Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Abstract

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Comment on Dong and Ochsner (2018): “Soil Texture Often Exerts Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns”

“…Therefore, we also repeated the analysis for gravimetric soil moisture and found higher correlation with sand content while correlations with API were relatively similar. In addition to the uncertainties in soil bulk density, lattice water, and organic matter content, the cosmic‐ray neutron method is also susceptible to other uncertainties that are not considered here and in the study by Dong and Ochsner (2018), e.g., the uncertainty in raw neutron counts (Jakobi et al., 2020), the influence of vegetation (e.g., Avery et al., 2016; Fersch, Jagdhuber, Schrön, Völksch, & Jäger, 2018; Jakobi, Huisman, Vereecken, Diekkrüger, & Bogena, 2018) and the influence of roads (Schrön et al., 2018). We hope that this exchange will generate further interest in the use of the CRN rover method to improve our understanding of the controls on mesoscale soil moisture patterns.…”

The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

“…The recording interval was set to 10 seconds. [63]. The measurement of soil moisture with cosmic-ray neutron sensing relies on the inverse dependence of above-ground epithermal neutrons (energy range from 0.2 eV to 100 keV) on the environmental water content in a footprint of 130 m to 240 m radius and 15 cm to 83 cm penetration depth [61].…”

Towards disentangling heterogeneous soil moisture patterns in Cosmic-Ray Neutron Sensor footprints

“…As the analyses revealed a significant (p < 0.05) difference between the CRNS-derived soil moisture based on the standard calibration approach and both alternative calibration approaches for at least 97 percent of the data points of the time series, a significantly improved representation of the near-field soil moisture dynamics can be achieved by either adjusting all parameters (approach 1) or combining both neutron energy ranges (approach 2). However, this does not represent an uncertainty analysis that considers various statistical sources of uncertainty as it was done in previous studies (e.g., Gugerli et al, 2019;Jakobi et al, 2020).…”