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.

Soil moisture (SM) is an important state variable in land surface models. Here, we investigate the potential of a ground-based global navigation satellite system receiver with two linearly polarized antennas that measure the interference power (IP) of direct and reflected signals in horizontal polarization (H-pol) and vertical polarization (V-pol) to estimate SM. The coefficient of determination between the IP waveforms at H-pol and V-pol ( $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ ) was used as a predictor of SM. A coherent specular reflection model was employed to first explore the relationship between $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ and SM for different values of soil roughness. That relationship was subsequently applied to estimate SM from $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ determined from global positioning system (GPS) signals acquired continuously by a ground-based receiver between May and December 2022 for an area with very smooth bare soil. The results show that the proposed method can estimate the SM of the upper 10-cm layer with high accuracy (with a root-mean-square error (RMSE) of approximately 1.5 vol.%) and demonstrate the potential of the ground-based IP technique as a practical system solution for proximal remote sensing of SM over bare soils .