MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT

methaneset-emit provides analysis-ready hyperspectral data from the EMIT imaging spectrometer (ISS) for methane plume detection and quantification. Each sample contains the full L1B radiance cube (285 bands, 381–2493 nm) in sensor geometry, three pre-computed retrieval products (matched filter, robust matched filter, and mag1c concentration in ppm·m), dual plume segmentation masks from UNEP-IMEO and CarbonMapper analysts, Copernicus DEM GLO-30 elevation, and observation geometry (SZA, VZA, AMF, azimuth angles). Unlike multispectral MethaneSET subsets that require temporal image pairs, EMIT enables single-acquisition detection via direct spectral absorption fitting.

Dataset Information

Version: 1.0.0

License: CC-BY-4.0

Keywords: methane, hyperspectral, EMIT, ISS, imaging-spectroscopy, matched-filter, plume-detection, segmentation, retrieval, remote-sensing, earth-observation, deep-learning

Tasks: segmentation, regression

Usage

Python

# pip install tacoreader
import tacoreader

ds = tacoreader.load("methaneset-emit.tacozip")
print(f"ID: {ds.id}")
print(f"Version: {ds.version}")
print(f"Samples: {len(ds.data)}")

R

# Coming soon: R support is planned but not yet available
# install.packages("tacoreader")
library(tacoreader)

ds <- load_taco("methaneset-emit.tacozip")
cat(sprintf("ID: %s\n", ds$id))
cat(sprintf("Version: %s\n", ds$version))
cat(sprintf("Samples: %d\n", nrow(ds$data)))

Julia

# Coming soon: Julia support is planned but not yet available
# using Pkg; Pkg.add("TacoReader")
using TacoReader

ds = load_taco("methaneset-emit.tacozip")
println("ID: ", ds.id)
println("Version: ", ds.version)
println("Samples: ", size(ds.data, 1))

Data Providers

NASA JPL — producer

UNEP IMEO — producer

CarbonMapper — producer

Hugging Face — host

Dataset Curators

Name	Organization	Email
Cesar Aybar	Universitat de València, Image and Signal Processing (ISP) Group	cesar.aybar@uv.es

Publications & Citations

If you use this dataset in your research, please cite:

DOI: 10.5067/EMIT/EMITL1BRAD.001

Green, R. O., et al. (2023). EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001. NASA Land Processes DAAC.

EMIT L1B calibrated radiance product used as source imagery.

DOI: 10.5194/amt-17-1333-2024

Roger, J., Guanter, L., Gorroño, J., & Irakulis-Loitxate, I. (2024). Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers. Atmospheric Measurement Techniques, 17, 1333–1346.

Wide/robust matched filter method used for RMF retrieval product.

DOI: 10.5194/amt-14-2771-2021

Foote, M. D., et al. (2020). Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6480–6492.

mag1c retrieval algorithm for calibrated ppm·m concentration estimates.

DOI: 10.48550/arXiv.2411.15452

Vaughan, A., Mateo-Garcia, G., et al. (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.

MARS operational system providing IMEO plume masks.

BibTeX

@dataset{methaneset-emit1,
  title = {MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT},
  author = {Cesar Aybar},
  year = {2024},
  version = {1.0.0},
  publisher = {Universitat de València, Image and Signal Processing (ISP) Group}
}

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