在本数据集中，参考Rs-相关文献并原地收集Rs使用常见的红外气体分析仪（即 Li-6400、Li-8100、 Li-8150）或气相色谱法组装成全面且均匀的中国森林生态系统在不同时间尺度上的数据集。除了Rs数据直接报告在原始论文中，每月的模式Rs和同时测量 5 厘米或 10 厘米深处的土壤温度。提供了基本数据和定量评估土壤碳排放的科学基础中国的森林生态系统。同时，还记录了相关信息，如地理位置（省份、研究地点、纬度、经度和海拔）、气候因素（年平均气温和年平均降水量）、林分描述（森林类型、起源、年龄、密度、平均树高和胸径）、测量制度（方法、时间、频率、领区面积、高度和数量）。希望该数据能为科学界所用，以更好地了解中国森林的碳循环，减少大尺度碳预算评估的不确定性。", "ds_source": "
从截至2018年发表的568篇文献中整理出一个全面、统一的中国森林Rs数据库，包括Rs与同期测量的土壤温度（N=8317）、月平均Rs（N=5003）和年Rs（N=634）。除了原始论文中直接给出的 Rs 数据外，还对图中 5 厘米或 10 厘米深度的 Rs 和同期测得的土壤温度的月变化规律进行了数字化处理。这些 Rs 数据来自未受干扰的森林生态系统。", "ds_process_way": "
选择了常用的测量方法，即红外气体分析仪（Li-6400、Li-8100、Li-8150 型（LI-COR 公司，美国内布拉斯加州林肯市））和气相色谱法。", "ds_quality": "
为了验证数字软件的准确性，将提取数据的平均值（Rs、T5、T10）与原始论文中给出的相应平均值进行了比较。Rs、T5 和 T10 的均方根误差（RMSE）分别为 0.09、0.35 和 0.44，且判定系数（R2）均大于 0.99，表明 WEBPLOTDIGITIZER 的精确度非常高。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.01666666666668, "ds_acq_lat_south": 3.8666666666666667, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 232471, "ds_files_count": 2, "ds_format": "csv", "ds_space_res": "", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "b201c1c0-f477-4b95-8460-69e098e147f9.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "99c0a56f-14cb-4cfc-a9a1-bb4b8d16a658", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-03-28 19:00:58", "last_updated": "2026-01-14 10:08:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PANGAEA.DB6813.2025", "i18n": { "en": { "title": "Soil respiration in forest ecosystems across China at different time scales (2000-2018)", "ds_format": "csv", "ds_source": "
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.", "ds_quality": "
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.", "ds_ref_way": "", "ds_abstract": "
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.
", "ds_time_res": "月", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "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.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "碳循环", "中国", "森林" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "贾丙瑞", "email": "jiabingrui@ibcas.ac.cn", "work_for": "中国科学院植物研究所植被与环境变化国家重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "贾丙瑞", "email": "jiabingrui@ibcas.ac.cn", "work_for": "中国科学院植物研究所植被与环境变化国家重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "贾丙瑞", "email": "jiabingrui@ibcas.ac.cn", "work_for": "中国科学院植物研究所植被与环境变化国家重点实验室", "country": "中国" } ], "category": "生态" }