该数据集基于反向传播神经网络（BPNN）模型和像素整合方法，开发了新一代 GIMMS LAI 产品（GIMMS LAI4g，1982-2020 年）。GIMMS LAI4g的特点是使用了北京大学GIMMS NDVI产品和大量高质量的Landsat LAI样本。最近发布的 PKU GIMMS NDVI 有效地消除了 NOAA 轨道漂移和 AVHRR 传感器退化的影响，而这一直是现有 LAI 产品的关键问题。高质量的全球陆地卫星 LAI 样本总数达 360 万个，时间覆盖 1984-2015 年，这为创建时空一致的 BPNN 模型提供了便利。时空一致的 GIMMS LAI4g 产品覆盖了从 1982 年到 2020 年的时间跨度，时间分辨率分别为15天。它可以为长期植被监测和高精度、高可靠性的模型开发提供有力的数据支持。
", "ds_source": "本研究共使用了八个全球数据集，即北京大学 GIMMS NDVI、Landsat LAI 样本数据集、MODIS 土地覆被类型、经再处理的 MODIS LAI、GLASS LAI、GLOBMAP LAI、GIMMS LAI3g 和实地 LAI 测量值。PKU GIMMS NDVI 是生成 GIMMS LAI4g 的主要数据源。陆地卫星 LAI 样本数据集被用作机器学习模型建立和产品评估的 LAI 参考。野外 LAI 测量也用于产品评估。MODIS 土地覆被类型产品为 LAI 建模提供了植被生物群落类型。经过再处理的 MODIS LAI 被用于扩展 GIMMS LAI4g 的时间覆盖范围。GLASS LAI、GLOBMAP LAI 和 GIMMS LAI3g 是三个主流的全球 LAI 产品，它们被纳入进来进行相互比较。", "ds_process_way": "
利用北京大学 GIMMS NDVI 和 Landsat LAI 样本来解决遥感和 LAI 参考数据的不确定性问题。利用这些数据，开发了生物群落特定的反向传播神经网络（BPNN）模型，并增加了解释变量（经度和纬度、NDVI 月份以及 NOAA 发射后的编号和年份）。然后根据 BPNN 模型生成 GIMMS LAI4g 产品。最后，通过像素融合方法将 GIMMS LAI4g 与重新处理的 MODIS NDVI 产品合并，将时间覆盖范围扩展到 2020 年。
", "ds_quality": "根据 Landsat LAI 样本对其进行的验证显示，R2为 0.96，均方根误差为 0.32m2m-2，平均绝对误差为 0.162m-2，平均绝对百分比误差为 13.6%，达到了全球气候观测系统提出的精度目标。在陆地大部分地区的大多数植被生物群落中，它的 LAI 性能优于其他产品。它有效地消除了卫星轨道漂移和传感器退化的影响，并在 2000 年前后呈现出更好的时间一致性。与经过再处理的 MODIS LAI 合并后，GIMMS LAI4g 的时间覆盖范围从 2015 年扩展到近期（2020 年），生成的 LAI 趋势在 2000 年前后保持高度一致，并与 MODIS 时代经过再处理的 MODIS LAI 趋势一致。GIMMS LAI4g 产品可能有助于减少全球植被长期变化研究之间的分歧，也有利于地球和环境科学的模型开发。
", "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": "login-access", "ds_total_size": 6068598071, "ds_files_count": 9, "ds_format": "tiff", "ds_space_res": "1/12°", "ds_time_res": "15日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "70012119-92cf-468d-b802-a06ed70791af.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": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-04-25 15:16:27", "last_updated": "2026-01-14 10:45:35", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6427.2024", "i18n": { "en": { "title": "Global Monitoring System Leaf Area Index (GIMMS LAI4g) spatiotemporal consistent dataset (1982-2020) (V1.2)", "ds_format": "tiff", "ds_source": "This study used eight global datasets, namely Peking University GIMMS NDVI, Landsat LAI sample dataset, MODIS land cover types, reprocessed MODIS LAI, GLASS LAI, GLOBMAP LAI, GIMMS LAI3g, and field LAI measurements. PKU GIMMS NDVI is the main data source for generating GIMMS LAI4g. The land satellite LAI sample dataset is used as a LAI reference for machine learning model building and product evaluation. Field LAI measurement is also used for product evaluation. MODIS land cover type products provide vegetation biotic community types for LAI modeling. The reprocessed MODIS LAI is used to extend the time coverage of GIMMS LAI4g. GLASS LAI, GLOBMAP LAI, and GIMMS LAI3g are three mainstream global LAI products that have been included for mutual comparison.", "ds_quality": "
According to the validation of Landsat LAI samples, R2is 0.96, with a root mean square error of 0.32m2m-2, an average absolute error of 0.162m-2, and an average absolute percentage error of 13.6%, achieving the accuracy target proposed by the global climate observation system. In most vegetation communities on land, its LAI performance is superior to other products. It effectively eliminates the effects of satellite orbit drift and sensor degradation, and exhibits better temporal consistency around 2000. After merging with the reprocessed MODIS LAI, the time coverage of GIMMS LAI4g has expanded from 2015 to the near future (2020), and the generated LAI trend remains highly consistent around 2000 and is consistent with the reprocessed MODIS LAI trend of the MODIS era. The GIMMS LAI4g product may help reduce the divergence between global vegetation long-term change studies and also facilitate model development in Earth and environmental science", "ds_ref_way": "", "ds_abstract": "
This dataset is based on the backpropagation neural network (BPNN) model and pixel integration method, and a new generation of GIMMS LAI product (GIMMS LAI4g, 1982-2020) has been developed. The feature of GIMMS LAI4g is the use of Peking University GIMMS NDVI products and a large number of high-quality Landsat LAI samples. The recently released PKU GIMMS NDVI effectively eliminates the effects of NOAA orbital drift and AVHRR sensor degradation, which have been key issues with existing LAI products. The total number of high-quality global land satellite LAI samples reaches 3.6 million, covering the period from 1984 to 2015, which provides convenience for creating spatiotemporal consistent BPNN models. The GIMMS LAI4g product, which is consistent in time and space, covers the time span from 1982 to 2020 with a time resolution of 15 days. It can provide strong data support for long-term vegetation monitoring and high-precision, high reliability model development.
", "ds_time_res": "15日", "ds_acq_place": "Global", "ds_space_res": "1/12°", "ds_projection": "", "ds_process_way": "Using GIMMS NDVI and Landsat LAI samples from Peking University to address the uncertainty issue of remote sensing and LAI reference data. Using this data, a community specific backpropagation neural network (BPNN) model was developed, with explanatory variables added (longitude and latitude, NDVI month, and number and year after NOAA emission). Then generate GIMMS LAI4g products based on the BPNN model. Finally, the GIMMS LAI4g was merged with the reprocessed MODIS NDVI product through pixel fusion method, expanding the time coverage to 2020", "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": [ "GIMMS叶面积指数", "植被动态", "归一化植被指数（NDVI）" ], "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": "曹森", "email": "sencao@szu.edu.cn", "work_for": "深圳大学地理空间信息研究团队", "country": "中国" }, { "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": "曹森", "email": "sencao@szu.edu.cn", "work_for": "深圳大学地理空间信息研究团队", "country": "中国" }, { "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": "曹森", "email": "sencao@szu.edu.cn", "work_for": "深圳大学地理空间信息研究团队", "country": "中国" }, { "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" }