Dust Tracking Camera

On the surface of the Moon, lunar dust specifically presents unique challenges to operations long-term due to regolith particles’ ubiquitous presence in the lunar environment, potential to electrostatically charge and possible chemical reactivity. Whether to avoid exposure, try to remove or simply to tolerate lunar dust infiltrating a system becomes a complex question involving length of required service life, dust effects and critical risks, mass and complexity trades for the entire system. This publication provides a snapshot of advice from topical experts for specific areas of concern to systems targeted for deployment on the lunar surface. Following introductory overview commentary, dust mitigation approaches appropriate to the lunar surface are first addressed for typically static structures such as optical surfaces, radiators, and other thermal control surfaces, followed by regolith exposure concerns for communications equipment and non-optical sensors. The broad topic of mechanisms and mechanical assemblies is broken down to address relevant component level concerns, such as for bearings and for seals. “Soft goods” components of space suits (fabric) specific concerns are discussed, followed finally by brief coverage of human health issues and concerns, though the emphasis of this publication remains with components directly exposed to the harsh lunar surface environment. A description of some lunar surface hazard details, in particular characteristics of lunar surface dust, follows in Appendix A, and more detailed explanations of quantification issues for particulates are in Appendix B. The simple inertial removal of particles from a surface is discussed in Appendix C, followed by a summary of terrestrial best practices for dust mitigation recommended within select industries in Appendix D. The aggregated bibliography of references, while extensive and very useful, should not be construed to be exhaustive and can serve as a constructive start.

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NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

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