Publication Date
1-9-2026
Document Type
Article
Publication Title
Remote Sensing
Volume
18
Issue
2
DOI
10.3390/rs18020227
Abstract
Highlights: What are the main findings? Conditional generative algorithms trained on simulations of historic wildfires may be used to effectively reconstruct the early-time progression of wildfires given satellite active fire measurements and terrain height data. When applied to real wildfires, generated fire progression estimates compare favorably to ground-truth high resolution infrared perimeters measured via aircraft, with the ability to gather additional information about model uncertainty from generated samples. What are the implications of the main findings? Once obtained, fire progression estimates may be used to perform data assimilation, wherein the estimated fire state is used to initialize subsequent wildfire spread forecasts. Fire progression estimates are additionally useful for providing situational awareness to wildfire stakeholders. The work developed here additionally demonstrates a framework for how critical characteristics affecting wildfire spread, such as terrain, may be used to improve estimates of a wildfire’s state. Wildfire spread prediction models, including even the most sophisticated coupled atmosphere–wildfire models, diverge from observed wildfire progression during multi-day simulations, motivating the need for measurement-based assessments of wildfire state and improved data assimilation techniques. Data assimilation in the context of coupled atmosphere–wildfire models entails estimating wildfire progression history from observations and using this to obtain initial conditions for subsequent simulations through a spin-up process. In this study, an approach is developed for estimating fire progression history from VIIRS active fire measurements, GOES-derived ignition times, and terrain height data. The approach utilizes a conditional Wasserstein Generative Adversarial Network trained on simulations of historic wildfires from the coupled atmosphere–wildfire model WRF-SFIRE, with corresponding measurements for training obtained through the application of an approximate observation operator. Once trained, the cWGAN leverages measurements of real fires and corresponding terrain data to probabilistically generate fire progression estimates that are consistent with the WRF-SFIRE solutions used for training. The approach is validated on five Pacific US wildfires, and results are compared against high-resolution perimeters measured via aircraft, finding an average Sørensen–Dice coefficient of 0.81. The influence of terrain data on fire progression estimates is also assessed, finding an increased contribution when measurements are uninformative.
Funding Number
80NSSC23K1344
Funding Sponsor
National Oceanic and Atmospheric Administration
Keywords
active fires satellite data, Bayesian methods, conditional generative models, data assimilation, deep learning, fire monitoring, model initialization
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Meteorology and Climate Science
Recommended Citation
Bryan Shaddy, Brianna Binder, Agnimitra Dasgupta, Haitong Qin, James Haley, Angel Farguell, Kyle Hilburn, Derek V. Mallia, Adam Kochanski, Jan Mandel, and Assad A. Oberai. "Generative Algorithms for Wildfire Progression Reconstruction from Multi-Modal Satellite Active Fire Measurements and Terrain Height" Remote Sensing (2026). https://doi.org/10.3390/rs18020227
