对比发现：VIIRS数据更适合预测次日火灾，且改进的MODIS模型表现更优。
Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite Data
- 用VIIRS输入+VNP14目标，预测效果最佳
- 使用MODIS输入+VNP14目标时准确率显著提升
- MOD14火点掩膜随机性高，不适合机器学习建模
多项研究利用卫星图像进行次日火灾预测。主要使用的两颗卫星为MODIS和VIIRS，分别提供火点掩膜产品MOD14和VNP14。现有研究多仅采用其一，缺乏二者对比。本文评估了两者在次日火灾预测中的表现，发现以VIIRS为输入、VNP14为目标的模型效果最佳。有趣的是，使用MODIS输入与VNP14目标的模型优于使用VNP14输入与MOD14目标的模型。进一步分析表明，MOD14火点掩膜具有高度随机性，与合理火灾扩散模式不相关，不利于机器学习任务。因此，我们得出结论：MOD14不适合用于次日火灾预测，而VNP14是更优选择。但将MODIS作为输入、VNP14作为目标可显著提升预测性能，说明对MODIS的火点检测模型仍有优化空间。代码与数据集已公开于：https://github.com/justuskarlsson/wildfire-mod14-vnp14
原文摘要 · Abstract (English)
Multiple studies have performed next-day fire prediction using satellite imagery. Two main satellites are used to detect wildfires: MODIS and VIIRS. Both satellites provide fire mask products, called MOD14 and VNP14, respectively. Studies have used one or the other, but there has been no comparison between them to determine which might be more suitable for next-day fire prediction. In this paper, we first evaluate how well VIIRS and MODIS data can be used to forecast wildfire spread one day ahead. We find that the model using VIIRS as input and VNP14 as target achieves the best results. Interestingly, the model using MODIS as input and VNP14 as target performs significantly better than using VNP14 as input and MOD14 as target. Next, we discuss why MOD14 might be harder to use for predicting next-day fires. We find that the MOD14 fire mask is highly stochastic and does not correlate with reasonable fire spread patterns. This is detrimental for machine learning tasks, as the model learns irrational patterns. Therefore, we conclude that MOD14 is unsuitable for next-day fire prediction and that VNP14 is a much better option. However, using MODIS input and VNP14 as target, we achieve a significant improvement in predictability. This indicates that an improved fire detection model is possible for MODIS. The full code and dataset is available online: https://github.com/justuskarlsson/wildfire-mod14-vnp14
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