## 4 Discussion  
The resulting burned area maps for five wildfire cases indicate that the new burned area mapping method obtains higher accuracy than the traditional empirical formula method based on a single MODIS image. The advantages of the proposed method come from the integration of multiple spectral indices and a neural network. The separability analysis between the burned area type and unburned types indicates that using six spectral indices (MIRBI, NBR, CSI, bsVI, NSEv1, and NSTv1) brings better performance, even though using a single spectral band results in inconsistent separating capacity. In contrast to the traditional empirical formula method [18], the new method utilizes the optimal spectral indices rather than the spectral bands to identify the burned pixels. Also, based on the training samples of multiple spectral indices, the BPNN algorithm used in our method can make better use of the differences between the burned area and unburned cover types. The refinements will greatly improve the accuracy of burned area detection results. The new method can be applied to a single MODIS image by taking full advantage of the spectral information of different types in the image while providing rapid wildfire assessment. The accuracy of this method is based upon the important premise that the extracted samples of one type definitely belong to this type. A couple of factors are considered to ensure that the sample sets are correct. First of all, a combination of traditional methods based on previous research [18; 25; 31; 42; 43; 44; 45; 46; 69] is utilized to extract the samples of five typical land cover types. Conservative thresholds from the corresponding references and the appropriate empirical formulas derived from the data analysis are used in the traditional methods to guarantee the validity of the extracted samples. Moreover, we also performed the quantitative accuracy assessment by 10-fold cross validation for the sample sets. The analysis results of high accuracy and low error rate indicate that the sample sets extracted by the methods described in Section 2.3.2 are valid for training the neural network. Spectral differences between the burned area and unburned types were investigated based on the sample sets. We analyzed seven spectral bands of visible, NIR and SWIR spectral regions and 13 spectral indices including the vegetation indices, burned area indices, and NBR-based indices in the separability analysis. The beneficial effect of using the reflectance of NIR and SWIR spectral regions, as well as the data of emissivity and LST, to identify the burned area is consistent with the findings of References [20; 22; 24; 52]. Nevertheless, the reflectance of any single spectral band has limited capacity to distinguish the land cover types in varying regions, which explains the limitations of using spectral bands to identify the burned area with the traditional empirical formula method. Also, multiple spectral indices of NBR, bsVI, NSEv1, and NSTv1 performed well in burned region detection, which corroborates the research of References [5; 16; 22]. The index CSI was also proved with high separability in Reference [20], and MIRBI is known to be suitable in shrub-savannah ecosystems [24; 52]. The spectral indices could not reach the same performance in different regions [5] due to the vegetation types, the conditions of climate, and the illumination, as well as the wavelengths of the sensor; these results were also found by Reference [22]. However, we can utilize the learning and modeling ability of the BPNN algorithm to obtain the spectral differences of different types based on the samples of the spectral indices, thereby achieving better separability of the burned area. This contributes to improving the accuracy of detection results in the burned area map. Furthermore, whether for MODIS data or other sensor data, the results of the separability analysis in this paper provide valuable assessment information about the use of spectral regions and spectral indices to discriminate burned areas.