Footprint Characteristics Revised for Field-Scale Soil Moisture Monitoring with Cosmic-Ray Neutrons

TL;DR

This study refines the understanding of cosmic-ray neutron probe footprints, revealing their sensitivity to environmental heterogeneity and providing improved models for soil moisture monitoring at the field scale.

Contribution

It introduces a refined simulation model with an updated neutron spectrum to better characterize the probe's footprint and response to environmental conditions.

Findings

Footprint radius varies from 130 to 240 meters depending on conditions.

Soil moisture influences the penetration depth exponentially with distance.

Footprint remains nearly isotropic in complex terrain.

Abstract

Cosmic-ray neutron probes are widely used to monitor environmental water content near the surface. The method averages over tens of hectares and is unrivaled in serving representative data for agriculture and hydrological models at the hectometer scale. Recent experiments, however, indicate that the sensor response to environmental heterogeneity is not fully understood. Knowledge of the support volume is a prerequisite for the proper interpretation and validation of hydrogeophysical data. In a previous study, several physical simplifications have been introduced into a neutron transport model in order to derive the characteristics of the cosmic-ray probe's footprint. We utilize a refined source and energy spectrum for cosmic-ray neutrons and simulate their response to a variety of environmental conditions. Results indicate that the method is particularly sensitive to soil moisture in the first tens of meters around the probe, whereas the radial weights are changing dynamically with ambient water. The footprint radius ranges from 130 to 240 m depending on air humidity, soil moisture and vegetation. The moisture-dependent penetration depth of 15 to 83 cm decreases exponentially with distance to the sensor. However, the footprint circle remains almost isotropic in complex terrain with nearby rivers, roads or hill slopes. Our findings suggest that a dynamically weighted average of point measurements is essential for accurate calibration and validation. The new insights will have important impact on signal interpretation, sensor installation, data interpolation from mobile surveys, and the choice of appropriate resolutions for data assimilation into hydrological models.