作物物候为地表物候动态监测和建模以及作物管理和生产提供了重要信息。以往的大多数研究主要是在站点尺度上研究作物物候，然而，大尺度地表物候动态监测和建模需要高分辨率的作物物候动态空间显式信息。在本研究中，我们基于全球陆面卫星叶面积指数（LAI）产品，制作了 2000 年至 2015 年三种主要作物的 1 km 网格作物物候数据集，称为 ChinaCropPhen1km。首先，我们比较了三种常见的平滑方法，并针对不同作物和地区选择了最适合的方法。然后，我们开发了一种基于最优滤波的物候检测（OFP）方法，该方法结合了基于拐点和基于阈值的方法，在 1 km 空间分辨率下检测了中国三种主要作物的关键物候期。最后，我们建立了 2000-2015 年中国三种主要作物的高分辨率网格物候产品。与中国气象局农业气象站（AMS）的密集物候观测数据相比，该数据集具有较高的精度，检索到的物候日期误差小于 10 d，较好地表现了观测到的物候动态在站点尺度上的时空格局。经过验证的数据集可用于多种用途，包括改进大面积的农业系统或地球系统建模。
", "ds_source": "1）ChinaCropPhen1km 输入数据：2000-2015 年基于 MODIS 的改进 LAI 数据集（GLASS LAI）来自 Liang 等（2013；http://glass-product.bnu.edu.cn/?pid=3&c=1，最后访问日期：2020 年 1 月）。\n
\n2）ChinaCropPhen1km 验证数据：2000-2013 年玉米、水稻和小麦作物物候观测记录来自中国气象局管理的 AMSs（https://data.cma.cn/，最后访问日期：2019 年 12 月）。这些物候信息由训练有素的农业技术人员在试验田进行观测和记录，然后由中国农业气象监测系统（CAMMS）进行检查和管理。
", "ds_process_way": "数据处理过程如下 (1) 数据预处理；(2) 选择耕地样方网格，确定合适的平滑方法；(3) 确定基于最优滤波的物候检测（OFP）方法；(4) 生成 ChinaCropPhen1km 数据集。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "中国", "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": "apply-access", "ds_total_size": 10118184024, "ds_files_count": 2, "ds_format": ".tif", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2a3cd038-b66a-40ce-8814-3e39ee0fb9ae.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": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "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": "2023-11-27 15:38:26", "last_updated": "2023-11-27 15:38:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB4098.2023", "i18n": { "en": { "title": "ChinaCropPhen1km: Phenological Dataset of Three Major Crops in China Based on LAI Products (2000-2015)", "ds_format": ".tif", "ds_source": "（1）ChinaCropPhen1km input data：An improved MODIS-based LAI dataset (GLASS LAI)from 2000 to 2015 was from Liang et al. (2013; http://glass-product.bnu.edu.cn/?pid=3&c=1, last access: January 2020). \n
\n（2）ChinaCropPhen1km validation data：The crop phenology observation records from 2000 to 2013 of maize, rice and wheat crops were obtained from AMSs,which were governed by the CMA (https://data.cma.cn/, last access: December 2019). Such phenology information was observed and recorded by well-trained agricultural technicians in the experimental field and then checked and managed by the Chinese Agricultural Meteorological Monitoring System (CAMMS).
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need highresolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area .
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "The data processes are as follows: (1) data preprocessing, (2) selecting the cropland sample grid to determine the suitable smoothing method, (3) determining the optimal filter-based phenology detection (OFP) approach and(4) generating the ChinaCropPhen1km dataset.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation 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_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "GLASS LAI", "1 公里", "中国三大主粮作物", "作物物候信息", "OFP", "环境科学" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 ], "ds_contributors": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "生态" }