Extrapolating field-scale near-surface soil moisture information from Cosmic Ray Neutron Sensing to greater depth

Cosmic-Ray Neutron Sensing (CRNS) is a modern technique for non-invasive soil moisture estimation at the field scale. It closes the scale gap between point-scale observations (e.g. soil sampling, in-situ sensors) and coarse-scale satellite-derived estimates. While CRNS has a large horizontal footprint with a radius of roughly 150 m around the instrument, the average vertical measurement depth is only about 30 cm. Thus, extrapolating the CRNS-derived soil moisture to greater soil depths such as the entire root zone can be highly beneficial for hydrological applications such as landscape water balancing or irrigation management. To this end, previous studies have used, for instance, additional in-situ sensors and time-stability approaches or calibrated exponential filters against reference measurements in deeper soil depths.

However, additional permanent in-situ sensors and reference measurements in greater depths are not always available or feasible. Against this background, we use the physically-based soil moisture analytical relationship (SMAR) which can be used without calibration against reference measurements. We estimate the required model parameters from soil characteristics (e.g. porosity, water content at field capacity and wilting point) as well as from the CRNS soil moisture time series itself.

As CRNS for soil moisture estimation is developing rapidly, new transfer functions from observed neutron intensities to surface soil moisture have been introduced. We investigate the influence of using both the standard transfer function and the recently introduced universal transport solution (UTS) on the depth-extrapolated soil moisture time series. These depth-extrapolated soil moisture time series are then evaluated against soil moisture reference time series from in-situ soil moisture sensors down to 450 cm depth.