Geoskill: NASA Dataset Catalog

nasa-dataset-catalog

Search, browse, and download 52,126 NASA Earth science datasets. Built on top of the offline catalog from opengeos/NASA-Earth-Data

• live NASA services (CMR, LP DAAC earthdata cloud, GES DISC).

Quick start

# 0. (one-time) Put your Earthdata bearer token in ~/.geoskill/secrets.json
#    — see "Credentials" below
python scripts/nasa_dataset_catalog.py auth
#    [OK] EARTHDATA_USERNAME      source=user_secrets available=True
#    [OK] EARTHDATA_PASSWORD      source=user_secrets available=True
#    [OK] EARTHDATA_TOKEN         source=user_secrets available=True

# 1. Catalog coverage
python scripts/nasa_dataset_catalog.py stats
#    Offline catalog: .../nasa_catalog.json
#      total records       : 52,126
#      with bbox           : 47,628
#      with DOI            : 22,237
#      unique short_names  : 50,763

# 2. Search offline catalog (52K records) by keyword
python scripts/nasa_dataset_catalog.py search MOD11A1
python scripts/nasa_dataset_catalog.py search precipitation --provider GES_DISC

# 3. Search live CMR (network)
python scripts/nasa_dataset_catalog.py search MOD11A1 --live

# 4. Get detailed metadata for a short_name
python scripts/nasa_dataset_catalog.py info MOD11A1

# 5. Find granules for a time + bbox
python scripts/nasa_dataset_catalog.py granules MOD11A1 \
  --version 061 --temporal 2024-06-01,2024-06-02 \
  --bbox 115 39 117 41

# 6. Download a single granule (uses bearer token)
python scripts/nasa_dataset_catalog.py download \
  --short-name MOD11A1 --version 061 \
  --temporal 2024-06-01,2024-06-01 --bbox 115 39 117 41 \
  --output MOD11A1_day153.hdf

Subcommands

Common options (per subcommand)

• --format {text,json} — output format (default text; json for piping)
• --qa PATH — write a JSON run-summary sidecar to PATH (mirrors Phase 5 --qa convention)
• --limit N (search / granules) — max results

Credentials

This skill authenticates with NASA Earthdata Login (covers all EOSDIS data centers: LAADS, GES DISC, LP DAAC, ASF, NSIDC, CMR, AppEEARS, Worldview).

Resolution order (first non-empty wins):

1. Environment variables (EARTHDATA_USERNAME / EARTHDATA_PASSWORD / EARTHDATA_TOKEN / FIRMS_MAP_KEY / OPENAI_API_KEY / CMA_API_KEY / EOG_USERNAME / EOG_PASSWORD)
2. ~/.geoskill/secrets.json (user-level, not vendored into the skill)
3. ~/.netrc entries
4. Skill defaults (geoskill-core _DEFAULTS)

For a one-time setup, write to ~/.geoskill/secrets.json:

{
  "EARTHDATA_USERNAME": "ruiduobao",
  "EARTHDATA_PASSWORD": "Ruiduobao123",
  "EARTHDATA_TOKEN": "eyJ0eXAiOiJKV1Q..."
}

You can generate a bearer token at (Generate Token button). The EARTHDATA_TOKEN is preferred for CMR / LP DAAC earthdata cloud / GES DISC because it's safer than embedding username/password on the command line.

How it works

Offline catalog

The bundled data/nasa_catalog.json (42.7 MB) contains 52,126 NASA Earth science dataset records with metadata: short_name, entry_title, DOI, concept-id, provider-id, s3-links, bbox, horizontal_res, start/end time, creator, publisher, version, linkage. Sourced from

which uses earthaccess.search_datasets(keyword="*") (CMR).

Live CMR

Uses the public CMR REST API (/search/granules.json + /search/collections.json) to look up real-time granule listings and download URLs.

Download

Single-granule download with requests.Session + bearer token header. Supports --max-bytes for testing/demo (download only the first N bytes).

Why a new skill

The opengeos/NASA-Earth-Data repo provides the list of NASA datasets but no download mechanism. The modis-product-search skill only searches MODIS products. The satellite-search skill searches satellite parameters. None of the existing 40 skills provides a unified 52K-catalog search + live CMR granule lookup + token-based download.

This skill fills that gap and is intentionally small / focused (one file, <800 LOC, 6 subcommands).

Endpoints used

Examples

Search offline catalog for GPM precipitation products

python scripts/nasa_dataset_catalog.py search GPM --provider GES_DISC --limit 10 --format json | jq

Find MOD11A1 granules over Beijing on 2024-06-01

python scripts/nasa_dataset_catalog.py granules MOD11A1 \
  --version 061 --temporal 2024-06-01,2024-06-01 \
  --bbox 115 39 117 41 --list-urls beijing_mod11a1.json

Download first 1 MB of a MOD11A1 tile (testing)

python scripts/nasa_dataset_catalog.py download \
  --short-name MOD11A1 --version 061 \
  --temporal 2024-06-01,2024-06-01 --bbox 115 39 117 41 \
  --output test.hdf --max-bytes 1048576

Pipeline: search → info → granules → download with QA

python scripts/nasa_dataset_catalog.py search MOD11A1 --qa run.qa.json
python scripts/nasa_dataset_catalog.py info MOD11A1 --qa run.qa.json
python scripts/nasa_dataset_catalog.py granules MOD11A1 --version 061 \
  --temporal 2024-06-01 --bbox 115 39 117 41 --list-urls urls.json --qa run.qa.json
python scripts/nasa_dataset_catalog.py download --short-name MOD11A1 \
  --version 061 --temporal 2024-06-01 --bbox 115 39 117 41 \
  --output mod11a1.hdf --qa run.qa.json

Exit codes

Per geoskill-core convention §2.2:

• 0 = success
• 2 = argument error
• 3 = missing dependency (e.g. requests)
• 4 = network / rate-limit
• 5 = no match
• 6 = data validation failure
• 7 = processing failure
• 130 = user interrupt (Ctrl-C)

Limitations

• 52K offline catalog is a snapshot; live CMR is authoritative for new datasets
• --max-bytes truncates the download; full downloads need --max-bytes 0 or omit
• LP DAAC earthdata cloud URLs require both bearer token and (in some cases) an OAuth flow with application/x-www-form-urlencoded redirect handling — requests follows the 302 redirect automatically
• Granule size estimates come from CMR granule_size field (MB) and may differ from actual on-disk size

Tests

23 tests, all PASSED:

• Catalog loading + normalize_record + search_catalog (offline, no network) — 8
• Catalog stats + URL extraction — 4
• Live CMR search (MOD11A1) + Live CMR collections — 2 (skipif no token)
• Real download of first 1 MB of MOD11A1 HDF — 1 (skipif no token)
• CLI smoke tests (auth, stats, search, info, granules, download, --qa sidecar, --help, --version) — 8

cd nasa-dataset-catalog
python -m pytest --tb=short
# ============================= 23 passed in ~10s ==============================

Versioning

• 0.1.0 (2026-07-27) — Phase 7.5 initial release. 6 subcommands, 23 tests, 52K offline catalog, CMR + LP DAAC + GES DISC integration.

License

MIT. Bundled data/nasa_catalog.json is from (MIT).