MonteCarlo Biphasic Estimation of Fire Properties (McBEF): Part II, Night-time VIIRS Implementation

This study presents the application and global evaluation of the Monte Carlo Biphasic Estimation of Fire Properties (McBEF) algorithm using nighttime observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). McBEF extends traditional fire retrieval techniques by partitioning sub-pixel combustion into flaming and smoldering phases, enabling the estimation of phase-specific temperatures and fractional areas through a Bayesian inference framework. Building upon theoretical analyses and simulation-based validation in Part I, we apply McBEF to the NASA's VIIRS Fire Light Detection Algorithm version 2 (FILDA-2) fire detection product and assess its performance using a series of real-world wildfire cases, high-resolution Landsat observations, and multi-angle VIIRS views. We show that McBEF produces physically consistent fire parameter estimates across diverse land cover types and observation geometries. Compared to conventional empirical Fire Radiative Power (FRP) retrievals, McBEF achieves improved radiative closure and significantly reduces angular dependence in FRP estimates, a key advancement for satellite-based emission inventories. Global analyses reveal biome-specific combustion patterns and seasonal variations in flaming temperature, which correlate well with meteorological indicators such as the Fire Weather Index. A proof-of-concept assessment further demonstrates that McBEF-derived flaming heat fluxes for pyroCb events exceed those of general global fires and align with or exceed thresholds used in plume-rise modeling frameworks. These results confirm that McBEF effectively characterizes sub-pixel combustion properties and enhances the physical interpretability of satellite fire observations. The algorithm shows strong potential for integration into fire weather, air quality, and climate modeling systems, particularly where accurate vertical smoke injection profiles and energy partitioning are critical.