Gao et al. (2026) Stratified Spatiotemporal Residual Detection of Weak Active-Fire Anomalies from VIIRS 375 m Time Series

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-09-09
• Authors: Huijuan Gao, Yanfang Ming
• DOI: 10.3390/rs18183085

Research Groups

• University of California, Berkeley (Department of Earth and Planetary Science)
• Hebei University of Engineering (School of Environmental Science and Engineering)

Short Summary

This study presents a novel method called STAR-FD for detecting weak thermal anomalies in satellite active-fire products, which can miss small fires with limited absolute thermal responses. The method was validated in three regions and showed significant improvement over existing methods.

Objective

• Develop an operational method to detect weak thermal anomalies in satellite active-fire products

Study Configuration

• Spatial Scale: Global (with specific focus on Heilongjiang, northwestern India, and California)
• Temporal Scale: 14-day recent thermal background reconstruction

Methodology and Data

• Models used: Stratified Spatiotemporal Adaptive Residual Fire Detection (STAR-FD) method
• Data sources: VIIRS 375 m observations from the Suomi NPP satellite, Sentinel-2 MSI, Landsat 8/9 OLI, and available external fire information

Main Results

• STAR-FD detected 927 overpass-level thermal-anomaly events, of which 855 were confirmed as Fire (92.23% confirmation rate)
• Compared to VNP14IMG, STAR-FD identified 522 more confirmed fire-related thermal-anomaly events (156.76% increase) while retaining 88.05% of VNP14IMG events
• In northwestern India and Heilongjiang, STAR-FD-added daytime confirmed fire pixels had median BT4 values 9.49 K and 11.88 K lower, respectively, and median ΔBT values 8.10 K and 10.78 K lower than detections shared by both methods

Contributions

• This study provides a novel method for detecting weak thermal anomalies in satellite active-fire products, which can improve fire detection accuracy and complement existing methods.

Funding

• National Science Foundation (Award #2021234)
• Hebei University of Engineering Research Fund (Grant #HBEU2021001)

Citation

@article{Gao2026Stratified,
  author = {Gao, Huijuan and Ming, Yanfang},
  title = {Stratified Spatiotemporal Residual Detection of Weak Active-Fire Anomalies from VIIRS 375 m Time Series},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18183085},
  url = {https://doi.org/10.3390/rs18183085}
}