The objective of this research project was to evaluate capabilities of the remote sensing methods to assess wildfire behavior and effects from the space using open-source data and tools. The project utilized existing geospatial data layers, standardized satellite imagery sources and conventional workflows, showcasing the feasibility of using these open access resources as a combinatorial system. Within the project, a wildfire in Salamanca, Spain, in 2022 was analyzed. The distinct stages of the wildfire were evaluated utilizing remotely sensed data from Sentinel-2, Landsat-8, and Landsat-9. The methodology focused on the Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR/dNBR), smoke plume mapping, and surface temperature. The data was analyzed using open access computational platforms R, Python, and open source geographic information system QGIS. In the experimental set-up, both the NBR and NDVI were computed to assess vegetation health, burn severity, and land cover changes. NBR enables post-fire assessment of the burned area changes in the Area of Interest (AOI) and the complementary NDVI layer vegetation classification to arid and open ground areas. The NDVI, calculated from the Sentinel-2, Landsat-8, and Landsat-9 imagery, was able to detect changes in the vegetation from healthy green coverage to the arid land during the observation period of July 8 to 17. The computed NBR showed progression of the fire form the area of ignition of 15 km2 on July 13 to 63 km2 of area with moderate to high severity burn impact on July 18 correlating with the observed changes in the vegetation. Hot-spot mapping utilizing Sentinel-2 imagery visualized spread of the fire front from the initial ignition spot to the AOI. The experimental results correlated with the data provided by the Copernicus Emergency Management Service – Mapping (Copernicus EMS 2022) that was used as the reference. Smoke plume mapping using Sentinel-2 imagery was performed to determine possible smoke-related impacts on the surrounding villages. Band-based cloud masking showed visual alignment with the clusters generated by the unsupervised machine learning model K-means were compared. Classification into clusters (K-means) improved the detection sensitivity compared to the Sentinel-2 L2 atmospheric corrected data. The results demonstrated that open access resources can be employed as a combinatorial system to accurately detect and monitor wildfire on its various stages. Adding different machine learning data pipelines as well as non-publicly available satellite data with onboard fire detection sensors, would reduce manual efforts in the computation and improve accuracy and detection ability.