Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-1911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An adapted hourly Himawari-8 fire product for China: principle, methodology and verification
Jie Chen
Key Laboratory of Radiometric Calibration and Validation for
Environmental Satellites, National Satellite Meteorological Center (National
Center for Space Weather), China Meteorological Administration and
Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing
100081, China
Qiancheng Lv
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100091, China
Shuang Wu
Heilongjiang Eco-Meteorology Center, Harbin, Heilongjiang 150030,
China
Yelu Zeng
College of Land Science and Technology, China Agricultural University,
Beijing 100083, China
Manchun Li
School of Geography and Ocean Science, Nanjing University, Nanjing
210023, China
Ziyue Chen
CORRESPONDING AUTHOR
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100091, China
Enze Zhou
Electric Power Research Institute, Guangdong Power Grid, Guangzhou,
Guangdong 510000, China
Wei Zheng
Key Laboratory of Radiometric Calibration and Validation for
Environmental Satellites, National Satellite Meteorological Center (National
Center for Space Weather), China Meteorological Administration and
Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing
100081, China
Cheng Liu
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100091, China
Xiao Chen
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100091, China
Jing Yang
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100091, China
Bingbo Gao
CORRESPONDING AUTHOR
College of Land Science and Technology, China Agricultural University,
Beijing 100083, China
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Cited
11 citations as recorded by crossref.
- Evaluation of Himawari-8/AHI land surface reflectance at mid-latitudes using LEO sensors with off-nadir observation B. Zhang et al.
- Crop residue burning in China (2019–2021): Spatiotemporal patterns, environmental impact, and emission dynamics Q. Lv et al.
- AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data X. Chen et al.
- A Multiple Spectral Criteria Active Fire Detection Algorithm Supported by Hyperspectral Dataset X. Liu et al.
- Research on forest and grassland fire detection algorithm based on Himawari-8 geostationary satellite S. Zhang et al.
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al.
- Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 Z. Xu et al.
- Analysis of Forest Fire Emissions and Meteorological Impacts in Southwestern China Based on Multi-Source Satellite Observations L. Fang et al.
- Fusing Instantaneous and Historical Spatial–Contextual Brightness Temperature Differences for Himawari-8/9 Active Fire Detection X. Liu & Y. Ming
- A Bidirectional Spatiotemporal Deep Learning Model with Integrated Vegetation–Thermal Features for Wildfire Detection H. Luo et al.
- 10-minute level and near real-Time wildfire detection: integrating multiple features for Himawari-8/9 satellite B. Zhang et al.
11 citations as recorded by crossref.
- Evaluation of Himawari-8/AHI land surface reflectance at mid-latitudes using LEO sensors with off-nadir observation B. Zhang et al.
- Crop residue burning in China (2019–2021): Spatiotemporal patterns, environmental impact, and emission dynamics Q. Lv et al.
- AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data X. Chen et al.
- A Multiple Spectral Criteria Active Fire Detection Algorithm Supported by Hyperspectral Dataset X. Liu et al.
- Research on forest and grassland fire detection algorithm based on Himawari-8 geostationary satellite S. Zhang et al.
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al.
- Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 Z. Xu et al.
- Analysis of Forest Fire Emissions and Meteorological Impacts in Southwestern China Based on Multi-Source Satellite Observations L. Fang et al.
- Fusing Instantaneous and Historical Spatial–Contextual Brightness Temperature Differences for Himawari-8/9 Active Fire Detection X. Liu & Y. Ming
- A Bidirectional Spatiotemporal Deep Learning Model with Integrated Vegetation–Thermal Features for Wildfire Detection H. Luo et al.
- 10-minute level and near real-Time wildfire detection: integrating multiple features for Himawari-8/9 satellite B. Zhang et al.
Saved (final revised paper)
Latest update: 20 May 2026
Short summary
The Himawari-8 fire product is the mainstream fire product with the highest temporal resolution, yet it presents large uncertainties and is not suitable for reliable real-time fire monitoring in China. To address this issue, we proposed an adaptive hourly NSMC (National Satellite Meteorological Center) Himawari-8 fire product for China; the overall accuracy increased from 54 % (original Himawari product) to 80 %. This product can largely enhance real-time fire monitoring and relevant research.
The Himawari-8 fire product is the mainstream fire product with the highest temporal resolution,...
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