LIME: the Lunar Irradiance Model of the European Space Agency

Absolute calibration of Earth observation sensors is key to ensuring long term stability and interoperability, essential for long term global climate records and forecasts. The Moon provides a photometrically stable calibration source, within the range of the Earth radiometric levels, and is free from atmospheric interference. However, to use this ideal calibration source, one must model the variation of its disk integrated irradiance resulting from changes in Sun-Earth-Moon geometries.

LIME, the Lunar Irradiance Model of the European Space Agency, is a new lunar irradiance model developed from ground-based observations acquired using a lunar photometer operating from the Izaña Atmospheric Observatory and Teide Peak, Tenerife. Approximately 300 lunar observations acquired between March 2018 and October 2020 currently contribute to the model, which builds on the widely-used ROLO (Robotic Lunar Observatory) model.

This presentation will outline the strategy used to derive LIME. First, the instrument was calibrated traceably to SI and characterised to determine its thermal sensitivity and its linearity over the wide dynamic range required. Second, the instrument was installed at the observatory, and nightly observations over a two-hour time window were extrapolated to provide top-of-atmosphere lunar irradiance using the Langley plot method. Third, these observations were combined to derive the model. Each of these steps includes a metrologically rigorous uncertainty analysis.

Comparisons to several EO sensors will be presented including Proba-V, Pleiades and Sentinel 3A and 3B, as well as a comparison to GIRO, the GSICS implementation   of the ROLO model. Initial results indicate LIME predicts 3% - 5% higher disk integrated lunar irradiance than the GIRO/ROLO model for the visible and near-infrared channels. The model has an expanded (k = 2) absolute radiometric uncertainty of ~2%, and it is expected that planned observations until at least 2024 will further constrain the model in subsequent updates.