叶面积指数（LAI）是表征植被冠层结构和能量吸收能力的重要参数。中分辨率成像分光仪（MODIS）的叶面积指数因其明确的理论基础、大量的历史时间序列、广泛的验证结果和开放的可访问性，在里程碑式的研究中发挥了重要作用。然而，MODIS LAI 的检索是针对每个像素和特定日期独立计算的，导致时间序列的噪声水平较高，限制了其在光学遥感领域的应用。对 MODIS LAI 的再处理主要依靠时间信息来获得更平滑的 LAI 剖面，而很少利用空间信息，因此很容易忽略真正的 LAI 异常。针对这些问题，我们设计了时空信息合成算法（STICA），用于 MODIS LAI 产品的再处理。该方法综合了像素质量信息、时空相关性和原始检索等多个维度的信息，从而实现了对现有 MODIS LAI 产品的 \"再处理 \"和 \"数据增值\"，最终开发出高质量 LAI（HiQ-LAI）数据集。与地面测量结果相比，HiQ-LAI 的性能优于原始 MODIS 产品，均方根误差（RMSE）或偏差分别从 0.87 或 -0.17 降至 0.78 或 -0.06。这是因为 HiQ-LAI 在捕捉植被物候的季节性和减少异常时间序列波动方面有所改进。代表时间稳定性的时间序列稳定性（TSS）指数表明，全球具有平滑 LAI 时间序列的区域从 31.8%（MODIS）扩大到 78.8%（HiQ），这一改进在光学遥感通常无法实现良好性能的赤道地区更为明显。我们发现，从空间和时间角度来看，HiQ-LAI 与原始 MODIS LAI 相比，都具有更好的连续性和一致性。我们预计，在谷歌地球引擎（GEE）平台上使用 STICA 程序生成的全球 HiQ-LAI 时间序列将大大增强对各种全球 LAI 时间序列应用的支持。
", "ds_source": "高质量叶面积指数（HiQ-LAI）是通过时空信息合成算法（STICA）对 MODIS LAI C6.1 产品进行再处理后得出的。该方法综合了多个维度的信息，包括像素质量信息、时空相关性和原始观测数据，以改进质量较差的 MODIS LAI 原始数据。
", "ds_process_way": "我们提出了一种时空信息构成算法（STICA），旨在减少噪声波动，提高 MODIS LAI 产品的整体质量。该算法直接将先验时空相关信息和多重质量评估（MQA）信息纳入现有的 MODIS LAI 产品中。详细的算法过程可参见 Wang 等人发表的文章（2023 年）。该算法包括四个主要步骤：多重质量评估、采用空间相关信息、采用时间相关信息和多重信息合成。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "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": 38030924994, "ds_files_count": 6, "ds_format": "tif", "ds_space_res": "5000", "ds_time_res": " 8 天", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1aafec31-0191-4be4-97fd-fda451631048.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": "2024-05-21 10:27:40", "last_updated": "2025-06-30 16:18:08", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6466.2024", "i18n": { "en": { "title": "A High-Quality Reprocessed MODIS Leaf Area Index Dataset (HiQ-LAI)(2000-2022)", "ds_format": "tif", "ds_source": "The High-Quality Leaf Area Index (HiQ-LAI) is derived from reprocessed MODIS LAI C6.1 product by SpatioTemporal Information Compositing Algorithm (STICA). This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and original observations, to improve the raw MODIS LAI retrievals with poor quality.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both “reprocessing” and “value-added data” with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or −0.17 to 0.7.
", "ds_time_res": " 8 天", "ds_acq_place": "Global", "ds_space_res": "5000", "ds_projection": "", "ds_process_way": "We proposed a spatiotemporal information composition algorithm (STICA) aimed at reducing noise fluctuations and improving the overall quality of the MODIS LAI product. This algorithm directly incorporates the prior spatiotemporal correlation information and multiple quality assessment (MQA) information into the existing MODIS LAI product. The detailed algorithmic process can be found in the article published by Wang et al. (2023). The algorithm consists of four main steps: multiple quality assessment, employing spatial correlation information, employing temporal correlation information, and multiple information compositing.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation method provided in this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "叶面积指数", "中分辨率成像光谱仪 （MODIS）", "时空信息合成算法（STICA）", "高质量LAI（HiQ-LAI）数据集" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "闫凯", "email": "kaiyan@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "闫凯", "email": "kaiyan@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "闫凯", "email": "kaiyan@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "生态" }