Python · hyperspectral imaging

HyperGas

Trace gases, read from light itself.

HyperGas reads, processes, and writes data from hyperspectral imagers, turning L1 radiance data into trace-gas concentration enhancements and emission-rate estimates.

Get started View on GitHub

conda install -c conda-forge hypergas EMIT · EnMAP · PRISMA

Supported instruments

One interface, every sensor

Reading is built on Satpy's hyperspectral readers, so new instruments plug in as they gain support.

EMIT

Earth Surface Mineral Dust Source Investigation, JPL / NASA.

emit_l1b

EnMAP

Environmental Mapping and Analysis Program, DLR, GFZ.

hsi_l1b

PRISMA

PRecursore IperSpettrale della Missione Applicativa, ASI.

hyc_l1

Processing levels

From radiance to emission rate

Each HyperGas run: radiance in, quantified emission rates out.

L1 · radiance L2 · denoised ΔX L3/L4 · plume mask + emission estimation

L1 radiance → L2 denoised trace-gas enhancement (ΔX) and automatic plume mask → L3/L4 emission estimation using the integrated mass enhancement (IME) and cross-sectional flux (CSF) methods.

Key features

What HyperGas does for you

RGB compositing

Combine multiple spectral bands into RGB images.

Trace gas retrieval

Retrieve enhancements for methane, carbon dioxide, and other trace gases.

Denoising

Clean retrieval outputs to isolate real plume signal from noise.

Flexible export

Save results as PNG, HTML, or CF-compliant NetCDF for downstream tools.

Plume detection

Semi-supervised detection of gas plumes with automatic plume mask generation.

Emission estimation

Estimate gas emission rates and write them out to CSV.

Get started

Install HyperGas and read your first L1 radiance in a few lines.

conda install -c conda-forge hypergas

See the quickstart guide and API reference for details.

Get involved

HyperGas is designed to make it straightforward to add trace-gas retrieval support for new HSI instruments.

Contributions and issue reports are welcome on the GitHub repository.

Research

Citation

If HyperGas contributes to your research, please cite the software and the associated publication. And don't forget to add your paper to the Publications page.

Software DOI 10.5281/zenodo.18154956

Recommended citation

Zhang, X., Maasakkers, J. D., de Jong, T. A., Tol, P., Reuland, F., Brandt, A. R., Kort, E. A., Adams, T. J., and Aben, I. (2026). HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification, Geosci. Model Dev., 19, 5979–6000, https://doi.org/10.5194/gmd-19-5979-2026.