Abstract
The Cosmic-Ray Neutron Sensor (CRNS) technique for estimating landscape average soil water content (SWC) is now a decade old and includes now many practical methods for implementing measurements, such as identification of detection area and depth, installation, calibration, and validation. However, in order to maximize the societal relevance of CRNS SWC data, practical value-added products need to be developed that can estimate both water flux (i.e. rainfall, deep percolation, evapotranspiration) and root zone storage changes. In particular, simple methods that can be used to estimate daily values at landscape average scales are needed by decision makers and stakeholders interested in utilizing this technique. Moreover, landscape average values are necessary for better comparisons with remote sensing products. In this work we utilize three well established algorithms to enhance the usability of the CRNS data. The algorithms aim to: 1) temporally smooth the neutron intensity and SWC time series, 2) estimate a daily rainfall product using the Soil Moisture 2 Rain (SM2RAIN) algorithm, and 3) estimate daily root zone SWC using an exponential filter algorithm. The algorithms are tested on the CRNS site at the Hydrological Open Air Laboratory experiment in Petzenkirchen, Austria. Independent observations of rainfall and point SWC data are used to calibrate and validate the algorithms. With respect to rainfall, the SM2RAIN algorithm resulted in a Kling-Gupta-Efficiency (KGE) criteria of 0.665 for daily and 0.819 for 5 day totals. With respect to SWC, the exponential filter algorithm resulted in a KGE of 0.909 for the 0-30cm depth and 0.912 for the 0-60 cm depth. A methodological framework is presented that summarizes the different processes, required data, algorithms, and products.
Highlights
The Cosmic-Ray Neutron Sensor (CRNS) is an in situ technique that is unique in its capability to estimate soil water content (SWC) at scales from ∼1 to 10 ha using stationary and mobile platforms
This methodological paper provides the background, equations, and example calculations from the Petzenkirchen CRNS study site using three well-established algorithms summarized in the methodological framework in Figure 1 and available for general use
The algorithms make the essential step of enhancing the CRNS SWC data for providing stakeholders with the value-added products of a smoothed SWC time series, landscape average rainfall, and root zone SWC data in order to make decisions
Summary
The Cosmic-Ray Neutron Sensor (CRNS) is an in situ technique that is unique in its capability to estimate soil water content (SWC) at scales from ∼1 to 10 ha using stationary and mobile platforms In order to maximize the societal and scientific relevance of SWC data (Vereecken et al, 2008), practical value-added products need to be developed that can estimate both water flux and root zone SWC changes. Simple methods that can be used to estimate daily values at landscape average scales are needed by stakeholders as well as for better comparisons with remote sensing products (e.g., soil moisture products from Metop Advanced SCAT Scatterometer (ASCAT), NASA’s Soil Moisture Active Passive mission (SMAP), ESA’s Soil Moisture Ocean Salinity mission (SMOS), and Sentinel-1, see McCabe et al (2017) for details on current and planned missions for measuring hydrologic fluxes and state variables). A critical and likely remaining gap for agricultural stakeholders, is providing daily field and subfield scale (0.1–10 ha) root zone SWC data (0 to ∼1 m). With the inability of satellites to directly estimate root zone SWC, indirect methods using a combination of satellites, ground sensors, and models are needed to produce root zone SWC data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
