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@ARTICLE{Jakobi:851432,
author = {Jakobi, J. and Huisman, J. A. and Vereecken, H. and
Diekkrüger, B. and Bogena, H. R.},
title = {{C}osmic {R}ay {N}eutron {S}ensing for {S}imultaneous
{S}oil {W}ater {C}ontent and {B}iomass {Q}uantification in
{D}rought {C}onditions},
journal = {Water resources research},
volume = {54},
number = {10},
issn = {0043-1397},
address = {[New York]},
publisher = {Wiley},
reportid = {FZJ-2018-05079},
pages = {7383-7402},
year = {2018},
abstract = {Understanding the feedback mechanisms between soil water
content (SWC) and biomass production is important for
sustainable resources management. Here, we present a new
method enabling simultaneous non‐invasive measurements of
SWC and biomass dynamics based on cosmic‐ray neutron
sensing (CRNS). Recently, it was suggested that the neutron
ratio (N<sub>r</sub>) between thermal neutron (TN) and fast
neutron (FN) intensity contains information on other
hydrogen pools like vegetation, canopy interception, and
snow. The aim of this study is to evaluate the accuracy of
simultaneous measurements of SWC and biomass dynamics during
agricultural drought conditions using CRNS probes. To this
end, we instrumented an arable field cropped with sugar beet
with CRNS probes and a wireless in‐situ SWC sensor
network. Below‐ and aboveground biomass were sampled in
monthly intervals. We found a linear relationship between
N<sub>r</sub> and the aboveground biomass that allowed to
continuously quantify the dry aboveground biomass
development throughout the growing season with a root mean
square error (RMSE) from 0.14 to 0.22 kg/m<sup>2</sup>. This
information was used together with measured belowground
biomass to correct for the effect of biomass on SWC
determination with CRNS probes, which increased the accuracy
of the SWC estimates considerably as indicated by the
decrease of the RMSE from 0.046 to 0.013
cm<sup>3</sup>/cm<sup>3</sup>. We anticipate that future
research on the N<sub>r</sub> can further improve the
accuracy of SWC and biomass estimates, and extend the
application of CRNS to include canopy interception, ponding
water, and snow water equivalent estimation for both
stationary and roving CRNS systems.},
cin = {IBG-3},
ddc = {550},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {255 - Terrestrial Systems: From Observation to Prediction
(POF3-255)},
pid = {G:(DE-HGF)POF3-255},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000450726000016},
doi = {10.1029/2018WR022692},
url = {https://juser.fz-juelich.de/record/851432},
}