An Open Database of Lunar Regolith and Simulants Properties

Lunar regolith, the layer of unconsolidated material covering the Moon’s surface, is central to the science and technology developed for the Moon, notably related to in-situ science investigations, resource utilization, surface infrastructure, and mobility systems. However, data on lunar soil properties remain fragmented across decades of mission reports, often in formats that are difficult to access or interpret. We present a newly compiled database of lunar regolith physical and geotechnical properties, including data collected by direct in-situ measurements from crewed missions, estimates inferred from surface interactions on the Moon and using remote sensing, as well as laboratory analyses of samples returned to Earth. The data collected include, among others, the angle of internal friction and cohesion (both Mohr-Coulomb model parameters), bulk density, and static bearing capacity, extracted from Luna and Apollo-era historical mission documentation all the way to contemporary Lunar programs. The dataset specifies the type and location of the tests from which each value was obtained. Our database also includes parameters for lunar regolith simulants, providing a direct link between mission data and laboratory studies. In addition to centralizing this information, we developed a user interface that facilitates data retrieval, filtering, and visualization. This interface enables users to generate customized plots for comparative analysis. Developed in an open-science perspective, it is designed to evolve in response to the community’s needs. The database and its associated tools significantly enhance the accessibility and usability of lunar regolith and simulants data for scientific and engineering research.

💡 Research Summary

The paper presents an openly accessible, web‑based database that consolidates physical and geotechnical properties of lunar regolith and its simulants. Recognizing that data on the Moon’s surface material are scattered across decades of mission reports, scientific articles, and archived documents—often in incompatible formats—the authors systematically collected, curated, and standardized these information sources. Their dataset spans from the earliest Luna 9 and Surveyor missions (mid‑1960s) through Apollo, Luna sample‑return missions, up to recent Chinese (Chang’e 5, Chang’e 6) and Indian (Chandrayaan‑3) missions, covering the period 1966‑2025.

Each entry in the database is richly annotated with metadata: mission name, testing method, geographic coordinates on the Moon, depth of measurement, normal‑stress range, and bibliographic reference. The authors distinguish three measurement contexts—“in‑situ” (direct measurements on the lunar surface), “on‑Earth” (laboratory tests on returned samples), and “remote” (derived from remote‑sensing instruments). Test methods are classified into categories such as spacecraft touchdown analysis, vernier‑thruster plume interaction, trench tests, optical assessments, penetrometer insertions, rover‑track and footprint analyses, core‑tube/drill‑core extraction, and laboratory shear tests (direct, triaxial, rotational).

Core geotechnical parameters included are the Mohr‑Coulomb model’s internal friction angle (φ) and cohesion (c), bulk density, bearing capacity, normal‑stress range, porosity, void ratio, grain‑density, and compressibility coefficient. When multiple values exist for the same parameter, mission, and method, all are retained, allowing users to trace the evolution of interpretations over time.

The database is delivered via a Streamlit web application (https://lunar‑regolith‑database.streamlit.app/) that requires no special software beyond a modern browser. Users can filter by mission, method, depth, or parameter, generate interactive Plotly visualizations, and download the filtered data in CSV or JSON format. The full source code and raw data are hosted on GitHub (https://github.com/leoniegasteiner/Lunar‑Regolith‑Database), enabling local deployment with Python 3.9‑3.12 and standard libraries (pandas, numpy, plotly, etc.).

In addition to lunar regolith data, the database includes a dedicated section for lunar regolith simulants. For each simulant, the repository records the developer, year of creation, type (mare or highland), and the same set of mechanical parameters measured in the laboratory. This parallel structure facilitates direct comparison between “Moon‑truth” and “simulant‑truth,” supporting the development and validation of new simulant formulations.

To ensure scientific integrity, the authors impose strict inclusion criteria: data must be published in peer‑reviewed, non‑predatory journals, and the testing methodology must be clearly described. Contributors wishing to add new entries must provide detailed protocols if the original publication lacks them. The paper outlines a community‑driven model for future updates, encouraging researchers to submit new measurements from upcoming missions (e.g., Artemis, Lunar Gateway) or novel simulant studies.

Three illustrative use‑cases demonstrate the database’s utility: (1) rover designers can extract rover‑track‑derived friction angles to inform wheel geometry and traction control; (2) landing‑site engineers can assess bearing capacity from touchdown‑analysis data to size landing pads and predict settlement; (3) simulant developers can quantify discrepancies between simulant and actual regolith properties, guiding material formulation.

The authors acknowledge current limitations, such as sparse data for polar regions, temperature‑dependent behavior, and long‑term cyclic loading, and they propose systematic incorporation of future mission results to fill these gaps. By providing a transparent, reproducible, and extensible data platform, the work lays a foundational infrastructure for lunar science, in‑situ resource utilization, habitat construction, and mobility system design—key components of the emerging era of sustained lunar exploration.