AUTHOR=Franz Trenton E. , Wahbi Ammar , Zhang Jie , Vreugdenhil Mariette , Heng Lee , Dercon Gerd , Strauss Peter , Brocca Luca , Wagner Wolfgang 

TITLE=Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications

JOURNAL=Frontiers in Water

VOLUME=Volume 2 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2020.00009

DOI=10.3389/frwa.2020.00009

ISSN=2624-9375

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.