Regular monitoring of plant development and soil moisture variations is essential for managing orchard systems and optimizing irrigation. Cosmic Ray Neutron Sensors (CRNS) are increasingly used for reliable, non-invasive soil moisture estimation. However, the potential of CRNS for monitoring plant development remains largely uninvestigated. The objective of this study is to assess the response of thermal (N_th) and epithermal (N_epi) neutron intensities to the seasonal changes in tree structure and water content. In particular, we aim to investigate whether the observed neutron responses can be used as an indicator of plant development in commercial orchard settings. A CRNS was installed at a cherry orchard site in southeastern France and operated continuously for 10 months in 2022. Observations were compared to several proxies for tree canopy characteristics. First, neutron intensity values were compared with monthly plant area index (PAI) estimates derived from images collected with a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV). PAI in (m² m⁻²) is defined as the total surface area of all above-ground canopy components, including leaves, stems, and branches per unit horizontal ground surface area. Second, N_th was compared with commonly used vegetation indices derived from multispectral satellite images acquired by PlanetScope and Sentinel-2. The results show a strong correlation between N_th and UAV-derived PAI with R² = 0.86. N_th increased linearly by approximately 4.5% per 1 m² m⁻² increase in PAI. Of the vegetation indices, the Normalized Difference Red Edge (NDRE) index derived from PlanetScope images showed the highest correlation (R² = 0.69) with N_th. The corresponding R² with NDRE from coarser-resolution Sentinel-2 data was lower (R² = 0.51). The correlation between N_th and PAI was higher than that between N_th and SM (R² = 0.61). Results suggest that variations in N_th are potentially valuable for vegetation monitoring, provided the confounding effect of soil moisture can be taken into account.

Soil moisture (SM) plays a central role in water cycle dynamics and land-atmosphere interactions, acting across local and regional scales. Few studies have explored the use of the ground-based global navigation satellite system reflectometry (GNSS-R) interference pattern technique (IPT) for SM estimation. In these studies, SM was estimated from the GPS elevation angle where lower reflectivity occurs (notch), which is difficult to determine in real GNSS-R interference power (IP) acquisitions. This study introduces the use of IP amplitude at vertical polarization (V-pol), readily extracted from the IP oscillations, as an alternative for SM estimation beneath vegetation cover. An empirical model was developed for estimating SM in irrigated grassland using a GNSS-R receiver with a linearly polarized antenna. The experiment, conducted between June 6 and August 8, 2022, covered the grassland's growth phase and preharvesting and postharvesting. The study incorporated normalized difference water index (NDWI) from the Sentinel-2 satellite to account for vegetation's impact on IP amplitude. Results indicated that the IP amplitude at V-pol accurately estimates SM (RMSE =0.04 m3/m3). Moreover, the results show that the vegetation layer mainly attenuates the IP amplitude with a nonsignificant scattered contribution to the IP, allowing for the simplification of the empirical model by ignoring the scattered contribution of vegetation. The simplified empirical model can be numerically resolved to estimate the NDWI if the SM is known. In summary, this study highlights the effectiveness of the ground-based IPT for close-range sensing of SM and a biomass proxy, such as NDWI.

Cosmic ray neutron sensor (CRNS) has gained popularity in the last decade for its suitability in estimating area-averaged soil moisture (SM). The presence of fresh biomass influences the CRNS signal due to its water content, introducing bias to soil moisture estimation. Calibration and correction methods have been developed to account for this bias, but they usually require laborious sampling. Here, a novel approach is tested to assess the impact of biomass water equivalent (BWE) on CRNS soil moisture estimation. It was conducted in two contrasting environments from 15/11/21–1/02/23 for an olive orchard in Saudi Arabia, and from 15/02/22–30/03/23 for a cherry orchard in France. Water-uptake rates were monitored using sap flow sensors, as well as actual evapotranspiration (AET) and in-situ SM within the CRNS footprint. Concurrent environmental variables were also measured with a research-grade weather stations. It was found that when vapor pressure deficit (VPD) > 1.8kPa, CRNS-derived SM (CRNS-SM) closely matched in-situ SM measurements, which indicates minimal influence from BWE. Conversely, when VPD is lower than 1.8kPa, CRNS-SM overestimates the in-situ moisture. An optimization approach was used to find a temporally-varying value of N₀ parameter that minimizes the difference between soil moisture estimated with CRNS and in-situ sensors. Furthermore, the results showed that the relative change in the optimized value of N₀ (N_0,opt) was well correlated with VPD in both orchards (R² = 0.66 for olive and R² = 0.74 for cherry orchards), indicating a strong correlation between these variables. These findings suggest that integrating VPD and CRNS observations, and using the VPD-N_0,opt correlation approach could be a promising way to account for the bias due to biomass dynamics on the estimation of area-averaged SM.