作物物候指标提供了作物生长阶段的基本信息，是农业生态系统管理和产量估算的重要依据。以往的作物物候研究主要使用粗分辨率（如 500 米）卫星数据，如中等分辨率成像分光仪（MODIS）数据。然而，精准农业需要更高分辨率的作物物候信息，以便更好地进行农业生态系统管理，而长期和精细分辨率的陆地卫星观测数据可以满足这一要求。在本研究中，我们利用谷歌地球引擎（GEE）平台上所有可用的陆地卫星图像，生成了中国首个空间分辨率高（30 米）、时间跨度长（1985-2020 年）的全国玉米物候产品。
", "ds_source": "利用了谷歌地球引擎（GEE）平台上所有可用的陆地卫星图像，生成产品。
", "ds_process_way": "首先，我们利用谐波模型提取了长期平均物候指标，包括 v3 期（即第三叶完全展开的日期）和成熟期（即玉米籽粒干重首次达到最大值的日期）。其次，我们通过测量特定年份植被指数达到与其长期平均值相同幅度的日期差异，确定了物候指标的年度动态。得出的玉米物候数据集与农业气象站和 PhenoCam 网络的现场观测结果一致。此外，得出的精细分辨率物候数据集在空间模式和时间动态方面与 MODIS 物候产品非常吻合。此外，我们观察到 2000 年前后玉米物候的时间趋势存在明显差异，这可能是由于气温和降水的变化进一步改变了农耕活动。提取的玉米物候数据集可支持精确的产量估算，并加深我们对未来农业生态系统应对全球变暖的理解。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1958-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 140.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 72.0, "ds_acq_lat_north": 52.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 38068113775, "ds_files_count": 2, "ds_format": ".tif", "ds_space_res": "30", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "46945c03-0558-4281-a26f-253645142aaa.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": "2023-10-23 11:51:02", "last_updated": "2025-05-29 11:33:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB4056.2023", "i18n": { "en": { "title": "A 30-m annual maize phenology dataset from 1985 to 2020 in China(1985-2020)", "ds_format": ".tif", "ds_source": "Utilized all available Landsat images on the Google Earth Engine (GEE) platform to generate products.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Crop phenology indicators provide essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "30", "ds_projection": "", "ds_process_way": "First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent with in situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming.
", "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, "ds_topic_tags": [ "作物物候指标", "高空间分辨率", "长时间跨度", "全国玉米物候产品" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 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": "黄健熙", "email": "jxhuang@cau.edu.cn", "work_for": "中国农业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "黄健熙", "email": "jxhuang@cau.edu.cn", "work_for": "中国农业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "黄健熙", "email": "jxhuang@cau.edu.cn", "work_for": "中国农业大学", "country": "中国" } ], "category": "生态" }