GNSS-IR and spectral remote sensing data fusion for soil moisture estimation

Recent advancements in Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) have enabled the extraction of environmental data by analyzing differences between direct and multipath signals. A key application is soil moisture estimation using Signal-to-Noise Ratio (SNR) measurements of reflected signals. However, these methods only provide relative moisture estimates, requiring periodic in-situ measurements to establish the minimum moisture level for each observation period. On the other hand, optical remote sensing estimates soil moisture through pixel reflectance, clearing the need for in-situ measurements but is limited by sensitivity to weather and illumination conditions, cloud cover, ground vegetation cover, and a 3–5-day satellite orbit, hindering continuous estimation. This study further develops the potential of GNSS-IR for soil moisture estimation by introducing a novel, optimized approach to enhance accuracy. We also develop a data fusion model that combines GNSS-IR's continuous, weather-independent measurements with discrete estimates from spectral remote sensing Sentinel-2 imagery. This model enables continuous soil moisture estimation without in-situ measurements. We use datasets from Valencia, Spain, and Kabri, Israel, to evaluate the methodology. Our models achieve an accuracy relative to in-situ data of approximately 0.02 [m 3 /m 3 ] in soil moisture estimation, outperforming traditional methods, which have an accuracy of around 0.05 [m 3 /m 3 ].