Terrain-Aware Uncertainty Quantification and Cross-Sensor Consistency Analysis of Hyperspectral Surface Reflectance

Abstract

Topographic variation introduces substantial uncertainty into hyperspectral surface reflectance retrieval. This study proposes a combined correction and uncertainty quantification framework that integrates ISOFIT-based atmospheric correction with a semi-empirical Modified Minnaert topographic correction, while further introducing a novel uncertainty evaluation approach. The framework combines optimal estimation (OE) with Guide to the Expression of Uncertainty in Measurement (GUM)-based uncertainty propagation to explicitly account for terrain parameter uncertainties, enabling efficient and physically consistent characterization of surface reflectance uncertainty. Application to the Environmental Mapping and Analysis Program (EnMAP) hyperspectral data demonstrates that the method achieves comparable correction accuracy to established algorithms, while providing per-pixel, per-band uncertainty estimates. Validation is performed through cross-sensor comparison with SI-traceable Landsat 8 surface reflectance products across diverse land cover types and terrain conditions. The results confirm the reliability of the proposed uncertainty estimates and highlight their value for assessing cross-sensor reflectance consistency. Analysis further reveals that reflectance uncertainty generally follows a Gaussian distribution, and that slope uncertainty is the dominant driver of reflectance uncertainty. Overall, this work delivers a practical framework for uncertainty-aware surface reflectance retrieval in mountainous regions and establishes a pathway toward uncertainty-based interoperability of hyperspectral products across multiple sensors.