本数据是从广东省科学院广州地理研究所姜浩团队发表在ZENODO平台上的名为\"CropLayer: 2-meter resolution cropland mapping dataset for China in 2020\"的数据中下载的新疆的数据。原始数据是按照中国31个省分别压缩发布的，我们下载了新疆的数据，没有对下载的数据进行任何处理。
\n关于矢量数据属性表字段“cls”下记录的数字“1”的含义，通过邮件与数据通讯作者的沟通，了解到“1”仅表示农田，没有其他意义。
\n", "ds_source": "Croplayer是一款2020年中国全域高精度（2米分辨率）农作物分布数据集，旨在解决现有主流数据（如CLCD、WorldCover、SinoLC等）在省级面积统计偏差大、复杂地形农田（如南方梯田、坝田）细节缺失及破碎化农田（如北方非农干扰区）识别不足等问题。
\n数据论文以预印本的形式现发布在Earth System Science Data(ESSD)的讨论版块，且采用CC BY 4.0许可协议。
\n", "ds_process_way": "该数据集通过融合Mapbox与Google的高分辨率影像，首先利用ResNet模型进行区块级图像质量评估（IQA）筛选高质量数据并修复元数据缺失；进而构建基于Mask2Former语义分割与XGBoost误差评估的主动学习框架，通过迭代优化样本提升农田边界分割精度；最终创新性地整合地理特征、影像质量、区域属性及一致性四类特征，采用加权融合策略生成统一农田图层。
", "ds_quality": "Croplayer实现了88.73%的制图精度，并在全国30个省级行政区的农田面积统计中达成与官方数据误差≤±10%的严格标准（显著优于现有数据集仅1-9省的达标表现），为作物估产、农业结构优化及粮食安全预警提供了高精度、高统计一致性的空间数据支撑。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "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": "login-access", "ds_total_size": 7098170250, "ds_files_count": 424, "ds_format": "", "ds_space_res": "2米", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "GCS_WGS84", "ds_thumbnail": "9b912b52-f65c-4e6d-9186-3f1269d73921.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Jiang, H., Zhou, X., & Ku, M. (2025). CropLayer: 2-meter resolution cropland mapping dataset for China in 2020 (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14726428", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-08-10 00:30:53", "first_publish_time": null, "last_updated": "2025-08-10 00:30:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "cf_county_xinjiang", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "CropLayer-2m crop cultivated land data for cities and counties in Xinjiang, China in 2020", "ds_abstract": "This data is downloaded from Xinjiang's data titled \"CropLayer: 2-meter resolution cropland mapping dataset for China in 2020\" published by Jiang Hao's team of Guangzhou Institute of Geography, Guangdong Province Academy of Sciences on the ZENODO platform. The raw data was compressed and released according to 31 provinces in China. We downloaded the data from Xinjiang and did not process the downloaded data.
\nRegarding the meaning of the number \"1\" recorded under the field \"cls\" of the vector data attribute table, through email communication with the author of the data communication, we learned that \"1\" only represents farmland and has no other meaning.
\n", "ds_source": "Croplayer is a 2020 China global high-precision (2-meter resolution) crop distribution dataset. It aims to solve the problems of large statistical deviations in existing mainstream data (such as CLCD, WorldCover, SinoLC, etc.) in provincial area, missing details of complex terrain farmland (such as southern terraces and dam fields), and insufficient identification of fragmented farmland (such as northern non-agricultural interference areas).
\nThe data papers are now published in the discussion section of Earth System Science Data(ESSD) in pre-printed form and are under the CC BY 4.0 license agreement.
\n", "ds_process_way": "By fusing high-resolution images from Mapbox and Google, the dataset first uses the ResNet model to conduct block-level image quality assessment (IQA) to screen high-quality data and correct missing metadata; and then builds an active learning framework based on Mask2Former semantic segmentation and XGBoost error assessment to improve the accuracy of farmland boundary segmentation by iteratively optimizing samples; finally innovatively integrates the four types of characteristics: geographical features, image quality, regional attributes and consistency, and uses a weighted fusion strategy to generate a unified farmland layer.
", "ds_quality": "Croplayer has achieved a mapping accuracy of 88.73%, and has reached a strict standard of error of ≤±10% with official data in farmland area statistics of 30 provincial administrative regions across the country (significantly better than the performance of only 1-9 provinces in the existing dataset), providing high-precision and high-statistical consistency spatial data support for crop yield estimation, agricultural structure optimization and food security early warning.
", "ds_ref_instruction": "Jiang, H., Zhou, X., & Ku, M. (2025). CropLayer: 2-meter resolution cropland mapping dataset for China in 2020 (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14726428", "ds_projection": "GCS_WGS84", "ds_space_res": "2 meters" } }, "license_type": null, "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "农业", "农作物", "耕地" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ "李锦" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "李锦" ], "category": "农业" }