## 5 Conclusions  
The new burned area mapping method integrating multiple spectral indices and a neural network was developed for near real-time burned area detection based on a single MODIS image. Besides this, the spectral characteristics analysis in this study can contribute to the selection of adequate spectral regions and spectral indices, which is applicable to the use of data from other sensors with a finer spatial resolution for burned area discrimination and wildfire assessment. The advantages of this proposed method come from the use of multiple spectral indices and the BPNN technique. Our separability analysis between the burned area and unburned types found that NIR and SWIR spectral regions are more sensitive to the burned area than the visible region. The data of LST and emissivity is also proved to be applicable for burned area detection. Rather than using the distinguishing capacity of a single spectral band, the spectral indices with a high capacity for burned area discrimination in various wildfires were used to generate the training sample sets for the BPNN. The sample sets' high accuracy of 98.68% and Kappa coefficient value of 97.46% improved the detection accuracy of the method. Compared to the traditional empirical formula method applied in the Hot Pot Fire and Range 12 Fire, the proposed method demonstrates a significant improvement in burned area detection with a reduction of 10.28% and 50.58% for the CE as well as 9.69% and 6.03% for the OE. This accuracy improvement is mainly due to the BPNN, which can take full advantage of the spectral differences between the burned area and unburned types. The new method was also applied in the validation fire cases to derive the MODIS-based burned area maps, which were assessed by the OLI-based burned area maps: the CE and OE of the Cinder Butte Fire are 8.07%, and they are 14.40% and 17.16% in Tohakum 2 Fire. The application of the method in the wildfire cases proves that it can effectively identify the burned area based on a single MODIS image. Although the method performed well in burned area detection in this study, more sample sets of varying wildfire cases need to be investigated. Future research will be devoted to establishing specific seasonal and regional training sample sets including different vegetation types, which can further improve the reliability and applicability of the method. Data curation, R.B. and Z.X. ; Funding acquisition, W.S. and S.L. ; Investigation, R.B. and Z.X. ; Methodology, R.B., W.S. and Z.X. ; Software, R.B. and Xiaolian Li; Supervision, W.S. and S.L. ; Validation, R.B. and X.L. ; Visualization, Z.X. ; Writing--original draft, R.B. ; Writing--review & editing, W.S., X.L. and S.L. This research was funded by National Key R&D Program of China (2018 YFC0807000), Fundamental Research Funds for the Central Universities (Grant No. WK2320000040), and the Research Grant Council of the Hong Kong Special Administrative Region, China (contract grant number CityU 11300815). We are very grateful for the valuable suggestions and comments of peer reviewers. We also acknowledge the use of MODIS Level 1B data which was acquired from the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland ([[https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)]([https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/))), and the use of OLI Collection 1 LTP products which were acquired from EarthExplorer website ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) of United States Geological Survey (USGS). The authors declare no conflict of interest.