PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimates

2026-07-15

AI Summary

This study addresses the significant uncertainty in existing methane plume products, which rely on scalar parameters—such as integrated mass enhancement (IME), plume length, and emission rate—to retroactively infer plume masks, yet fail to uniquely determine plume boundaries. For the first time, this work explicitly reveals the mask equivalence problem and proposes a CM-like mask generation method capable of reproducing key emission parameters without dependence on the original mask. Leveraging a genetic algorithm, the approach constructs an ensemble of masks satisfying scalar constraints and establishes a multi-representation consistency evaluation framework through IME computation, fivefold uncertainty decomposition, intersection-over-union (IoU) assessment, and scene-agnostic strategies. Leave-one-out validation shows that CM-like masks achieve mean absolute errors of 7.6%, 9.5%, and 6.1% for IME, length, and emission rate, respectively, with a median IoU of 0.843, effectively identifying weak or ambiguous plumes for expert review.

Abstract

Imaging spectrometers increasingly distribute source-resolved methane plume products in which the plume mask, integrated mass enhancement (IME), plume length, emission rate, and uncertainty are physically and algorithmically linked. Using 63 EMIT-derived Carbon Mapper plume records from 27 scenes, we show that these published scalar quantities do not uniquely constrain the plume boundary: substantially different yet plausible masks reproduce the same IME, plume length, and emission rate. Genetic-algorithm (GA) ensembles conditioned on the published IME and plume length make this equifinality explicit: the high-confidence core selected by nearly all target-consistent masks covers a median of 13% of the plausible footprint envelope, and ambiguity is largest for weak, low-overlap plumes. The diagnostics come from PlumeQuant, which recomputes IME, plume length, emission rate, and five-term uncertainty from distributed product components under stated conventions and evaluates four mask representations: the distributed reference mask, a transparent Carbon Mapper-informed analogue (CM-like), the GA ensemble, and optional expert edits. The CM-like mask is generated per plume without access to the reference mask or published quantities, with settings fixed once on a scene-disjoint 44-plume development split. It reproduced published IME with +0.72% median difference and emission rate with +0.16% (6.98% mean absolute), reached 0.843 median intersection-over-union against the reference masks, and matched the published uncertainty scale (median ratio 1.01). Holdout mean absolute errors were 7.6% (IME), 9.5% (length), and 6.1% (rate). These are product-level consistency diagnostics, not independent validation. They flag weak, offset, or ambiguous plumes for expert review.

Problem

• methane plume
• plume mask
• emission-rate estimation
• uncertainty
• equifinality

Innovation

• PlumeQuant
• methane plume uncertainty
• genetic algorithm ensemble
• equifinality
• consistency diagnostics