HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification

We present HyperGas, an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.