Systematic Absence of Low-Confidence Nighttime Fire Detections in VIIRS Active Fire Product: Evidence of Undocumented Algorithmic Filtering

TL;DR

This study analyzes an undocumented pattern in the VIIRS active fire confidence classifications, showing a complete absence of low-confidence nighttime detections over a full year, which cannot be explained by random variation. Using a dataset of $21{,}540{,}921$ detections from FIRMS (Jan 2023–Jan 2024) and a suite of methods—$\chi^2$ independence testing, bootstrap resampling, a decision-tree reconstruction, and spatial-temporal analyses—the author demonstrates a global, deterministic filtering constraint likely tied to a nighttime brightness cutoff near $295$ K. The finding has broad implications for fire risk assessment, day-night comparisons, and confidence-weighted analyses, potentially biasing studies that treat confidence as uncertainty and across conditions. The work emphasizes the need for explicit documentation and reprocessing considerations, illustrating the value of large-scale empirical auditing in operational satellite products.

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product is widely used for global fire monitoring, yet its confidence classification scheme exhibits an undocumented systematic pattern. Through analysis of 21,540,921 fire detections spanning one year (January 2023 - January 2024), I demonstrate a complete absence of low-confidence classifications during nighttime observations. Of 6,007,831 nighttime fires, zero were classified as low confidence, compared to an expected 696,908 under statistical independence (chi-squared = 1,474,795, p < 10^-15, Z = -833). This pattern persists globally across all months, latitude bands, and both NOAA-20 and Suomi-NPP satellites. Machine learning reverse-engineering (88.9% accuracy), bootstrap simulation (1,000 iterations), and spatial-temporal analysis confirm this is an algorithmic constraint rather than a geophysical phenomenon. Brightness temperature analysis reveals nighttime fires below approximately 295K are likely excluded entirely rather than flagged as low-confidence, while daytime fires show normal confidence distributions. This undocumented behavior affects 27.9% of all VIIRS fire detections and has significant implications for fire risk assessment, day-night detection comparisons, confidence-weighted analyses, and any research treating confidence levels as uncertainty metrics. I recommend explicit documentation of this algorithmic constraint in VIIRS user guides and reprocessing strategies for affected analyses.