Preprints
https://doi.org/10.5194/hess-2021-25
https://doi.org/10.5194/hess-2021-25

  03 Feb 2021

03 Feb 2021

Review status: a revised version of this preprint was accepted for the journal HESS.

Spatio-temporal soil moisture retrieval at the catchment-scale using a dense network of cosmic-ray neutron sensors

Maik Heistermann1, Till Francke1, Martin Schrön2, and Sascha E. Oswald1 Maik Heistermann et al.
  • 1Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Straße 24–25, 14476 Potsdam, Germany
  • 2UFZ – Helmholtz Centre for Environmental Research GmbH, Dep. Monitoring and Exploration Technologies, Permoserstr. 15, 04318, Leipzig, Germany

Abstract. The method of Cosmic-Ray Neutron Sensing (CRNS) is a powerful technique to retrieve representative estimates of soil water content at a horizontal scale of hectometers (the field scale) and depths of tens of centimeters (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 between May and July 2019 which featured a network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within 1 km2. 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 such as sensor sensitivity, vegetation biomass, soil organic carbon and lattice water, as well as for the influence of the temporally dynamic factors barometric pressure, air humidity, and incoming flux of neutrons. Based on the homogenised neutron data, we investigate the robustness of a uniform calibration approach using one calibration parameter value across all CRNS stations. Finally, we benchmark two different interpolation techniques in order to obtain space-time representations of soil moisture: first, Ordinary Kriging with a fixed range; second, a heuristic approach that complements the concept of spatial interpolation by the idea of a geophysical inversion (constrained interpolation). For the latter, we define a geostatistical model of the spatial soil moisture variation in the study area, and then optimize the parameters of that model so that the error of the forward-simulated neutron count rates is minimized. In order to make the optimization problem computationally feasible, we use a heuristic forward operator that is based on the physics of horizontal sensitivity of the neutron detector. The comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach outperforms Ordinary Kriging by putting a stronger emphasis on horizontal soil moisture gradients at the hectometer scale. The study demonstrates how a CRNS network can be used to generate consistent interpolated soil moisture patterns that could be used to validate hydrological models or remote sensing products.

Maik Heistermann et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-25', Anonymous Referee #1, 05 Mar 2021
    • AC1: 'Reply on RC1', Maik Heistermann, 16 Mar 2021
  • RC2: 'Comment Part 1/2: Sensor standardization, calibration, and sensitivity', Anonymous Referee #2, 08 Mar 2021
    • AC2: 'Reply on RC2', Maik Heistermann, 16 Mar 2021
  • RC3: 'Comment Part 2/2: Spatial patterns and interpolation', Anonymous Referee #2, 10 Mar 2021
    • AC3: 'Reply on RC3', Maik Heistermann, 16 Mar 2021
  • EC1: 'Comment on hess-2021-25 - Start interacting', Nunzio Romano, 15 Mar 2021
  • RC4: 'Comment on hess-2021-25', Anonymous Referee #3, 02 Apr 2021
    • AC4: 'Reply on RC4', Maik Heistermann, 13 Apr 2021

Maik Heistermann et al.

Data sets

A massive coverage experiment of cosmic ray neutron sensors for soil moisture observation in a pre-alpine catchment in SE-Germany (part I: core data) [post-review version] Fersch, B., Francke, T., Heistermann, M., Schrön, M., Döpper, V., and Jakobi, J. https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883

Model code and software

cosmic-sense/jfc1-analysis-hess: First release before manuscript submission Heistermann, M. https://doi.org/10.5281/zenodo.4438922

Maik Heistermann et al.

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Short summary
Cosmic-Ray Neutron Sensing (CRNS) is a powerful technique to retrieve representative estimates of soil moisture for footprints extending over hectometers in the horizontal and decimeters in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a a unique observational campaign which featured a dense network of more than 20 neutron detectors in an area of just 1 km2.