Distributed Spacecraft with Heuristic Intelligence to Monitor Wildfire Spread
This project will develop an adaptive, intelligent observation strategy that aims to improve wildfire response decisions by using Global Navigation Satellite System Reflectometry (GNSS-R) measurements to develop/enhance data products for fire danger estimation and active fire simulations. The system will demonstrate that autonomous tasking of satellite observations and downlink can optimize data collection in operational fire modeling systems and provide important tools needed for monitoring wildfires in near real time.
GNSS microwave signals have been successfully processed to produce soil moisture (SM) estimates. Raw GNSS-R data from missions like NASA’s Cyclone GNSS (CYGNSS) could also be used to produce vegetation water content (VWC) and burned area maps (BAMs). These data products can be incorporated into existing fire modeling systems to provide improved assessments of pre-fire (USGS Fire Danger products), Live Fuel Moisture and active-fire environments (WRFx—an integrated fire and smoke decision support tool).
D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions), a software tool suite for optimal observation planning for a constellation of spaceborne instruments, will be enhanced to demonstrate the utility of autonomously re-tasking GNSS-R satellites to make time-relevant science measurements that aid wildfire response efforts. D-SHIELD will also simulate the ground asset coordination required to provide actionable data products to wildfire responders.
- Dynamic BAMs will be generated from high-resolution GNSS-R data and integrated into WRFx wildfire predictor.
- Joint retrieval of SM and VWC will be enabled by new physics and machine learning-based retrieval models, providing higher accuracy compared to state-of-the-art SM retrievals from GNSS-R.
- USGS Fire Danger products will be updated with improved SM and VWC measurements to enhance fire prediction.
- The WRFx model will be updated with an improved emissions module to increase the fidelity of the Air Quality (AQ) forecasts.
Sreeja Roy-Singh is a Senior Research Scientist and Principal Investigator at the Bay Area Environmental Research Institute within NASA Ames Research Center. She has led project D-SHIELD (Distributed Spacecraft with Heuristic Intelligence Enabling Logistical Decisions) since it was an initial concept in 2016 to a functional, open-source software suite for optimizing observation and data delivery planning for any constellation of spaceborne instruments, informed by dynamic scientific objectives that guarantee responsiveness to evolving phenomena. D-SHIELD as a system, or components thereof, has been applied in simulation to spaceborne observations of events such as coral reefs, precipitation and urban floods, cyclones, and rapid chances in soil moisture; and has been funded by NASA’s NIP and AIST programs.
Sreeja completed her PhD from the Department of Aeronautics and Astronautics at Massachusetts Institute of Technology, Cambridge, USA. She also leads the Systems Engineering team at Nuro, a Silicon Valley start-up that is building and deploying safe, self-driving robotic fleets for public roads. Her research interests include distributed space systems, space robotics for Earth observation, space traffic management, and vehicular robotics validation.