VIIRS火点检测夜间低置信度数据全缺失，系未公开算法过滤所致。
Systematic Absence of Low-Confidence Nighttime Fire Detections in VIIRS Active Fire Product: Evidence of Undocumented Algorithmic Filtering
- 通过反向机器学习与模拟验证，发现夜间火点无低置信度分类是算法强制剔除所致
- 一年内600万夜间火点零低置信度，远低于预期的69.7万（p<10^-15）
- 影响27.9%总火点数据，涉及风险评估与昼夜对比研究，需更新用户指南
可见红外成像辐射计套件（VIIRS）活跃火产品广泛用于全球火情监测，但其置信度分类机制存在未公开的系统性偏差。基于2023年1月至2024年1月共21,540,921次火点检测分析，发现夜间观测中完全不存在低置信度分类。在6,007,831个夜间火点中，无一被标记为低置信度，而根据统计独立性预期应有696,908个（卡方值=1,474,795，p<10^-15，Z=-833）。该现象在全球所有月份、纬度带及两颗卫星（NOAA-20与Suomi-NPP）上均持续存在。机器学习反向建模（准确率88.9%）、自助法模拟（1,000次迭代）与时空分析均证实此为算法约束而非自然现象。亮度温度分析表明，夜间温度低于约295K的火点可能被彻底排除而非标记为低置信度，而日间火点则呈现正常置信分布。该未公开行为影响了27.9%的总体火点检测，对火险评估、昼夜检测对比、置信加权分析以及将置信度视为不确定性的研究产生重大影响。建议在VIIRS用户手册中明确标注此算法限制，并对受影响分析进行再处理。
原文摘要 · Abstract (English)
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.
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