Probabilistic regionalization of soil moisture data measured by Rail-based cosmic ray neutron sensing

Abstract

<p>Information about soil water content (SWC) in adequate spatial and temporal resolution is highly desired for a variety of scientific and practical applications. Cosmic-Ray Neutron Sensing (CRNS) has become an established method for passive SWC data collection, providing SWC information over several hectares, either by stationary CRNS sensors (local continuous measurements) or by mobile CRNS roving (expanding the footprint on certain field campaign days). Recent approaches of automatic rail-based CRNS roving (Rail-CRNS) allowed to expand the monitored areas further up to the kilometer scale in high temporal resolution. While a pilot study on Rail-CRNS provided promising results along the railway track, currently in daily resolution, it also raised the question of how transferable these SWC data are for areas not directly adjacent to the footprints along the railway. In this study, we have tested the performance of SWC regionalization by probabilistic predictions based on Rail-CRNS derived SWC data. A Monte Carlo approach was applied in regression random forest, using static (e.g. topographical indices, soil properties) and dynamic (precipitation) predictors and quantified their impact on the prediction accuracy. Using daily SWC values from a ~ 9 km long railway at the Harz mountain, Germany, recorded by the Rail-CRNS between September 2021 and July 2022, we predicted the daily spatial SWC variation for an area of ~ 85 km² and a period of 300 days on a 250 x 250 m grid. The resulting maps of gravimetric soil moisture showed realistic pattern for both, spatial and temporal SWC variation. The maps resolved spatial variation as related to land cover, seasonal SWC dynamics and individual responses of single areas to wetting and drying periods. As the demonstrated data represented the outcome of a relatively narrow area as given by the limited training Rail-CRNS data, the extension of the proposed approach by expanding the railway networks, by future technical improvements and by the automatization of the workflow has the clear potential to offer near real time SWC products for the large scale (> 100 km). </p>