Soil moisture partly controls land‐atmosphere mass and energy exchanges and ecohydrological processes in natural and agricultural systems. Thus, many models and remote sensing products continue to improve their spatiotemporal resolution of soil moisture, with some land surface models reaching 1 km resolution. However, the reliability and accuracy of both modeled and remotely sensed soil moisture require comparison with ground measurements at the appropriate spatiotemporal scales. One promising technique is the cosmic ray neutron probe. Here we further assess the suitability of this technique for real‐time monitoring across a large area by combining data from three fixed probes and roving surveys over a 12 km × 12 km area in eastern Nebraska. Regression analyses indicated linear relationships between the fixed probe averages and roving estimates of soil moisture for each grid cell, allowing us to derive an 8 h product at spatial resolutions of 1, 3, and 12 km, with root‐mean‐square error of 3%, 1.8%, and 0.9%.

Abstract. The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE  =  5.45 wt %, R2  =  0.68), soil bulk density (RMSE  =  0.173 g cm−3, R2  =  0.203), and soil organic carbon (RMSE  =  1.47 wt %, R2  =  0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE  ∼  0.035 cm3 cm−3 at a SWC  =  0.40 cm3 cm−3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE  <  1 kg m−2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacitydigitalcommons.unl.edu F i n k e n b i n e r e t a l . i n P r e c i s i o n A g r i c u lt u r e , 2 0 1 8 2estimates reduced 20-25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6-8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

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