The near real-time (NRT) estimation of global burned area and fire emissions is crucial for applications such as climate modeling, air quality prediction, and ecosystem management. Active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) form the backbone of several NRT emissions systems. However, there is a critical need to connect NRT fire information to longer-term records from the Moderate Resolution Imaging Spectroradiometer (MODIS), which provide important context for the magnitude of extreme events and long-term trends. Additionally, quantifying burned areas and emissions from the same NRT system is essential to better understand fire impacts on biodiversity, ecosystem function, and infrastructure. To address these needs, we propose an innovative approach for estimating global fire emissions at a 0.25° spatial resolution for the post-MODIS era. Our method leverages historical data from the Global Fire Emissions Database version 5 (GFED5) and NRT detections of active fire pixels from VIIRS.

Our method employs a comprehensive two-step scaling algorithm to estimate burned area and fire emissions. In the first step, we derive a global map of NRT burned area using historical burned area from GFED5 and VIIRS active fire counts. This is achieved through a combination of local and regional regression analyses, which account for spatial and temporal variability in fire activity across 21 vegetation and fire classes. In the second step, we generate a global map of fuel consumption by correlating historical GFED5 emissions data with burned area for the same classes. The NRT burned area is combined with the fuel consumption map to produce global NRT fire estimates. This integrated method provides timely updates on fire activity while maintaining consistency with historical fire records. The resulting database contains multiple layers of global fire-related data such as active fire counts, burned area, fuel consumption, and fire emissions. This comprehensive dataset provides significant value for a variety of applications, including supporting air quality and climate models with real-time fire emissions inputs, and assessing fire impacts on ecosystems and the carbon cycle.