Abstract
Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.
Highlights
1.1 The retrieval of soil water content from cosmic-ray neutronsThe observation of soil water content remains a scientific challenge
This study demonstrates the potential of the Cosmic-ray neutron sensing (CRNS) technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints
The specific questions addressed in this study are the following: 1. Given the heterogeneity with regard to detector sensitivity and the spatial distribution of hydrogen pools, can we find a joint value of the calibration parameter N0 (Eq 1) for all CRNS sensors? If so, we could consistently convert observed neutron intensities to soil moisture across all sensors
Summary
1.1 The retrieval of soil water content from cosmic-ray neutronsThe observation of soil water content remains a scientific challenge. 1.1 The retrieval of soil water content from cosmic-ray neutrons. Many methods allow the pointwise measurement of soil water content, but their spatial representativeness is limited when the small spatial measurement support (on the order of centimetres) (Blöschl and Grayson, 2000) is confronted by high small-scale variability of soil moisture. Cosmic-ray neutron sensing (CRNS) has been established as a powerful option for retrieving volume-integrated estimates of soil water content (Zreda et al, 2008; Desilets et al, 2010; Zreda et al, 2012). These estimates are considered representative of a footprint that ex-. The method relies on measurements of the ambient density of epithermal neutrons (i.e. energies of 1–105 eV) above the ground, which is inversely related to the presence of hydrogen and soil moisture (Köhli et al, 2020)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
