@article{HeistermannFranckeScheiffeleetal.2023,
  author    = {Heistermann, Maik and Francke, Till and Scheiffele, Lena and Petrova, Katya Dimitrova and Budach, Christian and Schr{\"o}n, Martin and Trost, Benjamin and Rasche, Daniel and G{\"u}ntner, Andreas and Doepper, Veronika and F{\"o}rster, Michael and K{\"o}hli, Markus and Angermann, Lisa and Antonoglou, Nikolaos and Zude, Manuela and Oswald, Sascha},
  title     = {Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany},
  series = {Earth system science data : ESSD},
  volume    = {15},
  journal   = {Earth system science data : ESSD},
  number    = {7},
  publisher = {Copernics Publications},
  address   = {Katlenburg-Lindau},
  issn      = {1866-3508},
  doi       = {10.5194/essd-15-3243-2023},
  pages     = {3243 -- 3262},
  year      = {2023},
  abstract  = {Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany - the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral im-agery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for vari-ous disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).},
  language  = {en}
}