Advancing Global Vegetation Water Content Estimation with GNSS-R and Deep Learning

Vegetation water content (VWC) is a critical parameter for understanding Earth’s ecological and hydrological systems, especially as climate change accelerates and extreme events become more frequent. Existing remote sensing methods for monitoring VWC face limitations due to restricted spatiotemporal coverage, soil moisture interference, and poor cloud penetration capability. To address these challenges, this study explores the synergy between unprecedentedly large datasets enabled by GNSS Reflectometry (GNSS-R) constellations and advanced deep learning algorithms for VWC estimation.

We propose a triple-collocated CGS dataset that integrates measurements from Cyclone GNSS (CYGNSS), Global Land Data Assimilation System (GLDAS), and Soil Moisture Active Passive (SMAP). Several deep learning models are benchmarked and evaluated over a three-year timespan. Validation results against ground truth measurements demonstrate robust performance with a minimum root mean square deviation (RMSD) of 1.099 kg/m². Moreover, predictive uncertainty is quantified using the Monte Carlo dropout method, providing a trustworthy representation for timely applications where ground truth data are unavailable.

Our study highlights the potential of combining GNSS-R with deep learning to address vegetation monitoring gaps. By leveraging the proposed CGS scheme and its large-scale dataset, we aim to catalyze further algorithmic advancements in GNSS-R-based vegetation monitoring and enhance the utility of GNSS-R for environmental applications.