具有明确生物物理意义的叶面积指数（LAI）是描述陆地生态系统特征的关键变量。长期的全球 LAI 数据集已成为监测植被动态和探索其与其他地球成分相互作用的基础数据支持。然而，目前的 LAI 产品在时空一致性方面存在一些局限性。在这项研究中，我们采用了反向传播神经网络（BPNN）和数据整合方法，生成了1982-2020年期间的新版半月全球存量建模与绘图研究（GIMMS）LAI产品，即GIMMS LAI4g。GIMMS LAI4g的重要意义在于使用了最新的北京大学GIMMS归一化差异植被指数（NDVI）产品和360万个高质量的全球Landsat LAI样本，以消除卫星轨道漂移和传感器退化的影响，并建立时空一致的BPNN模型。结果表明，与前者（GIMMS LAI3g）和两个主流 LAI 产品（全球地表卫星（GLASS）LAI 和长期全球测绘（GLOBMAP）LAI）相比，GIMMS LAI4g 利用实地 LAI 测量和大地遥感卫星 LAI 样本显示出更高的精度和更低的低估率。GIMMS LAI4g 产品可能有助于缓解全球长期植被变化研究之间的分歧，也可能有利于地球和环境科学的模型开发。
", "ds_source": "本研究共使用了 8 个全球数据集，即 PKU GIMMS NDVI、Landsat LAI 样本数据集、MODIS 土地覆盖类型、再处理的 MODIS LAI、GLASS LAI、GLOBMAP LAI、GIMMS LAI3g 和野外 LAI 测量。PKU GIMMS NDVI 是生成 GIMMS LAI4g 的主要数据源。Landsat LAI 样本数据集用作机器学习模型建立和产品评估的 LAI 参考。现场 LAI 测量也用于产品评估。MODIS Land-Cover Type 产品在 LAI 建模中提供了植被生物群落类型。再加工的 MODIS LAI 用于扩展 GIMMS LAI4g 的时间覆盖范围。GLASS LAI、GLOBMAP LAI 和 GIMMS LAI3g 是三种全球主流的 LAI 产品，旨在进行相互比较。
", "ds_process_way": "该方法包括三个关键步骤 (1）根据北京大学 GIMMS NDVI、Landsat LAI 样本和其他解释变量，通过生物群落特定的 BPNN 模型生成 GIMMS LAI4g 产品；（2）在两者重叠的时间跨度（2004-2015 年）内，使用像素融合方法将 GIMMS LAI4g 产品与重新处理的 MODIS LAI 产品进行合并；（3）使用野外 LAI 测量和 Landsat LAI 样本对 GIMMS LAI4g 产品进行评估，并将其与其他全球 LAI 产品进行比较。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 63.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 6068786902, "ds_files_count": 10, "ds_format": "TIFF", "ds_space_res": null, "ds_time_res": "半个月", "ds_coordinate": "无", "ds_projection": " Geographic", "ds_thumbnail": "14ada18e-6851-4382-81e4-7703795cabf9.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": "2024-11-28 11:01:18", "last_updated": "2025-06-30 16:18:33", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6648.2024", "i18n": { "en": { "title": "Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) （1982—2020 ）", "ds_format": "TIFF", "ds_source": "A total of eight global datasets were used in this study, namely, the PKU GIMMS NDVI, Landsat LAI sample dataset, MODIS Land-Cover Type, reprocessed MODIS LAI, GLASS LAI, GLOBMAP LAI, GIMMS LAI3g, and field LAI measurements. The PKU GIMMS NDVI was the primary data source from which the GIMMS LAI4g was generated. The Landsat LAI sample dataset was used as the LAI reference in machine learning model establishment and product evaluation. The field LAI measurements were also employed for product evaluation. The MODIS Land-Cover Type product provided vegetation biome types in the LAI modeling. The reprocessed MODIS LAI was used to extend the temporal coverage of the GIMMS LAI4g. The GLASS LAI, GLOBMAP LAI, and GIMMS LAI3g are three mainstream global LAI products that were included for an inter-comparison purpose.
", "ds_quality": "The data quality is good", "ds_ref_way": "", "ds_abstract": "
Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples.The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences.
", "ds_time_res": "半个月", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": " Geographic", "ds_process_way": "The methodology includes three key steps : (1) generating the GIMMS LAI4g product from biome-specific BPNN models based on PKU GIMMS NDVI, Landsat LAI samples, and other explanatory variables; (2) consolidating the GIMMS LAI4g product with the reprocessed MODIS LAI product using a pixel-wise fusion method in their overlapping time span (2004–2015); and (3) evaluating the GIMMS LAI4g product using field LAI measurements and Landsat LAI samples and comparing it with other global LAI products.
", "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", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "LAI", "BPNN", "NDVI", "GIMMS LAI4g" ], "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, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "朱再春", "email": "zhu.zaichun@pku.edu.cn", "work_for": "北京大学深圳研究生院城市规划与设计学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "朱再春", "email": "zhu.zaichun@pku.edu.cn", "work_for": "北京大学深圳研究生院城市规划与设计学院", "country": "中国" } ], "ds_managers": [ { "true_name": "朱再春", "email": "zhu.zaichun@pku.edu.cn", "work_for": "北京大学深圳研究生院城市规划与设计学院", "country": "中国" } ], "category": "生态" }