Where is methane rising from paddies, livestock, and landfills near me?

What you can answer

• Regional XCH4 enhancement (column minus background) for any AOI from TROPOMI
• Persistent methane hotspots by compositing daily overpasses over weeks–months
• Paddy vs steady-source seasonality by comparing monsoon-flood vs dry-season windows
• Facility-scale plumes (landfills, large point sources) from EMIT 60 m imaging
• Multi-year drift using the GOSAT long record (2009+) for context

What you can NOT answer with these alone

• Surface emission flux in tonnes/yr — TROPOMI is a column, not a flux; conversion needs an inversion model with meteorology.
• Source separation within a pixel — at ~5.5 km, paddy + livestock + urban + landfill mix; needs EMIT/airborne or inventories to disaggregate.
• Cloudy / low-albedo days — retrievals drop out under cloud, haze and over dark/wet surfaces; flooded paddies themselves can lower retrieval quality.
• Cause vs accumulation — a high column under stagnant air may be trapped methane, not a local source, without transport analysis.

Code template

import earthaccess
import xarray as xr
import numpy as np

earthaccess.login(strategy="netrc")

# AOI: Gangetic plain (UP–Bihar)
aoi = (80, 25, 86, 28)

# Monsoon window (paddy flooding) vs dry window (steady sources)
windows = {
    "monsoon": ("2024-07-01", "2024-09-30"),
    "dry":     ("2025-01-01", "2025-03-31"),
}

xch4_comp = {}
for label, (start, end) in windows.items():
    granules = earthaccess.search_data(
        short_name="S5P_L2__CH4___",     # TROPOMI L2 CH4, ~5.5 km
        bounding_box=aoi,
        temporal=(start, end),
    )
    # Open methane_mixing_ratio_bias_corrected + qa_value from each granule
    # ds = xr.open_mfdataset(...) for the PRODUCT group

    # Keep only good retrievals (recommended qa_value > 0.5)
    # ds = ds.where(ds.qa_value > 0.5)

    # Regrid scattered L2 pixels onto a regular grid (e.g. ~0.05°),
    # then composite (median) over the window
    # grid = regrid_and_median(ds.methane_mixing_ratio_bias_corrected)

    # Background = low percentile over the AOI (clean-air reference)
    # bg = np.nanpercentile(grid, 10)
    # enhancement = grid - bg   # ppb above background
    xch4_comp[label] = "<gridded enhancement>"

# Seasonality discriminator:
#   paddy signal  ~ (monsoon enhancement) - (dry enhancement)  > 0
#   steady source ~ enhancement present in BOTH windows
# For facility-scale plumes, drop in EMIT scenes (EMITL2BCH4ENH /
# EMITL2BCH4PLM) over flagged landfill locations and inspect 60 m plumes.

Expected output

• XCH4 enhancement map (ppb above regional background) per season window
• Persistent-hotspot composite (pixels enhanced across the whole window)
• Monsoon-minus-dry difference map highlighting paddy-driven seasonality
• EMIT plume insets at landfill / point-source locations (60 m)
• Optional overlay of a bottom-up inventory (EDGAR) for sanity-checking

Caveats

• Column, not flux: high XCH4 can mean trapped air under stagnant meteorology, not local emission. Pair with winds before naming a source.
• Retrieval artefacts: albedo, aerosol and cloud bias XCH4 — always apply the qa_value filter (recommended > 0.5) and be wary over bright deserts, dark water and haze.
• Coarse pixels mix sources: at ~5.5 km, paddy, livestock and landfill emissions blend; treat TROPOMI as regional, and use EMIT/airborne to attribute facility plumes.
• Paddy retrievals are tricky: standing water lowers surface reflectance and can degrade retrieval quality exactly where paddy methane peaks — check pixel counts per window.
• EMIT is opportunistic: 60 m plume detection has limited, scheduled coverage and detects strong point sources, not diffuse area emissions like flooded fields.
• Verify before attribution: confirm hotspots against inventories (EDGAR), repeat overpasses, or airborne campaigns; don’t assign a single source from one coarse scene.

Cross-DAAC composition

TROPOMI/Sentinel-5P CH4 is distributed by GES DISC. EMIT plume/enhancement products (EMITL2BCH4ENH / EMITL2BCH4PLM) come from LP DAAC — a separate DAAC, so facility-scale analysis spans two auth contexts under the common Earthdata login.

Sources

• Sentinel-5P TROPOMI CH4: https://disc.gsfc.nasa.gov/datasets/S5P_L2__CH4____HiR_2/summary
• TROPOMI CH4 product readme (qa / bias correction): https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Methane-Product-Readme-File
• EMIT methane: https://earth.jpl.nasa.gov/emit/data/data-products/
• GOSAT/ACOS XCH4: https://disc.gsfc.nasa.gov/datasets?keywords=ACOS
• EDGAR emission inventory: https://edgar.jrc.ec.europa.eu/