Through ESTO’s IIP program we are developing a small, low-cost backscatter lidar in pursuit of a future commercial free-flyer minisat that will permit dispersed constellation-style lidar measurements with unprecedented spatiotemporal sampling and real-time data product availability. Backscatter lidar is well suited for detection of aerosols, including aerosol plumes, plume boundaries, and internal structure of aerosol layers. Obtaining information about the vertical distribution of aerosols in real time is essential to improve air quality forecasting and hazardous plume monitoring, which mitigates the impacts of these events through public warnings and notifications.
Spaceborne lidar instruments to-date, and most currently being proposed, have been large and no spaceborne lidar has been packaged within minisat size and cost constraints. Reducing resource requirements for lidar sensors continues to present a challenge because measurement accuracy is driven by power-aperture product. We are applying advanced processing algorithms that can more effectively sift through noise to identify signal thereby reducing the power-aperture product without compromising performance. Key to future commercial success is employing machine learning algorithms to enable direct input to predictive models (for air quality and human health) and generating actionable real-time data products for decision making (for hazardous plume detection and monitoring, and security applications). The goal of our Global Orbital Research with a Diurnal Observing Network (GORDON) effort is provide conclusive, quantified demonstration of an integrated instrument package that is fully scalable to space.