Exploring the use of thermal neutron counts to track orchard phenological development
Samer K. Al-Mashharawi (TU Delft - Mathematical Geodesy and Positioning, King Abdullah University of Science and Technology)
Susan C. Steele-Dunne (TU Delft - Geoscience and Remote Sensing)
Marcel M. El Hajj (King Abdullah University of Science and Technology)
Oliver M.López Valencia (King Abdullah University of Science and Technology)
Omar A.López Camargo (King Abdullah University of Science and Technology)
Guillaume Pouget (Avignon University)
Claude Doussan (Avignon University)
Dominique Courault (Avignon University)
Matthew F. McCabe (King Abdullah University of Science and Technology)
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Abstract
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 (Nth) and epithermal (Nepi) 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, Nth 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 Nth and UAV-derived PAI with R2 = 0.86. Nth 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 Nth. The corresponding R2 with NDRE from coarser-resolution Sentinel-2 data was lower (R2 = 0.51). The correlation between Nth and PAI was higher than that between Nth and SM (R2 = 0.61). Results suggest that variations in Nth are potentially valuable for vegetation monitoring, provided the confounding effect of soil moisture can be taken into account.
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