近年来，中国大气二氧化碳浓度逐年上升。卫星观测是获取大气二氧化碳浓度的主要手段。然而，目前用于测量大气二氧化碳的星载传感器观测范围较窄，无法获得时空连续的大气二氧化碳浓度。因此，本数据集提出了一种基于 DSC-DF-LGB（Deep Separable Convolutional Neural Network and Deep Forest concatenated with LightGBM）模型的日全覆盖 XCO2 数据集生成方法，以获取中国大气二氧化碳的时空分布。建立 DSC-DF-LGB 模型的目的是训练 OCO-2 XCO2 检索与相关变量（再分析 XCO2 、植被参数、人为因素、海拔高度和气象参数）之间的映射关系。利用该模型生成了 2015 至 2020 年中国每日 0.1° 全覆盖 XCO2 数据集。全覆盖和高分辨率的 XCO2 数据集可为碳源和碳汇研究提供数据支持。
", "ds_source": "包括估算后得到的 XCO2数据和现场测量的XCO2数据。
", "ds_process_way": "数据集提出了一种基于 DSC-DF-LGB（Deep Separable Convolutional Neural Network and Deep Forest concatenated with LightGBM）模型的日全覆盖 XCO2 数据集生成方法，以获取中国大气二氧化碳的时空分布。
", "ds_quality": "交叉验证（CV）结果表明，该模型在估算XCO2方面具有很强的性能，R2 和 RMSE 分别为 0.9633 和 0.9761 ppm。TCCON 独立现场验证结果表明，估算的 XCO2 与现场测量值高度一致，R2 和 RMSE 分别为 0.8786 和 1.5452 ppm。
", "ds_acq_start_time": "2015-01-21 00:00:00", "ds_acq_end_time": "2020-12-31 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": 5128674462, "ds_files_count": 2, "ds_format": "nc", "ds_space_res": "10000", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "47111df7-d758-42f9-9ebb-3a53fcd41113.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2025-01-24 10:09:17", "last_updated": "2026-01-14 10:47:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6751.2025", "i18n": { "en": { "title": "China Full Coverage XCO2 Daily Dataset Based on DSC-DF-LGB (2015-2020)", "ds_format": "nc", "ds_source": "Including estimated XCO2data and on-site measured XCO2data", "ds_quality": "
The cross validation (CV) results of XCO2show that the model has strong performance in estimating XCO2, with R2and RMSE of 0.9633 and 0.9761 ppm, respectively. The independent on-site verification results of TCCON indicate that the estimated XCO2 is highly consistent with the measured values on site, with R2and RMSE of 0.8786 and 1.5452 ppm, respectively", "ds_ref_way": "", "ds_abstract": "
In recent years, the concentration of atmospheric carbon dioxide in China has been increasing year by year. Satellite observation is the main means of obtaining atmospheric carbon dioxide concentration. However, currently, spaceborne sensors used to measure atmospheric carbon dioxide have a narrow observation range and cannot obtain spatially and temporally continuous atmospheric carbon dioxide concentrations. Therefore, this dataset proposes a daily full coverage XCO2dataset generation method based on the DSC-DF-LGB (Deep Separable Convolutional Neural Network and Deep Forest concatenated with LightGBM) model to obtain the spatiotemporal distribution of atmospheric carbon dioxide in China. The purpose of establishing the DSC-DF-LGB model is to train the mapping relationship between OCO-2 XCO2retrieval and related variables (reanalysis of XCO2, vegetation parameters, human factors, altitude, and meteorological parameters). The model was used to generate a daily 0.1 ° full coverage XCO2 dataset in China from 2015 to 2020. The XCO2dataset with full coverage and high resolution can provide data support for carbon source and sink research
", "ds_time_res": "日", "ds_acq_place": "China", "ds_space_res": "10000", "ds_projection": "", "ds_process_way": "method for generating a daily full coverage XCO dataset based on the DSC-DF-LGB (Deep Separable Convolutional Neural Network and Deep Forest concatenated with LightGBM) model was proposed to obtain the spatiotemporal distribution of atmospheric carbon dioxide in China", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "XCO2", "DSC-DF-LGB", "中国" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "杨辉", "email": "yanghui@cumt.edu.cn", "work_for": "中国矿业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨辉", "email": "yanghui@cumt.edu.cn", "work_for": "中国矿业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "杨辉", "email": "yanghui@cumt.edu.cn", "work_for": "中国矿业大学", "country": "中国" } ], "category": "气象" }