Forest fire is a problem that cannot be overlooked as it occurs every year and covers many areas. GISTDA has recognized this problem and created the model to detect burn scars from satellite imagery. However, it is effective only to some extent with additional manual correction being often required. An automated system is enriched with learning capacity is the preferred tool to support this decision-making process. Despite the improved predictive performance, the underlying model may not be transparent or explainable to operators. Reasoning and annotation of the results are essential for this problem, for which the XAI approach is appropriate. In this work, we use the SHAP framework to describe predictive variables of complex neural models such as DNN. This can be used to optimize the model and provide overall accuracy up to 99.85 \% for the present work. Moreover, to show stakeholders the reason and the contributed factors involved such as the various indices that use the reflectance of the wavelength (e.g. NIR and SWIR).