Evaluating GNSS-T VOD sensitivity to plant water dynamics, rainfall interception, and dew in a coniferous forest

Abstract. Monitoring forest canopy water is essential for understanding drought response and interception losses under climate change. At short timescales, canopy water is partitioned among internal plant water storage (S_p), rainfall interception (S_i), and dew (S_d), which together regulate plant functioning, canopy evaporation, and precipitation partitioning, yet are rarely observed simultaneously. GNSS transmissometry (GNSS-T) has recently emerged as a low-cost, continuous, stand-scale method to observe L-band vegetation optical depth (VOD) from signal attenuation. However, interpreting GNSS-T VOD remains difficult because the signal integrates multiple water pools and is similarly affected by biomass, canopy structure, and measurement noise.

Here, we applied GNSS-T in a mature Picea abies stand in Tharandt, Germany, during the 2024 growing season to separate canopy water storage into S_i, S_d, and S_p. Rainfall interception was simulated with the multilayer Penman–Rutter model CanWat and used to calibrate the empirical VOD–water-storage relationship and to convert the GNSS-T noise floor into an equivalent storage detectability threshold. GNSS-T VOD tracked interception storage robustly and approximately linearly at both 30-min and event scales (R² = 0.63/0.79), and modeled S_i explained VOD variability better than gross precipitation alone. The inferred attenuation coefficient b was physically consistent with the reported L-band values but varied seasonally, indicating that time-varying calibration is preferable to a fixed relationship. Dew-related wetting signals were distinguishable in VOD and yielded plausible mean nightly amounts, but short event duration and high noise caused unrealistic extremes and limited detectability. Diurnal changes in internal plant water storage were not directly detectable at sub-daily scales, indicating that realized variations in S_p remained below the GNSS-T noise floor during the study period which we could show using trait-based estimates of expected maximum plant water loss under non-stressed conditions.

This storage-versus-noise framework provides a practical way to benchmark the hydraulic sensitivity of GNSS-T across sites that differ in biomass, hydraulic strategy, and climate, and further highlights noise reduction as a prerequisite for plant-hydraulic applications, especially in low-biomass ecosystems. This study further promotes GNSS-T VOD as a robust monitoring instrument for resolving sub-event interception storage, a potential avenue for constraining hydrological models.