The Environmental Mapping and Analysis Program (EnMAP) satellite carries a hyperspectral instrument that is able to measure methane (CH<sub>4</sub>) plumes of localized hotspot sources from which facility-scale emission rates can be estimated. Here, we implement and evaluate three retrieval techniques for scenes with different complexity and source strengths and discuss the differences and implications for emission estimation. These techniques are (i) RemoTeC, a physics-based retrieval that relies on pixel-wise radiative transfer calculations; (ii) a matched-filter retrieval that exploits the spectral covariance of a scene to detect CH<sub>4</sub> enhancements; and (iii) a hybrid retrieval that incorporates spectral covariance information into RemoTeC, thereby combining the strengths of approaches i) and ii).</p> <p>RemoTeC and the hybrid method yield similar emission estimates, with the hybrid method exhibiting lower statistical noise. The enhancements retrieved by the matched filter have the lowest statistical noise. For sources with emission rates larger than ∼ 3 t h<sup>−1</sup>, the matched filter yields source strength estimates similar to the physics-based retrievals. For weaker sources, the matched filter estimates larger emission rates than the physics-based retrievals, with deviations being on the order of the emission rate itself. While the matched filter effectively suppresses regular and recurring spectral albedo structures, its performance degrades in the presence of statistically rare spectral features. In contrast, the hybrid retrieval more reliably accounts for such albedo-induced artifacts.</p> <p>Our results demonstrate that retrieval methodology can significantly influence methane emission estimates, particularly in challenging scenes. The matched filter is well suited for rapid quantification of strong emission sources, whereas the physics-based approaches provide greater robustness under difficult observational conditions and for weak emitters. The hybrid retrieval offers the best overall performance by combining the mechanistic rigor of radiative transfer modeling with the noise-reduction benefits of covariance-based methods.