小麦作为全球主要粮食作物之一，对农业贸易格局的形成具有重要影响。中国是全球最大的小麦生产国和消费国，其种植面积广阔且种植体系多样。然而，当前基于遥感技术的小麦制图研究往往依赖于统一的物候特征变量，未能充分考虑中国不同农业生态区小麦生长周期存在的显著差异。此外，大规模训练样本的缺失严重制约了模型精度与时空泛化能力。国内现有研究主要聚焦冬小麦监测与制图，春小麦领域——尤其是华北主要春小麦产区——仍处于研究空白，导致针对性遥感产品严重匮乏。这些局限性阻碍了高精度、空间覆盖全面的小麦分布图集开发，削弱了农业监测与粮食安全评估的完整性。为解决上述问题，本数据集中提出一种跨区域训练样本生成方法，将时序遥感数据与作物分布产品相融合。同时引入省级差异化特征选择策略，以增强模型的区域适应性与分类性能。基于上述方法构建了覆盖2018-2024年的10米分辨率小麦分布数据集（CN_Wheat10），包含全国15个省份的春冬小麦收获面积图及10个省份的冬小麦种植面积精细图。CN_Wheat10不仅提供冬春两季小麦收割面积的空间分布信息，还覆盖主要产区冬小麦种植区域。相较于现有以冬小麦为主的产品，本数据集在空间覆盖范围和作物类型上均有拓展，为中国农业监测与管理提供了更全面的数据支持。
", "ds_source": "本数据集使用一种跨区域训练样本生成方法，将时序遥感数据与作物分布产品相融合。同时引入省级差异化特征选择策略，以增强模型的区域适应性与分类性能。
", "ds_quality": "通过实地调查与高分辨率影像目视判读构建的大规模参考数据集验证表明：CN_Wheat10对冬小麦的制图精度达0.93以上，春小麦精度达0.91以上。与《中国统计年鉴》小麦面积统计数据对比时，省级层面的决定系数(R²)超过0.94，市级层面仍保持在0.88以上。空间分布上，中国小麦呈现东部集聚、西部分散的格局，以冬小麦为主体，春小麦为补充。
", "ds_acq_start_time": "2018-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.01666666666668, "ds_acq_lat_south": 3.8666666666666667, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 4474838421, "ds_files_count": 2, "ds_format": ".tif", "ds_space_res": "10", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "61ad6ec9-856c-4ade-971c-7ffacf5429a3.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "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": "2025-12-29 10:19:14", "last_updated": "2026-01-14 11:02:28", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB7051.2025", "i18n": { "en": { "title": "A 10 meter resolution dataset of spring and winter wheat distribution in China based on time-series remote sensing data (2018-2024)", "ds_format": ".tif", "ds_source": "The validation of a large-scale reference dataset constructed through field investigation and high-resolution image visual interpretation shows that the mapping accuracy of CN-Wheat10 for winter wheat is above 0.93, and for spring wheat it is above 0.91. When compared with the wheat area statistics in the China Statistical Yearbook, the determination coefficient (R ²) at the provincial level exceeds 0.94, while at the municipal level it remains above 0.88. In terms of spatial distribution, Chinese wheat shows a pattern of clustering in the east and dispersion in the west, with winter wheat as the main body and spring wheat as a supplement.
", "ds_ref_way": "", "ds_abstract": "Wheat, as one of the world's major food crops, has a significant impact on the formation of agricultural trade patterns. China is the world's largest producer and consumer of wheat, with a vast planting area and diverse planting systems. However, current wheat mapping research based on remote sensing technology often relies on unified phenological characteristic variables and fails to fully consider the significant differences in wheat growth cycles among different agricultural ecological regions in China. In addition, the lack of large-scale training samples severely restricts the accuracy and spatiotemporal generalization ability of the model. The existing research in China mainly focuses on winter wheat monitoring and mapping, while the field of spring wheat, especially in the major spring wheat producing areas in North China, is still in a research gap, resulting in a serious shortage of targeted remote sensing products. These limitations hinder the development of high-precision and spatially comprehensive wheat distribution atlases, weakening the integrity of agricultural monitoring and food security assessment. To address the aforementioned issues, this dataset proposes a cross regional training sample generation method that integrates time-series remote sensing data with crop distribution products. At the same time, a provincial-level differentiated feature selection strategy is introduced to enhance the regional adaptability and classification performance of the model. Based on the above method, a 10 meter resolution wheat distribution dataset (CN-Wheat10) covering 2018-2024 was constructed, including spring and winter wheat harvest area maps of 15 provinces in China and detailed winter wheat planting area maps of 10 provinces. CN-Wheat10 not only provides spatial distribution information of wheat harvesting area in winter and spring seasons, but also covers the winter wheat planting areas in major production areas. Compared to existing products mainly based on winter wheat, this dataset has expanded in spatial coverage and crop types, providing more comprehensive data support for agricultural monitoring and management in China.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "10", "ds_projection": "", "ds_process_way": "This dataset uses a cross regional training sample generation method to integrate time-series remote sensing data with crop distribution products. At the same time, a provincial-level differentiated feature selection strategy is introduced to enhance the regional adaptability and classification performance of the model.
", "ds_ref_instruction": "" } }, "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, "ds_topic_tags": [ "冬小麦", "遥感", "10m" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "张洪艳", "email": "zhanghongyan@cug.edu.cn", "work_for": "中国地质大学（武汉）计算机学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张洪艳", "email": "zhanghongyan@cug.edu.cn", "work_for": "中国地质大学（武汉）计算机学院", "country": "中国" } ], "ds_managers": [ { "true_name": "张洪艳", "email": "zhanghongyan@cug.edu.cn", "work_for": "中国地质大学（武汉）计算机学院", "country": "中国" } ], "category": "遥感及产品" }