全球气候变暖的不可逆趋势凸显了高精度监测与分析全球尺度大气碳动态的必要性。碳卫星在大气CO2监测方面具有重要潜力，但现有全球CO2研究受限于较粗分辨率（0.25°-2°）和有限的空间覆盖度。本研究基于碳卫星观测数据、多源卫星产品及改进的深度学习模型，构建了首个0.05°分辨率全覆盖的全球大气柱平均干空气CO2摩尔分数（XCO2）数据集，进而探究了2015-2021年全球大气CO2浓度变化及其异常特征。重建的XCO2产品与TCCON地面观测网络数据具有更高一致性（R2=0.92，RMSE=1.54 ppm），相比原始碳卫星监测数据和既有XCO2产品，能更精确反映全球与区域尺度的XCO2空间格局。全球XCO2呈现显著增长趋势（2.32 ppm/年），至2021年达414.00 ppm。不同纬度和大陆间表现出明显空间异质性：北半球特别是东亚、北美等人类活动密集区浓度最高。本研究还验证了该产品在识别高强度CO2排放源方面的有效性。
", "ds_source": "本研究采用2014年12月至2021年12月期间的OCO-2/3卫星XCO2观测数据。该卫星传感器通过三个近红外波段进行探测，包括0.76微米氧A波段（O2-A）、1.61微米弱二氧化碳吸收波段（Weak CO2）及2.06微米强二氧化碳吸收波段（Strong CO2）。在本研究中，我们使用了来自全球 23 个站点的 GGG2014 和 GGG2020 数据集来验证重建的 XCO2 产品。
", "ds_process_way": "本研究基于Google Earth Engine（GEE）平台，整合OCO-2/3卫星XCO2数据与多源环境参量作为输入数据，采用注意力机制双向长短期记忆网络（At-BiLSTM）构建卫星XCO2与环境变量的耦合关系模型。通过该模型重构全球月度XCO2空间分布数据，并利用TCCON站点观测数据及原始OCO-2/3卫星数据进行精度验证。在此基础上，系统解析全球XCO2的时空分异特征，精准识别高强度碳排放热点区域。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 5563344864, "ds_files_count": 85, "ds_format": ".nc", "ds_space_res": "0.05° ", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d8075cae-6d83-4070-bc73-68d37f77dae3.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-05-29 16:16:20", "last_updated": "2025-05-29 16:16:20", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6863.2025", "i18n": { "en": { "title": "A monthly full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021", "ds_format": ".nc", "ds_source": "In this study, we utilized the satellite-based XCO2 data from OCO-2 and OCO-3, covering the period from December 2014 to December 2021. The OCO-2/3 measure at three near-infrared wavelength bands, that are 0.76 μm Oxygen A-band, 1.61 μm weak CO2, and 2.06 μm strong CO2 bands.In this research, we used the GGG2014 and GGG2020 datasets from 23 sites around the world to validate the reconstructed XCO2 products.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "The irreversible trend for global warming underscores the necessity for accurate monitoring and analysis of atmospheric carbon dynamics on a global scale. Carbon satellites hold significant potential for atmospheric CO2 monitoring. However, existing studies on global CO2 are constrained by coarse resolution (ranging from 0.25° to 2°) and limited spatial coverage. In this study, we developed a new global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite observations, multi-source satellite products, and an improved deep learning model. We then investigated changes in global atmospheric CO2 and anomalies from 2015 to 2021. The reconstructed XCO2 products show a better agreement with Total Carbon Column Observing Network (TCCON) measurements, with R2 of 0.92 and RSME of 1.54 ppm. The products also provide more accurate information on the global and regional spatial patterns of XCO2 compared to origin carbon satellite monitoring and previous XCO2 products. The global pattern of XCO2 exhibited a distinct increasing trend with a growth rate of 2.32 ppm/year, reaching 414.00 ppm in 2021. Globally, XCO2 showed obvious spatial variability across different latitudes and continents. Higher XCO2 concentrations were primarily observed in the Northern Hemisphere, particularly in regions with intensive anthropogenic activity, such as East Asia and North America. We also validated the effectiveness of our XCO2 products in detecting intensive CO2 emission sources.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "0.05° ", "ds_projection": "", "ds_process_way": "In this study, we utilized Google Earth Engine (GEE) to integrate OCO-2/3 XCO2 data and multiple environmental variables as data inputs. In addition, the attention-based Bidirectional Long Short-Term Memory (At-BiLSTM) model was trained for building the relationship between OCO-2/3 XCO2 and the related environmental variables. Then, we reconstructed the global monthly XCO2 and validated the accuracyof the products against TCCON XCO2 data and the original OCO-2/3 XCO2 data. We also analyzed the spatial and temporal variation of XCO2 over the globe and detect theintense CO2 emission regions.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "CO2", "0.05° 分辨率", "XCO2", "碳排放", "碳动态" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "陈颂超", "email": "chensongchao@zju.edu.cn", "work_for": "浙江大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈颂超", "email": "chensongchao@zju.edu.cn", "work_for": "浙江大学", "country": "中国" } ], "ds_managers": [ { "true_name": "陈颂超", "email": "chensongchao@zju.edu.cn", "work_for": "浙江大学", "country": "中国" } ], "category": "气象" }