Footprint characteristics revised for field‐scale soil moisture monitoring with cosmic‐ray neutrons

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.

Application of cosmic-ray neutron sensing to monitor soil water content in an alpine meadow ecosystem on the northern Tibetan Plateau

“…MCNPX simulations indicate that the radius (R) of the horizontal footprint at sea level (air pressure of 1013 hPa) is about 300 m and is mainly affected by the properties of the atmosphere and has little relationship with SWC (Desilets and Zreda, 2013). In a newer study, however, the radius was between 130 and 240 m, and a calculation equation was proposed (Köhli et al, 2015). According to Desilets and Zreda (2013), R could be calculated using the equation:…”

“…Unlike the conventional methods of measuring SWC, the CRNS can continuously measure the mean SWC of what can be conceived to be a flat-topped cylinder of soil. The radius of the horizontal footprint of the CRNS, the region containing 86% of the detected neutrons, is estimated to be about 300 m by Desilets and Zreda (2013) and from 130 to 240 m by Köhli et al (2015) at sea level (atmospheric pressure of 1013 hPa). The surveying depth is estimated to be about 12 cm (saturated soil) to 76 cm (dry soil) by Zreda et al (2008) and from 15 to 83 cm by Köhli et al (2015).…”

Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

“…This sampling scheme is based on a CRP footprint of ∼ 300 m radius. According to Köhli et al (2015), the CRP footprint might be smaller (∼ 200 m radius). This study was performed prior to the new estimations of the CRP footprint so a radius of ∼ 300 m was still assumed and samples along the 200 m radial were included in the snow surveys.…”

“…The CRP is an appealing soil water content measurement tool for several reasons. Firstly, it has a landscape-scale measurement area with a radius originally thought to be ∼ 300 m (Desilets and Zreda, 2013) but recently estimated to be ∼ 200 m (Köhli et al, 2015). Secondly, it measures soil water content passively (non-radioactive) and non-invasively (CRP sits above the soil surface).…”

Scaling, similarity, and the fourth paradigm for hydrology

“…In particular, there are exciting developments in mesoscale (i.e., hillslope to catchment) observations, which are critical for testing hypotheses about scaling (REA, RH, REW) by connecting point measurements, hydrological models, and remote sensing observations. Examples include recent advances in cosmic ray neutron sensors (Franz et al, 2015;Köhli et al, 2016;Zreda et al, 2008), distributed temperature sensing (DTS; Steele-Dunne et al, 2010;Bense et al, 2016;Dong et al, 2016), soil moisture observations, the use of crowdsourcing (de Vos et al, 2016) and microwave signal propagation from telecommunications towers for precipitation (Leijnse et al, 2007), to the rise in the use of unmanned autonomous vehicles to characterize the landscape on centimeter scale (Vivoni et al, 2014). These alternative data sources enhance our ability to observe, understand, and simulate the hydrological cycle.…”