Skip to main content

Resource Catalog


Type: Journal Article
Author(s): Mukul Badhan; Kasra Shamsaei; Hamed Ebrahimian; George Bebis; Neil P. Lareau; Eric M. Rowell
Publication Date: 2024

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Online Links
Citation: Badhan, Mukul; Shamsaei, Kasra; Ebrahimian, Hamed; Bebis, George; Lareau, Neil P.; Rowell, Eric M. 2024. Deep learning approach to improve spatial resolution of GOES-17 wildfire boundaries using VIIRS satellite data. Remote Sensing 16(4):715.

Cataloging Information

National    Alaska    California    Eastern    Great Basin    Hawaii    Northern Rockies    Northwest    Rocky Mountain    Southern    Southwest
  • AI - artificial intelligence
  • autoencoder
  • deep learning
  • GOES - Geostationary Operational Environmental Satellite
  • machine learning
  • operational monitoring
  • remote sensing
  • super resolution
  • VIIRS - Visible Infrared Imaging Radiometer Suite
  • wildfires
Record Last Modified:
Record Maintained By: FRAMES Staff (
FRAMES Record Number: 69159