Fire is a natural disruption that affects the structure and function of forest systems by changing the vegetation composition, climatic situation, carbon cycle, wildlife habitat, and many other major properties. The measure of these changes’ degree is known as fire severity, and it can be assessed using remote sensing data (i.e., satellite images, aerial images, etc.) and various biophysical indices (such as Normalized Burn Ratio (NBR), Char Soil Index (CSI), Burn Area Index (BAI), etc.), in addition to the measurement of Land Surface Temperature (LST). This research aims to assess the response of NBR and LST in pre- and post-forest fire, taking as a study area, a Mediterranean forest located in the northern part of Morocco (35.1167° N, 5.7754° W), which burned in the summer of 2022. We used seven Landsat-8 images spanning three years: three images from 2021 (i.e., pre-fire), one image from the summer of 2022 (i.e., fire period), and three images from 2023 (i.e., post-fire). Results demonstrated a negative correlation between LST and NBR in the pre-fire period; when the temperature rises, the NBR drops. Same for the fire period in summer 2022, LST reached its peak at 50°C, while NBR decreased to its lowest point at -0.2. Whereas, in the recovery time (i.e., 2023), LST and NBR changed their fluctuation patterns; the first one variated normally according to seasons, dropping from the 50°C to 12°C in winter and reaching 37°C in summer, and the second one increased over time, going from the -0.2 to -0.04 in winter rising to 0.03 in summer, which indicates the gradual restoration of vegetation in the study area. The study concludes that in the post-fire period when the forest is recovering, NBR is unaffected by seasonal changes in temperature and is more reflective of the vegetation it projects more the vegetation situation in the area, unlike LST. Thus, relying only on LST to measure fire severity can give biased results due to changes in seasons.