Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.

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 (m ² 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 R ² = 0.86. N _th increased linearly by approximately 4.5% per 1 m ² m ⁻² increase in PAI. Of the vegetation indices, the Normalized Difference Red Edge (NDRE) index derived from PlanetScope images showed the highest correlation ( R ² = 0.69) with N _th . The corresponding R ² with NDRE from coarser-resolution Sentinel-2 data was lower ( R ² = 0.51). The correlation between N _th and PAI was higher than that between N _th and SM ( R ² = 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.