Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments

Satellite-based fire detection provides critical data for fire management, fire spread modeling, air quality forecasts, and assessments of fire impacts on ecosystems and communities. Current fire detection algorithms, whether physics-based or machine learning (ML)-based, frequently fail when wildfires are obscured by dense clouds or smoke, creating data gaps that degrade the quality of air quality and fire emissions estimates. Part of the problem is the lack of training data for fires beneath clouds, and another part is the influence of legacy computational limitations on the approach for satellite data analysis. Separate data products for fire, clouds, and aerosols, rather than joint solutions that explicitly consider the influence of atmospheric conditions on attenuation of the thermal emissions from fire events, perpetuates this issue. These missing fire detections hamper our understanding of changing fire activity in response to climate warming and inhibit our ability to attribute aerosols and greenhouse gas emissions to fire activity. This proposed work aims to address these challenges by developing an explainable multitask ML model for fire detection and integrating it with cloud and aerosol retrieval. Our primary focus lies in addressing the specific challenges posed by the presence of clouds and dense smoke, where active fires are not consistently detected with current approaches. Our objective is to develop a multitask ML approach with the primary task focused on fire detection, aided by subtasks related to cloud and smoke aerosol retrievals. All tasks are collectively learned to facilitate situational awareness. We will build upon our prior research in physics-informed and explainable ML for 3D cloud reconstruction and extend it to include both cloud and smoke aerosol retrievals for enhancing active fire detection. In observation-constrained environments, where atmospheric interference increases uncertainty, understanding the rationale behind ML model predictions is essential for reliable fire detection under challenging cloudy and smoky conditions. To ensure physical, spatial, and temporal consistency, we will integrate explainable ML techniques with physics in the proposed multitask transformer architecture, ensuring the transparency of ML models and their consistency with the physics of fire activity and atmospheric radiative transfer. Additionally, we will utilize the attention mechanism to integrate temporal and spatial contextual information, including near-coincident measurements from geostationary (GEO) and low Earth orbit (LEO) satellites, as well as atmosphere profiles from reanalysis data. This will further enhance the accuracy of active fire detection under adverse atmospheric conditions by ensuring spatial and temporal consistency. The outcomes of this proposed project include: (1) a fire detection ML model developed through the joint learning of vertical cloud and aerosol distributions, (2) a representative fire dataset under cloudy and smoky conditions, and (3) an explainable ML-based approach with broader applications for the retrieval of other surface properties obscured by clouds and aerosols. Despite having a lower entry TRL, this proposed effort has the potential to be "game-changing" as it tackles critical challenges in wildfire monitoring and tracking under observation-constrained conditions. By integrating physical constraints into ML models and learning from multiple satellite observations and reanalysis data over time, this project bridges the gap between traditional physics-based models and data-driven ML techniques. The project's emphasis on explainability ensures the transparency of ML model's predictions and maintains spatiotemporal consistency with physical processes. The project will be carried out by an interdisciplinary team with expertise in ML, spaceborne fire detection, and satellite remote sensing of clouds and aerosols.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.