Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored.

We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments.

We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous US. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds and potential bias.

Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence with observed burn severity categories.

Offset corrections offer benefits but also present trade-offs to accurately representing remotely sensed burn severity.

The utility of offset corrections depends on the environment, methods and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.