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This research aims to accurately retrieve soil moisture and vegetation optical depth from GNSS-R data using deep learning methods.

January 22, 2026Open Access

A New Joint Retrieval of Soil Moisture and Vegetation Optical Depth from Spaceborne GNSS-R Observations

Key Points

This research aims to accurately retrieve soil moisture and vegetation optical depth from GNSS-R data using deep learning methods.
Utilized a deep learning framework based on an artificial neural network (ANN) for retrieval.
Input features included GNSS reflectivity, incidence angle, total precipitation, soil temperature, and other soil properties.
Focused on simultaneous retrieval across the contiguous United States (CONUS).
Achieved correlation coefficients of R = 0.83 for soil moisture and R = 0.89 for vegetation optical depth.
Demonstrated RMSE values of 0.063 m3/m3 for soil moisture and 0.088 for vegetation optical depth.
Predicted maps show strong agreement with reference maps, achieving R ≈ 0.93.

Abstract

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m3/m3 and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks.

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