长时间序列和大规模水稻日历数据集为以水稻为基础的种植系统的农业规划和田间管理提供了宝贵的信息。然而，目前的区域级水稻日历数据集不能准确区分中国的水稻季节，给作物模型模拟和气候变化影响分析带来了不确定性。中国水稻年历数据集与中国农业气象站（AMSs）实地观测到的早稻、中稻和晚稻物候期显示出良好的一致性。根据中国 2003 年至 2022 年的日历数据，早稻、中稻和晚稻的插秧日期每十年分别偏移 +0.7、-0.7 和 -5.1DOY（年日）；早稻、中稻和晚稻的抽穗日期每十年分别偏移 -0.5、+2.7 和 -5.1DOY（年日）。 早稻、中稻和晚稻的抽穗期每 10 年分别偏移-0.5、+2.7 和-0.6 个 \"年日\"；早稻、中稻和晚稻的成熟期每 10 年分别偏移-0.7、+3.8 和-1.6 个 \"年日\"。中国水稻年历可用于研究和优化气候和土地利用变化下中国水稻种植的时空结构。
", "ds_source": "基于卫星遥感数据，我们提取了 2003 年至 2022 年中国早、中、晚三季水稻的插秧期、抽穗期和成熟期，建立了多季水稻历法数据集 ChinaRiceCalendar-中国早稻、中稻和晚稻的季节性作物日历。
", "ds_process_way": "将 PhenoRice 算法与生长季节划分方法相结合，以提取不同生长季节的水稻像素和作物日历。首先，我们根据每个图像像素的加权平滑 EVI 时间序列曲线，确定可能的作物生长期。然后，我们将可能的作物生长期输入PhenoRice算法，以划分潜在的生长季节，并检查相应的 EVI 时间序列是否属于水稻。最后，我们估算了水稻的播种期、抽穗期和成熟期，并根据各自的插秧和成熟时间将其分为早、中、晚三季日历。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2003-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "东北平原，黄淮海平原，黄土高原，长江中下游地区，华南地区，云贵高原，四川盆地及周边地区", "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": 3494650571, "ds_files_count": 91, "ds_format": ".tif", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "f3a2b5c5-5693-4ce4-a27e-a8877328a56d.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": "a3ce23a2-c353-4383-a544-65c8f218579f", "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 15:47:36", "last_updated": "2025-05-29 11:22:05", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.DVN.DB6449.2024", "i18n": { "en": { "title": "ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China（2003-2022）", "ds_format": ".tif", "ds_source": "Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis.The ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by +0.7, −0.7, and −5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by −0.5, +2.7, and −0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by −0.7, +3.8, and −1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change.
", "ds_time_res": "", "ds_acq_place": "Northeast Plain, Huang Huai Hai Plain, Loess Plateau, middle and lower reaches of the Yangtze River, South China, the Yunnan-Guizhou Plateau, Sichuan Basin and surrounding areas", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "We combined the PhenoRice algorithm with a growing season division method to extract rice pixels and cropping calendars in different growing seasons. Firstly, we identified possible crop heading periods based on a weighted and smoothed EVI time series curve in each image pixel. Then we input the possible heading periods into the PhenoRice algorithm to divide potential growing seasons and check whether the corresponding EVI time series belongs to rice. Lastly, we estimated rice planting, heading, and maturity dates and categorized them into early-, middle-, and late-season calendars according to the respective transplanting and maturity times.
", "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": [ "水稻季节", "中国农业气象站", "ChinaRiceCalendar数据集" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北平原", "黄淮海平原", "黄土高原", "长江中下游地区", "华南地区", "云贵高原", "四川盆地及周边地区" ], "ds_time_tags": [ 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "王绍强", "email": "sqwang@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" }, { "true_name": "王小博", "email": "wxbwxb1995@163.com", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王绍强", "email": "sqwang@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "王绍强", "email": "sqwang@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "生态" }