Monitoring ecosystem events such as wildfires with remote sensing is fundamental to natural resources management. However, precisely delineating burned areas with remote sensing remains a challenge for post-fire ecological assessment. Burned area mapping methods with spectral indices are affected by several interferences such as soil background, water surfaces and atmospheric effects. In this study, we test the applicability of a normalized difference spectral index with the shortwave infrared and blue spectral bands in accurately mapping burned areas. The spectral index was tested in two study sites and compared to six spectral indices commonly used in burned area mapping. Random forest (RF), a machine learning algorithm, was used to classify the spectral indices and map burned areas. Although the True Skill Statistic (TSS) was relatively low, we found out that the Optimized Soil Adjusted Vegetation Index (OSAVI) and Normalized Burned Index (NBI) performed better than all the other spectral indices considered in the two study sites. The Burned Area Index (BAI) was the third highest performing spectral index. OSAVI and NBI performed well in the study sites plausibly because they were adjusted for soil effects and the blue spectral band was included to account for atmospheric effects. Our results indicate the need to adjust spectral indices for soil, water and atmospheric effects when using them for burned area mapping. The study also adds rudimentary knowledge on the potential of the blue spectral band in monitoring burned area vegetation