冬小麦、冬大麦、冬黑麦和三叶草等冬季三叶草作物在人类饮食中占有重要地位，世界各地都有种植，因此准确的冬季三叶草作物空间分布信息对于监测作物生产和粮食安全至关重要。然而，由于现有的作物绘图方法依赖于训练样本，限制了其在全球范围内的应用，因此仍然缺乏全球高分辨率的冬季三叶草作物地图。在本研究中，我们提出了一种基于冬三叶草作物指数（WTCI）的全球冬三叶草作物绘图新方法。这是一种新的无样本方法，可根据冬生三叶草作物从抽穗期到收获期的归一化差异植被指数（NDVI）特征与其他类型植被的差异来识别冬生三叶草作物。基于这一新方法，我们首次绘制了 2017 年至 2022 年全球 30 米分辨率的冬凌草作物分布图。利用田野调查样本和谷歌地球样本进行的验证表明，该方法表现出令人满意的性能和稳定的时空转移性，生产者准确率、用户准确率和总体准确率分别为 81.12%、87.85% 和 87.7%。此外，与耕地数据层（CDL）和地块识别系统（LPIS）数据集相比，美国和欧洲大部分地区的总体准确率和 F1 分数均超过 80% 和 0.75。几乎所有调查县或地区的冬令三叶草作物识别面积与农业统计面积一致，识别面积与统计面积的相关系数（R2）均超过 0.6，而相对平均绝对误差（RMAE）在所有六年中均小于 30%。总之，本研究提供了一种可靠的、无需任何训练样本的冬凌草作物自动识别方法。高分辨率的全球冬凌草作物分布图有望为多种农业应用提供支持。
", "ds_source": "本研究使用的数据包括 (1) Landsat 7、Landsat 8 和 Sentinel-2 的反射率数据；(2) Sentinel-1 的合成孔径雷达 (SAR) 数据；(3) 实地调查样本和目视判读样本；(4) 农业统计数据。利用反射数据和合成孔径雷达数据生成冬季三叶草作物图；利用实地调查样本、目视判读样本和农业统计数据评估拟议方法的性能。
", "ds_process_way": "在本研究中，我们提出了一种基于冬三叶草作物指数（WTCI）的全球冬三叶草作物绘图新方法。这是一种新的无样本方法，可根据冬生三叶草作物从抽穗期到收获期的归一化差异植被指数（NDVI）特征与其他类型植被的差异来识别冬生三叶草作物。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2017-01-01 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": 30926161775, "ds_files_count": 625, "ds_format": "tif", "ds_space_res": "30", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e84c0f3b-6838-475d-801f-05da38eac1d7.png", "ds_thumb_from": 2, "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": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "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": "2024-05-22 09:02:40", "last_updated": "2025-05-29 11:01:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.12361", "i18n": { "en": { "title": "Global 30-m resolution distribution maps of winter-triticeae crops from 2017 to 2022", "ds_format": "tif", "ds_source": "The data used in this study included: (1) reflectance data from Landsat 7, Landsat 8 and Sentinel-2; (2) Synthetic Aperture Radar (SAR) data from Sentinel-1; (3) field survey samples and visual interpretation samples; (4) agricultural statistical data. Reflectance data and SAR data were used to generate winter-triticeae crops maps; field survey samples, visual interpretation samples and agricultural statistical data were used to assess the performance of the proposed method.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Winter-triticeae crops, such as winter wheat, winter barley, winter rye, and triticale, are important in human diets and planted worldwide, and thus accurate spatial distribution information of winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crops mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to the harvesting stages and those of other types of vegetation. Based on this new method, we produced the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022. Validation using field survey samples and Google Earth samples indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer’s accuracy, user’s accuracy and overall accuracy of 81.12%, 87.85% and 87.7%, respectively. Moreover, compared with the Cropland Data Layer (CDL) and the Land Parcel Identification System (LPIS) datasets, the overall accuracy and F1 score in most regions of the United States and Europe were more than 80% and 0.75. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all investigated counties or regions, and the correlation coefficient (R2) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30% in all six years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "30", "ds_projection": "", "ds_process_way": "In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crops mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to the harvesting stages and those of other types of vegetation.
", "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": [ "冬小麦", "冬大麦", "冬黑麦", "三叶草", "冬季三叶草作物地图" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "袁文平", "email": "yuanwp3@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "category": "生态" }