Validation of rail based CRNS-roving: underpinning the large-scale root zone soil moisture monitoring concept

Root zone soil moisture (RZSM) is a critical parameter for various environmental, agricultural, and hydrological applications. The recently proposed rail based Cosmic Ray Neutron Sensing monitoring method (Rail-CRNS) offers an innovative solution for soil moisture measurement by enabling continuous, large-scale RZSM measurements across extensive railway networks. By 2024, Germany established a fleet of five Rail-CRNS systems, covering up to hundreds of kilometers daily and marking thus a transformative step in soil moisture monitoring. Yet, questions remained regarding the reliability of Rail-CRNS data: did they accurately capture RZSM, or were they overly influenced by confounding factors such as land use and rail track conditions?

This study addresses these questions by analyzing 16 months of Rail-CRNS data collected along a pilot route in Rübeland, Low Harz Mountain, Germany. Time series from two stationary CRNS sites, located in forested and grassland areas, were compared with corresponding Rail-CRNS data segments. Additionally, soil moisture measurements from buried sensor nodes in the forest provided for parts of the period another independent reference dataset. The results demonstrated a strong correlation between the stationary CRNS measurements, the Rail-CRNS-derived RZSM values, and the soil moisture node data. This alignment indicates that Rail-CRNS data reliably captures not only spatial but also temporal variability in soil moisture. These findings provide robust support for the Rail-CRNS concept, emphasizing its potential to generate accurate and high-resolution RZSM data for regional and national-scale monitoring.

However, the pilot study was conducted under specific and well-monitored conditions, with frequent train passages and a well-instrumented route. Applying the Rail-CRNS method to longer, less-instrumented tracks, combined with higher train speed variability and fewer repeated passes, will likely introduce greater uncertainties. To address this, the deployment of a CRNS station cluster near railways was proposed. Such clusters would enable ongoing validation of Rail-CRNS data, ensuring their reliability across diverse environmental and operational conditions.

This study underscored the transformative potential of Rail-CRNS in overcoming the long-standing challenges of sparse and incomplete RZSM measurements. However, further instrumentation and research is planned to develop strategies for mitigating potential uncertainties in less-controlled environments. Integrating Rail-CRNS data with satellite-based products and RZSM estimates from hydrological modeling for example could further enhance the accuracy and applicability of soil moisture monitoring on a national scale.