Soil water content determination with cosmic-ray neutron sensor: Correcting aboveground hydrogen effects with thermal/fast neutron ratio

The cosmic-ray neutron sensor (CANS), which estimates field scale soil water content, bridges the gap between point measurement and remote sensing. The accuracy of CRNS measurements, however, is affected by additional hydrogen pools (e.g., vegetation, snow, and rainfall interception). The objectives of this study are to: (i) evaluate the accuracy of CRNS estimates in a farmland system using depth and horizontal weighted point measurements, (ii) introduce a novel method for estimating the amounts of hydrogen from biomass and snow cover in CRNS data, and (iii) propose a simple approach for correcting the influences of aboveground hydrogen pool (expressed as aboveground water equivalent, AWE) on CRNS measurements. A field experiment was conducted in northeast China to compare soil water content results from CANS to in-situ data with time domain reflectometry (TDR) and neutron probe (NP) in the 0-40 cm soil layers. The biomass water equivalent (BWE) and snow water equivalent (SWE) were observed to have separate linear relationships with the thermal fast neutron ratio, and the dynamics of BWE and SWE were estimated correctly in the crop seasons and snow-covered seasons, respectively. A simple approach, which considered the AWE, AWE at calibration, and the effective measurement depth of CRNS, was introduced to correct the errors caused by BWE and SWE. After correction, the correlation coefficients between soil water contents determined by CRNS and TDR were 0.79 and 0.77 during the 2014 and 2015 crop seasons, respectively, and CRNS measurements had RMSEs of 0.028, 0.030, and 0.039 m(3) m(-3) in the 2014 and 2015 crop seasons and the snow-covered seasons, respectively. The experimental results also indicated that the accuracies of CRNS estimated BWE and SWE were affected by the distributions of aboveground hydrogen pools, which were related to the height of the CRNS device above ground surface. (C) 2016 Elsevier B.V. All rights reserved.