HyGAS: an Open, Sensor-Agnostic Platform for Multi-Satellite Methane Plume Retrieval, Uncertainty Propagation, and Emission-Rate Estimation

TL;DR

HyGAS is an open, sensor-agnostic framework for standardized, uncertainty-aware methane plume detection and emission estimation across multiple satellite sensors, supporting operational robustness and inter-sensor comparability.

Contribution

It introduces HyGAS, a novel open platform that standardizes methane processing for various hyperspectral satellites, enabling consistent and reliable emission monitoring.

Findings

Supports end-to-end methane processing for multiple satellites.

Explicitly propagates uncertainty from instrument noise to emission estimates.

Improves multi-sensor comparability with scale-aware segmentation.

Abstract

The rapid expansion of spaceborne methane observing capabilities at the facility-scale (fostered both by public missions and commercial constellations) has created a need for harmonised, reproducible, and uncertainty-aware processing chains that support both monitoring workflows and fair inter-sensor comparisons. This paper presents HyGAS (Hyperspectral Gas Analysis Suite), an open and sensor-agnostic framework that standardises methane processing across multiple imaging spectrometers. HyGAS currently supports end-to-end processing from Level-1 radiance to methane enhancement for PRISMA, EnMAP, and Tanager-1, and it supports ingestion of Level-2 methane enhancement products from EMIT and GHGSat, which are subsequently processed through common downstream modules for background selection, plume segmentation, Integrated Mass Enhancement (IME), and emission-rate inversion. HyGAS prioritises operational robustness via (i) matched-filter variants designed to mitigate background heterogeneity and pushbroom artefacts, (ii) explicit decomposition and propagation of uncertainty from instrument noise and scene-driven clutter to IME and flux, and (iii) a scale-aware segmentation strategy defined in physical units and rescaled by ground sampling distance to improve multi-sensor comparability. Representative sample outputs are reported for PRISMA, EnMAP, and Tanager-1. Keywords: Methane emissions, hyperspectral satellites, Tanager-1, PRISMA, EnMAP, GHGSat, EMIT, Tanager, oil and gas, landfills, remote sensing, atmospheric science, greenhouse gas monitoring, spectral analysis, emission quantification, satellite synergy, environmental monitoring.