In this dataset, a comprehensive and uniform dataset of Chinese forest ecosystems at different time scales was assembled using common infrared gas analyzers (i.e. Li-6400, Li-8100, Li-8150) or gas chromatography, referencing Rs related literature and collecting Rs in situ. In addition to reporting Rs data directly in the original paper, monthly model Rs and simultaneous measurement of soil temperature at depths of 5 or 10 centimeters were also included. Provided scientific basis for basic data and quantitative assessment of soil carbon emissions in China's forest ecosystem. At the same time, relevant information was also recorded, such as geographical location (province, research site, latitude, longitude, and altitude), climatic factors (annual average temperature and annual average precipitation), forest stand description (forest type, origin, age, density, average tree height, and diameter at breast height), measurement system (method, time, frequency, territorial area, height, and quantity). I hope this data can be used by the scientific community to better understand the carbon cycle of Chinese forests and reduce the uncertainty of large-scale carbon budget assessments.
A comprehensive and unified Chinese forest Rs database was compiled from 568 published literature up to 2018, including Rs and soil temperature measured during the same period (N=8317), monthly average Rs (N=5003), and annual Rs (N=634). In addition to the Rs data directly provided in the original paper, digital processing was also performed on the Rs at depths of 5 or 10 centimeters in the figure and the monthly variation patterns of soil temperature measured during the same period. These Rs data come from undisturbed forest ecosystems.
We chose the commonly used measurement methods, namely infrared gas analyzer (Li-6400, Li-8100, Li-8150 models (LI-COR company, Lincoln, Nebraska, USA) and gas chromatography.
To verify the accuracy of the digital software, the average values of the extracted data (Rs, T5, T10) were compared with the corresponding average values given in the original paper. The root mean square errors (RMSE) of Rs, T5, and T10 are 0.09, 0.35, and 0.44, respectively, and the determination coefficients (R2）All values are greater than 0.99, indicating that the accuracy of WEBPLOTDGITIZER is very high.
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CSTR:11738.11.NCDC.PANGAEA.DB6813.2025
10.1594/PANGAEA.939619