Yonatan Tarazona Coronel
PhD student at the University of Coimbra, Portugal. I am a geospatial scientist specializing in advanced Earth Observation with a core focus on Synthetic Aperture Radar (SAR) applications. I am the author of the novel Normalized Radar Burn Ratio (NRBR) index, a significant contribution to SAR-based burned area mapping. My research extensively leverages multi-sensor data fusion, integrating SAR and optical time series with machine and deep learning for detecting burned areas, deforestation, forest degradation, and other land cover changes. A skilled developer of open-source software and proficient in geocomputation with Python, R, and Google Earth Engine.
Sessions
This study presents the development and multi-regional application of the Normalized Radar Burn Ratio (NRBR), a novel Synthetic Aperture Radar (SAR)-based index designed to improve burned area detection under challenging observational conditions. Unlike traditional optical indices such as the differenced Normalized Burn Ratio (dNBR), NRBR exploits the complementary behavior of Sentinel-1 C-band co-polarized (VV: vertical transmit–vertical receive) and cross-polarized (VH: vertical transmit–horizontal receive) backscatter signals, enhancing the contrast between burned and unburned surfaces by capturing fire-induced structural changes in vegetation.
The NRBR formulation is based on the normalized difference between polarization-specific Radar Burn Ratios, effectively integrating post- to pre-fire backscatter dynamics while reducing speckle noise and topographic effects. Initial validation in Mediterranean ecosystems demonstrated that NRBR improves burned area delineation compared to conventional radar indices, achieving strong agreement with optical-based metrics and competitive segmentation performance when implemented within a U-Net deep learning framework.
Building on these results, the index was further evaluated across diverse fire-prone regions including Portugal, Spain, California, and Canada, encompassing Mediterranean, chaparral, and boreal ecosystems. The results indicate that NRBR achieves performance comparable to, and in some cases exceeding, optical approaches, particularly in cloud-prone or smoke-affected conditions where optical data are limited. Additionally, a SAR–optical fusion strategy combining NRBR and dNBR further improves mapping accuracy and spatial consistency at large scales.
Overall, NRBR demonstrates strong potential as a robust and scalable alternative for burned area mapping, providing consistent performance across different land cover types and environmental conditions. Its cloud independence and sensitivity to vegetation structural changes position it as a valuable tool for operational wildfire monitoring and next-generation multi-sensor mapping frameworks.