Distributed Spacecraft with Heuristic Intelligence to monitor Wildfire Spread for Responsive Control

We propose to develop and verify a space-based distributed observing system to improve wildfire response decisions by monitoring and forecasting fuel flammability and wildfire spread and providing on-demand fire danger and burnt area maps. The system will use Global Navigation Satellite System Reflectometry (GNSS-R) within an intelligent, adaptive sensing framework, that leverages mid-TRL tools like D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) and WRFx employing WRF-SFIRE (Weather Research and Forecasting Fire Spread Model). GNSS-R provides microwave data that can pierce through clouds/smoke/canopy, has a small form factor allowing for scalable constellations, and hence frequent data products during active fires than MODIS and VIIRS (12-24 hours). D-SHIELD is a software tool suite for optimal, ground-based, observation planning for a constellation of spaceborne instruments informed by dynamic scientific objectives (AIST-18). WRFx is an integrated fire and smoke decision support tool (AIST-21). Resultant data products will be used to enhance existing USGS fire danger products and the USGS-supported LANDFIRE fuel layers product. We will develop/enhance five products using GNSS-R data from CYGNSS (7-satellite NASA mission) and Spire Global (commercial fleet) and assimilate into WRFx. GNSS-R has shown successful Soil Moisture (SM) retrievals for weather and flood forecasting, but active fire applications have been limited. 1/ Improved SM retrieval accuracy and resolution via an automated calibration strategy around active fire regions, enabled by new physics and machine learning based retrieval models 2/ Dynamic burnt-area maps (BAMs) from high-resolution Delay Doppler Maps; Will improve the ?absence of fire? component of WRFx, 3/ & 4/ Enhanced USGS Wildfire Fire Potential Index and Wildfire Large Fire Probability fire danger products using the SM from #1; will improve fire prediction 5/ High cadence LANDFIRE fuel layer products during active fires; will provide the fire simulator community access to updated fuel layer data. #3, #4, #5 leverages the popularity of USGS and LANDFIRE products such that public can access GNSS-R data enhanced fire products without the need for development of new interfaces. The WRFx system will be revised to process the new data products: SM, BAM. A new emissions module will be developed to replace the current simplistic one within WRF-SFIRE. The developed data products and new module is expected to improve WRFx fire prediction, which will inform observation planning and fire management via the 2 frameworks that we will develop: A/ Observation Value Framework, to quantify observation priorities based on different dynamic objectives such as improving WRFx prediction quality, field campaigns in populous areas B/ Fire Forecast Reporter to improve situational awareness and support ongoing wildfire field campaigns using improved fire growth expectations, smoke dispersion, visibility predictions A critical component of the proposed system is the intelligence to dynamically task the observing (satellites) and planning (ground stations) assets, and synchronization between them. GNSS-R satellites can switch to a high-resolution data gathering mode and have a programmable uplink-downlink with customizable data priorities. We will significantly enhance D-SHIELD planner for wildfire applications using satellites like CYGNSS to capture a desired number of specular locations at resolutions and priorities informed by frameworks A-B. System responsiveness depends on runtime of computational components, number of satellites and ground stations. Agile technology beyond what is currently available will be simulated and the utility vs cost evaluated to inform future GNSS-R missions. Increased utility by adding responsively tasked GNSS-R data will be verified against nominal WRFx forecasts, and nominal USGS and LANDFIRE products

Enhances the capabilities for existing science instruments for monitoring pre-fire, active-fire, and post-fire situations, reduces the power and mass of these instruments, and enables unprecedented observations in support of wildfire science through distributed observing systems and the information technologies needed for their support.

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