Monitoring Crop Structure and Moisture Using GNSS Interferometric Reflectometry Based on SNR Modeling

Abstract

This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.

Plant phenotyping relevance

GNSS-IRによる作物構造・水分の推定手法が研究の中心であり、植生高やバイオマス密度などの植物形質を地上測定と比較して技術検証している。

Code and data availability

The supplied blocks describe GNSS-IR field campaigns, Sentinel-2 NDVI processing in GEE, and self-developed R routines, but contain no public data deposit, code repository, or availability statement with an authors' URL. The only supplementary mention (Table S1) is a track-count table, not a qualifying dataset. No code

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