This study was conducted in a typical dryland agricultural area of the Loess Plateau. Given the current situation where long-term continuous carbon flux data for farmland ecosystems in arid regions of China is scarce, based on the Shanxi Shouyang National Field Scientific Observation and Research Station (113°12′E, 37°45′N), using the eddy covariance technique, a continuous long-term observational study of spring corn farmland was carried out for eight years (2013–2020, excluding 2018), providing a solid data foundation for evaluating the carbon balance of farmland and revealing the laws of carbon-water exchange and its environmental impact mechanisms. The observation indicators covered conventional meteorological data such as air humidity, air relative humidity, saturation vapor pressure difference, soil temperature, soil moisture, photosynthetic active radiation, net radiation, precipitation, etc., as well as core carbon-water fluxes such as net ecosystem CO2 exchange, ecosystem respiration, total primary productivity, latent heat flux and sensible heat flux. Finally, standardized data products at four time scales of 30 minutes, daily, monthly and annual were generated.

Shunyi District of Beijing is located at the northern edge of the North China Plain. Its farming pattern is a typical one of winter wheat-summer maize two-crop system in the North China region, which is a typical representative for studying the intensive agricultural ecological processes in North China. This study was conducted based on the eddy covariance observation technology in the w winter wheat-summer maize rotation farmland in Beijing. Continuous and high-frequency carbon and water flux monitoring was carried out at the half-hour, daily, monthly and annual time scales, and the ecosystem carbon fluxes, water and heat fluxes, and meteorological data for the years 2019-2020 were systematically obtained. The data set follows the quality control standards of the China Flux Observation Research Network (ChinaFLUX), and has undergone strict correction, interpolation and splitting processing to form standardized carbon and water flux and meteorological element data sets. These provide high-quality ground observation data for evaluating regional agricultural carbon sink capacity, water use efficiency, optimizing agricultural management measures, supporting crop models and remote sensing product validation, and thus have significant value in promoting the green and low-carbon development of agriculture.