Data-Driven Scaling Methods for Soil Moisture Cosmic Ray Neutron Sensors

Abstract. Cosmic ray neutron probes (CRNS) are increasingly used for soil moisture measurement, yet uncertainties persist due to reliance on traditional analytical scaling methods that may not fully account for site-specific and sensor-specific characteristics. This study introduces a novel, data-driven calibration approach to estimate key scaling parameters (beta, psi, and omega) for CRNS, emphasizing local environmental factors and sensor attributes. The method provides a more flexible, empirical approach to calibration by directly calculating correction parameters from measurement data.

The results demonstrate that the new method is both reliable and robust, showing strong correlations between the estimated parameters and those predicted by analytical methods. However, the study also reveals systematically higher variability in calibration parameters than previously assumed, underscoring the importance of data quality and careful selection of NMDB reference sites. Sensor-specific factors, such as the energy spectrum, along with site-specific factors like elevation and geographic proximity to NMDB sites, significantly influence scaling parameters, highlighting the necessity for site- and sensor-specific calibration to improve soil moisture estimates. Future research should focus on refining these scaling methods and enhancing data quality to further improve CRNS measurement accuracy.