The Potsdam Soil Moisture Observatory: high-coverage reference observations at kilometer scale

Abstract. Cosmic-ray neutron sensing (CRNS) has gained popularity for estimating soil moisture due to its innovative capability to measure at an intermediate scale – a notable advantage over point-scale sensors, which are often sparsely installed and lead to inaccurate absolute values due to small-scale heterogeneity. CRNS serves as a crucial link between small and large scales and has been emerging as a reference measurement for validating remote sensing algorithms. However, the sparse availability of long-term datasets limits use of this possibility. Within the DFG-research unit Cosmic Sense and the European 21GRD08 SoMMet project, multiscale soil moisture monitoring was implemented to integrate CRNS with complementary in-situ observations. In this paper, we present harmonized soil moisture data from different sensor types, including a CRNS cluster, shallow soil moisture measurements, and soil moisture profile data, creating a ready-to-use dataset as a reference observation for remote sensing products, covering a highly-instrumented agricultural site in the northeast of Germany. The newly established Potsdam Soil Moisture Observatory (PoSMO) comprises 16 stationary CRNS sensors with point-scale soil moisture sensors installed at the same locations in different depths and data from intensive manual sampling campaigns (covering soil moisture, bulk density, organic matter, etc.). This dataset goes beyond other studies by covering a larger area of approx. 1 km2, while nevertheless achieving a high sensor density and mostly overlapping CRNS footprints allowing for nearly complete coverage. Complementary measurements of soil properties, vegetation, groundwater, meteorology, and remote sensing imagery provide the context required to interpret the observed soil moisture dynamics across spatial and temporal scales. The data are available at https://doi.org/10.23728/b2share.bxamy-4zh85 (Grosse et al., 2025) and provide a new reference dataset for remote sensing products, hydrological or land-surface models, and other applications related to soil water balance.