本数据集是通过修订光利用效率模型（即 EC-LUE 模型），生成了纬度为 0.05∘、经度为 0.05∘、时间间隔为 8 d 的全球长期 GPP 数据。在修订后的EC-LUE模型中，整合了几个主要环境变量的规定：大气二氧化碳浓度、辐射成分和大气水汽压差（VPD）。这些环境变量的长期变化可能对全球植被生产力产生重大影响。来自 FLUXNET2015 数据集的 95 个高塔的涡度协方差（EC）测量数据被用于校准和验证模型，这些数据涵盖了全球九种主要生态系统类型。在 95 个观测点中，修订后的 EC-LUE 模型可解释 71% 的年度 GPP 空间变化。在 95% 以上的观测点，塔估和模型模拟的 GPP 季节变化相关系数（R2）大于 0.5。在所有 55 个观测年限超过 5 年的站点中，塔估和模式模拟的年平均 GPP 的平均R2为 0.44，明显高于原 EC-LUE 模式（R2=0.36）和其他 LUE 模式（R2从 0.06 到 0.30 不等，平均值为 0.16）。在全球尺度上，由光利用效率模型、机器学习模型和基于过程的生物物理模型得出的 GPP 在大小和年际变化上存在很大差异。修订后的EC-LUE模型将1982年至2017年的全球平均GPP量化为106.2±2.9 Pg C yr-1，趋势为0.15 Pg C yr-1。灵敏度分析表明，EC-LUE 模式模拟的 GPP 对大气 CO2浓度、VPD 和辐射敏感。在 1982-2017 年期间，VPD 的增加（Pg C yr-1）部分抵消了 CO2肥化对全球 GPP 的影响（0.22±0.07 Pg C yr-1）。环境变量的长期变化可以很好地反映在全球 GPP 中。总体而言，修订后的 EC-LUE 模式能够提供可靠的全球 GPP 长期估算值。
", "ds_source": "1、 涡度协方差塔数据：FLUXNET2015数据集（http://www.fluxdata.org）包括 200 多个 碳通量、能量通量和气象变量的变量 由FLUXNET社区在现场收集和处理；
\n2、全球范围的数据
\n（1）Air temperature、Dew point temperature、Direct PAR、Diffuse PAR、来源于https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ ；
\n（2）LAI数据来源于http://www.glass.umd.edu/Download.html;
\n（3）Land cover map数据来源于https：//lpdaac.usgs.gov/products/mcd12q1v006/ ;
\n（4）C4 crop percentage数据来源于https//doi.org/10.3334/ORNLDAAC/932 ;
\n（5）CO2 concentration数据来源于https//www.esrl.noaa.gov/gmd/ccgg/trends/ 。
", "ds_process_way": "1、 利用FLUXNET2015数据集中的95个EC站点对数据集中的95个EC站点进行优化 参数并评估修订后的EC-LUE模型的性能；
\n2、采用IGBP分类方案的MCD12Q1产品作为输入土地覆被图。
\n3、The ISLSCP II C4植被百分比图用于 分离 C3和 C4作物；
\n4、美国国家海洋和大气管理局（NOAA）地球系统研究实验室 （ESRL）一氧化碳浓度数据集用于表示一氧化碳施肥效果。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 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": "login-access", "ds_total_size": 9608517854, "ds_files_count": 2, "ds_format": "HDF", "ds_space_res": "0.05°", "ds_time_res": "8天", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d3103e98-6c2d-4733-a662-506d07493c65.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "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": "2023-11-27 15:38:20", "last_updated": "2025-05-29 11:31:43", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB4095.2023", "i18n": { "en": { "title": "Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017", "ds_format": "hdf", "ds_source": "1. Vorticity covariance tower data: FLUXNET2015 dataset（ http://www.fluxdata.org ）The variables including over 200 carbon flux, energy flux, and meteorological variables were collected and processed by the FLUXNET community on site\n
2. Global data
\n(1) Air temperature, Dew point temperature, Direct PAR, Diffuse PAR, sourced from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ \n
(2) LAI data sourced from http://www.glass.umd.edu/Download.html \n
(3) The Land cover map data is sourced from https://lpdaac. usgs. gov/products/mcd12q1v006/\n
(4) C4The data for the crop percentage is sourced from https/doi. org/10.3334/ORNLDAAC/932\n
(5) CO concentration data is sourced from https//www.esrl.noaa. gov/gmd/ccgg/trends/", "ds_quality": "
The data quality is good", "ds_ref_way": "", "ds_abstract": "
This dataset generated global long-term GPP data with a latitude of 0.05∘, a longitude of 0.05∘, and a time interval of 8 days by revising the light utilization efficiency model (i.e. EC-LUE model). In the revised EC-LUE model, several major environmental variables were integrated: atmospheric carbon dioxide concentration, radiation composition, and atmospheric water vapor pressure difference (VPD). The long-term changes in these environmental variables may have a significant impact on global vegetation productivity. The vorticity covariance (EC) measurement data from 95 tall towers from the FLUXNET2015 dataset were used to calibrate and validate the model, covering nine major ecosystem types worldwide. The revised EC-LUE model can explain 71% of the annual GPP spatial changes among 95 observation points. At over 95% of observation points, the correlation coefficient (R2) between tower estimation and model simulation of GPP seasonal variation is greater than 0.5. Among all 55 stations with observation years exceeding 5 years, the average annual GPP simulated by the tower estimation and model was 0.44, significantly higher than the original EC-LUE model (R2=0.36) and other LUE models (R2ranging from 0.06 to 0.30, with an average value of 0.16). On a global scale, there are significant differences in the size and interannual variations of GPP obtained from light utilization efficiency models, machine learning models, and process based biophysical models. The revised EC-LUE model quantifies the global average GPP from 1982 to 2017 as 106.2 ± 2.9 Pg C yr-1, with a trend of 0.15 Pg C yr-1. Sensitivity analysis shows that the GPP simulated by the EC-LUE model is sensitive to atmospheric CO2concentration, VPD, and radiation. During the period from 1982 to 2017, the increase in VPD (Pg C yr-1) partially offset the impact of CO2fertilization on global GPP (0.22 ± 0.07 Pg C yr-1). The long-term changes in environmental variables can be well reflected in the global GPP. Overall, the revised EC-LUE model can provide reliable long-term estimates of global GPP
", "ds_time_res": "8天", "ds_acq_place": "Global", "ds_space_res": "0.05°", "ds_projection": "", "ds_process_way": "1. Using 95 EC sites in the FLUXNET2015 dataset to optimize parameters and evaluate the performance of the revised EC-LUE model for 95 EC sites in the dataset\n
2. The MCD12Q1 product using the IGBP classification scheme is used as the input land cover map\n
3. The ISLSCP II C4 vegetation percentage map is used to separate C3 and C4 crops\n
4. The National Oceanic and Atmospheric Administration (NOAA) Earth Systems Research Laboratory (ESRL) carbon monoxide concentration dataset is used to represent the effectiveness of carbon monoxide fertilization", "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": [ "初级生产总值", "全球", "GPP" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017 ], "ds_contributors": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "category": "遥感及产品" }