A dataset of soil moisture observed by cosmic-ray neutron method in Quercus variabilis plantation at Xiaolangdi Station (2016–2021)

Soil moisture serves as a vital source and sink in the water cycle within the soil-biosphere-atmosphere system, playing a crucial role in vegetation carrying capacity, soil and water conservation, and climate regulation. Soil water monitoring typically depends on point-scale in situ observations and large-scale remote sensing inversion methods, while there is a gap in the monitoring spatial scale between these two approaches. The cosmic-ray neutron method can monitor soil water content within a radial radius of a few hundreds of meters and a vertical distance of tens of centimeters in soil, facilitating research on medium-scale hydrological processes between point-scale and remote-sensing large-scale levels. Soil water content at the regional scale based on the conversion of the cosmic-ray neutron method has been validated and applied across diverse vegetation types including farmland, grassland, and forest. Quercus variabilis plantation represents a typical forest type distributed within the key ecological zone of the Southern Taihang Mountains and the middle reaches of the Yellow River basin, serving the ecological engineering development of the Yellow River basin. Long-term observations of soil moisture in Quercus variabilis plantation have been carried out at Henan Xiaolangdi Forest Ecosystem National Observation and Research Station (Xiaolangdi Station). This dataset compiles long-term medium scale daily average soil moisture observations in Quercus variabilis plantation during the growing season at Xiaolangdi Station. The time span covered April to October from 2016 to 2021, employing the cosmic-ray neutron method as the monitoring method. The ratio of effective observation data for soil moisture exceeded 91% in each year. After vegetation biomass correction, soil water content estimated by the cosmic-ray neutron method was significantly correlated with the sensor-measured water content at 0–20 cm soil depth within the neutron source footprint, yielding a root mean square error of 0.044 cm3 cm-3. This dataset aims to provide fundamental data support for further exploring forest ecohydrological processes in the middle reaches of the Yellow River basin under climate change, and offer scientific decision-making basis for regional forest sustainable management and ecosystem service functions.