q02·intermediate

Has methane increased near this oil/gas infrastructure?

atmospheregreenhouse-gasesenergy Datasets: 5 15–45 min depending on plume density
Find the data for your area

Draw a rectangle to pick your area of interest, then see what NASA data covers it (live, here in your browser) or download a ready-to-run notebook with your AOI pre-filled. The notebook runs in any Python environment — it needs a free Earthdata Login to fetch the data.

Current AOI: -103.1, 32.6 → -103, 32.7 (Permian Basin demo facility)

Has methane increased near this oil/gas infrastructure?

What you can answer

  • Whether NASA detected a methane plume over a specific lat/lon during a date window.
  • Plume source strength (kg/hr) from L2B Methane Enhancement data inverted with PBL height + wind.
  • Repeat detection rate at a given facility (how often EMIT overhead caught a plume).
  • Whether the plume cluster has changed in size or frequency over the operational period.

What you can NOT answer with these alone

  • 24/7 continuous monitoring — EMIT revisit at a given point is ~16 days, sometimes longer; you see snapshots, not surveillance.
  • Sub-60m attribution to a specific well/tank when multiple are co-located within an EMIT pixel.
  • Total facility emissions per year without auxiliary models filling EMIT data gaps.

Code template

import earthaccess
import geopandas as gpd
from shapely.geometry import box

earthaccess.login(strategy="netrc")

# 1. Define facility location + buffer
facility = (32.65, -103.05)  # example Permian Basin coords
buffer_deg = 0.05  # ~5 km
aoi = (facility[1]-buffer_deg, facility[0]-buffer_deg,
       facility[1]+buffer_deg, facility[0]+buffer_deg)

# 2. Search EMIT L2B methane plume product
plumes = earthaccess.search_data(
    short_name="EMITL2BCH4PLM",  # confirm short_name via CMR
    bounding_box=aoi,
    temporal=("2022-08-01", "2026-05-01"),
)

# 3. Open as COG, mask to AOI, compute total plume mass per detection
# 4. Pull MERRA-2 PBL height + 10m wind for the detection timestamps to invert source rate
# 5. Tabulate: date · plume mass · estimated source rate (kg/hr)
# 6. Plot: time-series of source rate at facility; map of plume polygons

Expected output

  • Time-series chart: estimated source rate (kg/hr) at the facility across EMIT detections
  • Map: overlaid plume polygons colored by detection date
  • Histogram: source-rate distribution

Caveats

  • EMIT is hyperspectral imagery, not a methane sensor per se — the L2B methane product is derived from radiance via matched filter; false positives exist (look for the QA flag).
  • Sub-pixel mixing of multiple plume sources within 60m is common in dense oilfield basins.
  • EMIT does not see at night (it’s a passive optical instrument).
  • The plume vector product (L2B PLM) lags the raw enhancement product by weeks; use L2B Enhancement (L2BCH4ENH) for the most recent data.
  • UNEP-IMEO MARS workflow validates EMIT detections manually before public release of plume polygons; if you’re using the public plume catalog you’re already getting QA’d data, but if you’re processing the enhancement product yourself, you’re not.

Cross-DAAC composition

This is a 2-DAAC + 1-external join: LP DAAC (EMIT) + GES DISC (MERRA-2) + TROPOMI (external, ESA but mirrored via Earthdata Search). Auth uniform via Earthdata Login. See recipes/r02-emit-merra2-fusion.mdx.

Sources

Datasets used

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