juliensimon/esa-mars-express-observations

ESA Mars Express Observations

Part of the Planetary Science Datasets collection on Hugging Face.

Complete observation metadata catalog from the ESA Mars Express mission — 1,658,416 observations across 8 instruments, covering over two decades of Mars exploration since the spacecraft entered orbit in December 2003.

Dataset description

Mars Express is a European Space Agency mission that has been studying the Martian atmosphere, surface, subsurface, and space environment since 2003. It carries a suite of instruments including a high-resolution stereo camera (HRSC), radar sounder (MARSIS), infrared imaging spectrometer (OMEGA), atmospheric spectrometers (PFS, SPICAM), plasma analyzer (ASPERA-3), radio science experiment (MaRS), and visual monitoring camera (VMC).

This dataset contains the full observation metadata from the ESA Planetary Science Archive (PSA), conforming to the EPN-TAP standard. Each row represents one observation or data granule, with timing, spatial coverage, instrument parameters, and access URLs.

Mars Express has been one of the most scientifically productive Mars missions ever flown. HRSC has produced the most complete high-resolution stereo topographic map of Mars, essential for geological mapping and landing site characterization. MARSIS, a subsurface radar sounder, detected reflections consistent with liquid water beneath the south polar layered deposits — a finding that reshaped understanding of present-day Mars hydrology. OMEGA mapped the global mineralogy of the Martian surface, identifying phyllosilicates, sulfates, and other aqueous alteration minerals that constrain the planet's climatic history. SPICAM and PFS have tracked the seasonal behavior of water vapor, ozone, and dust in the Martian atmosphere across multiple Mars years.

The longevity of the mission — over two decades in Mars orbit — makes this observation catalog uniquely valuable for studying long-term variability. It captures multiple complete Martian years of atmospheric monitoring, seasonal polar cap evolution, and dust storm cycles, including the planet-encircling dust event of 2018. The temporal baseline also enables detection of surface changes such as fresh impact craters, slope streaks, and CO2 geyser activity at the south pole. Cross-referencing observations from different instruments at the same location and time supports multi-wavelength analysis that no single instrument could achieve alone.

Instruments

• ASPERA-3: 675,880 observations
• HRSC: 265,020 observations
• VMC: 233,925 observations
• MaRS: 231,219 observations
• PFS: 88,872 observations
• SPICAM: 70,653 observations
• MARSIS: 56,980 observations
• OMEGA: 35,867 observations

Schema (key columns)

Column	Type	Description
granule_uid	string	Unique observation identifier
granule_gid	string	Group identifier
obs_id	string	Observation ID
dataproduct_type	string	Data product type (e.g. spectrum, image, profile)
target_name	string	Target body (Mars, Phobos, Deimos, etc.)
target_class	string	Target class (planet, satellite, etc.)
instrument_name	string	Instrument name (HRSC, MARSIS, OMEGA, etc.)
time_min	float64	Observation start time (JD)
time_max	float64	Observation end time (JD)
c1min/c1max	float64	Spatial coordinate 1 range
c2min/c2max	float64	Spatial coordinate 2 range
spectral_range_min/max	float64	Spectral range bounds
processing_level	string	Data processing level
creation_date	string	Data product creation date
access_url	string	URL to access the data product
access_format	string	Data format (e.g. application/x-pds)

The full schema contains up to ~50 columns following the EPN-TAP standard.

Quick stats

• 1,658,416 total observations
• 8 instruments
• 2 distinct targets
• Time span: JD 2452690.3 – 2460964.2

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/esa-mars-express-observations", split="train")
df = ds.to_pandas()

# Observations per instrument
print(df["instrument_name"].value_counts())

# HRSC images
hrsc = df[df["instrument_name"] == "HRSC"]
print(f"{len(hrsc):,} HRSC observations")

# Timeline of observations
import matplotlib.pyplot as plt
df["year"] = ((df["time_min"] - 2451545.0) / 365.25 + 2000).astype(int)
df.groupby(["year", "instrument_name"]).size().unstack().plot(kind="bar", stacked=True)
plt.title("Mars Express observations per year")
plt.ylabel("Count")

Data source

ESA Planetary Science Archive — EPN-TAP service at https://psa.esa.int/psa-tap/tap/sync.

Update schedule

Weekly (Monday at 07:00 UTC) via GitHub Actions.

Related datasets

• mars-craters — Robbins Mars crater catalog
• exoplanet-archive — NASA Exoplanet Archive
• lunar-craters — Lunar crater catalog

Pipeline

Source code: juliensimon/space-datasets

Support

If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐️ to the space-datasets repo.

Citation

@dataset{mars_express_observations,
  author = {Simon, Julien},
  title = {ESA Mars Express Observations},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/esa-mars-express-observations},
  note = {Based on ESA Planetary Science Archive (PSA) EPN-TAP service}
}

License

CC-BY-4.0