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.

翻译：可见光红外成像辐射计套件（VIIRS）活跃火点产品被广泛用于全球火灾监测，但其置信度分类方案呈现出一种未记录的系统性模式。通过对跨越一年（2023年1月至2024年1月）的21,540,921次火点探测进行分析，我发现在夜间观测中完全不存在低置信度分类。在6,007,831次夜间火点中，零次被分类为低置信度，而统计独立性下的预期值为696,908次（卡方值 = 1,474,795，p < 10^-15，Z = -833）。该模式在全球范围内持续存在，涵盖所有月份、纬度带以及NOAA-20和Suomi-NPP两颗卫星。机器学习逆向工程（88.9%准确率）、自助法模拟（1,000次迭代）和时空分析证实这是一种算法约束，而非地球物理现象。亮温分析表明，低于约295K的夜间火点可能被完全排除而非标记为低置信度，而日间火点则呈现正常的置信度分布。这一未记录的行为影响了27.9%的VIIRS火点探测，对火灾风险评估、昼夜探测比较、置信度加权分析以及任何将置信度水平视为不确定性指标的研究具有重要影响。我建议在VIIRS用户指南中明确记录此算法约束，并为受影响的分析制定再处理策略。