric consistency, its impact on burned area detection with NRBR is limited, likely because the NRBR formulation itself already mitigates topographic effects through pre/post-fire ratios.