There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. The SFSIs were derived from composites of S2 multispectral images obtained with Google Earth Engine (GEE), processed using different techniques, for periods of 30, 60 and 90 days. Field verification was conducted through stratified random sampling by severity class on 100 circular plots of 707 m², where immediate post-fire effects were evaluated for five strata, including the canopy scorch in overstory (OCS)—divided in canopy (CCS) and subcanopy (SCS)—understory (UCS) and soil burn severity (SBS). Best fits were obtained with relative, phenologically corrected indices of 60–90 days. For canopy scorch percentage prediction, the indices RBR3c and RBR5n, using NIR (bands 8 and 8a) and SWIR (band 12), provided the best accuracy (R² = 0.82). SBS could be best mapped from RBR1c (using 11 and 12 bands) with relatively acceptable precision (R² = 0.62). Our results support the feasibility to separately map OCS and SBS from S2, in relatively open oak–pine seasonally dry forests, potentially supporting post-fire management planning.