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在站点尺度上研究作物物候；然而，大尺度的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2000-2015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。经过良好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": "基于 MODIS 的 2000 至 2015 年改进型 LAI 数据集（GLASS LAI）来自 Liang 等人（2013 年；http://glass-product.bnu.edu.cn/?pid=3&c=1，最后访问日期：2020 年 1 月）。GLASS LAI 产品由一般回归神经网络（GRNNs）训练生成，该网络由 MODIS LAI 和卫星集合碳轮廓和陆地观测产品变化（CYCLOPES）LAI 产品以及 2001-2003 年期间基准陆地多站点分析和产品相互比较（BELMANIP）站点的再处理 MODIS 反射率（Liang 等，2013 年）融合而成。通过计算多个全球LAI产品与高分辨率LAI参考图之间的均方根误差（RMSE）和判定系数（R2），可以看出，与MODIS LAI产品（MOD15）和Geoland2 BioPar版本1（GEOV1；肖等，2016）相比，GLASS LAI的精度（RMSE=0.78；R2=0.81）相当不错。此外，相互比较表明，GLASS LAI（1 km 空间分辨率的 8 d 合成）比其他 LAI 产品具有更强的时间连续性和空间完整性（Xiao 等，2014 年，2016 年）。它已被应用于植被监测和作物模型同化（Xiao 等，2014；Chen 等，2018a）。\n
\n此外，我们还使用了由中国 1 km 全国土地覆被数据集（NLCD）导出的耕地层作为耕地掩模。具体而言，我们检测了旱地作物（即玉米和小麦）和水稻的关键物候期，它们分别受限于从 NLCD 导出的旱地和水田图层。NLCD 由中国科学院资源与环境科学数据中心提供（http://www.resdc.cn/Default.aspx，最后访问日期：2020 年 1 月），其中还包括多个年代的土地利用数据集，即 2000 年、2005 年、2010 年和 2015 年（Liu 等，2005 年，2014 年）。
", "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": "open-access", "ds_total_size": 10118184024, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c93224dc-4f85-48b3-81bf-7ecc854f1f81.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": "2024-09-27 09:23:58", "last_updated": "2026-05-15 11:56:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6661.2024", "i18n": { "en": { "title": "ChinaCropPhen1km:A high-resolution crop phenological dataset for three staple crops in China based on LAI products(2000-2015)", "ds_format": "tif", "ds_source": "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). The GLASS LAI product was generated with general regression neural networks (GRNNs) trained by the fused LAI from MODIS and Carbon cYcle and Change in Land Observational Products from Ensemble of Satellites (CYCLOPES) LAI products and the reprocessed MODIS reflectance of the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites during the period 2001–2003 (Liang et al., 2013). By computing the root-mean-square error (RMSE) and determination coefficients (R2) between several global LAI products and the high-resolution LAI reference map, it could be shown that the accuracy of the GLASS LAI (RMSE=0.78; R2=0.81) was fairly good compared to that of the MODIS LAI product (MOD15) and Geoland2 BioPar version 1 (GEOV1; Xiao et al., 2016). Moreover, the intercomparison indicated that the GLASS LAI (8 d composites of 1 km spatial resolution) was more temporally continuous and spatially complete than the other LAI products (Xiao et al., 2014, 2016). It has been applied to vegetation monitoring and crop model assimilation (Xiao et al., 2014; Chen et al., 2018a).\r\n
\r\nIn addition, the cultivated-land layer derived from the 1 km National Land Cover Dataset (NLCD) of China was used as cropland masks. Specifically, we detected the key phenological dates for dryland crops (i.e., maize and wheat) and paddy rice, which were restricted on the dryland and paddy field layer derived from the NLCD, respectively. NLCD was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn/Default.aspx, last access: January 2020), which also included several epochs of land use datasets, i.e., 2000, 2005, 2010 and 2015 (Liu et al., 2005, 2014).
", "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 high-resolution 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": "", "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, 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, "recommendation_value": 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, "belong_to_nieer": false, "ds_topic_tags": [ "GLASS LAI", "1 公里", "中国三大主粮作物", "作物物候信息", "基于最优滤波的物候检测方法（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": "骆玉川", "email": "", "work_for": "北京师范大学地理科学学院", "country": "中国" }, { "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" }