CYGNSS-Based Machine Learning Approaches for Predicting Wildfire Risk

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that uses reflected GNSS signals from the Earth's surface to monitor geophysical parameters. This research explores an innovative approach that leverages GNSS-R satellite data from the Cyclone GNSS (CYGNSS) together with machine learning techniques, to predict the Fire Weather Index (FWI). Derived from meteorological data to estimate fire danger, this index is widely adopted in climate research, yet its relationship with GNSS-R observations remains untapped.

For this experiment, we assembled a three-year dataset of CYGNSS parameters collected over a specific region. This dataset is used to build and challenge different machine learning models ranging from classic methods like regression/classification, Support Vector Machines (SVM) or ensemble techniques (like Decision Trees, Random Forest or XGBoost) to deep learning models such as Artificial Neural Network (ANN) using Multilayer Perceptron (MLP).

The results reveal that incorporating Delay Doppler Map (DDM) related parameters into the training dataset significantly enhances the predictive accuracy across most of the evaluated models. Moreover, we present a MLP implementation in which parameters such as DDM center and peak are identified as strong contributors, approaching the importance of their spatiotemporal counterparts.

The analysis demonstrates that machine learning techniques in general and deep learning models in particular can successfully be used to infer the FWI with an acceptable level of accuracy for wildfire risk assessment, offering very promising new research lines based on modern AI advanced techniques like attention mechanisms or transformer architectures.