本数据集为塔吉克斯坦哈特隆州（Khatlon）2023年耕地作物类型/种植结构空间分布数据，以地块（面状图斑）为基本单元。每个图斑携带两个属性字段：clazz为作物/地类类型编码（0–33编码体系），name为对应的中文名称（如棉花、向日葵、玉米、水稻、小麦、果树、草地等）。数据以ESRI Shapefile格式存储，地理坐标系为WGS-84（EPSG:4326）， 空间分辨率源自10米级卫星影像。数据集反映了研究区2023年主要作物的空间分布格局。\n
以面状图斑（地块）为基本单元的作物类型矢量数据，每个图斑含两个核心属性：\n
clazz：作物/地类类型编码（0–33 编码体系）\n
name：对应中文名称（棉花、向日葵、玉米等）\n
坐标系为 WGS-84（EPSG:4326）， 格式为 ESRI Shapefile，空间分辨率源自 Sentinel-2 10m 影像。", "ds_source": "
本数据集以欧空局Sentinel-2（S_2A）多光谱卫星影像为主要数据源（蓝、绿、红、3个红边、双近红外、水汽及双短波红外共11个波段），通过Google Earth Engine云平台获取，成像时间为2023年5月至10月；同时采用项目积累的地块矢量边界数据，以及通过野外地面调查获取的作物类型训练样本。", "ds_process_way": "
首先，按作物物候期划分苗期（5月1日–6月1日）、生长旺季（7月1日–8月1日）、成熟收获期（9月1日–10月15日）三个时间窗口，分别以整景云量阈值（≤10%/15%）进行影像滤波；对三个时相分别构建NDVI时序特征与11波段中值合成光谱特征，共33个特征变量表征作物物候差异。\n
其次，采用随机森林分类器进行作物分类，训练样本按7:3随机划分训练集与验证集，以10米尺度采样；\n
最后，以地块为单元对像元级分类结果进行众数统计（10米尺度），取图斑内占优类型作为该地块作物类型，仅保留面状要素并导出为Shapefile；对导出数据进行属性规范化：按编码映射表添加中文名称字段。", "ds_quality": "
采用整景云量阈值滤波与中值合成相结合的方法控制云污染；利用多时相NDVI与光谱特征增强作物物候可分性；以30%独立验证样本进行精度评价，总体精度为75.46%，Kappa系数为0.65，分类结果与地面真实情况具有较高一致性；经地块众数法后处理有效抑制了椒盐噪声，保证地块内部类型一致性；经完整性校验，全部图斑的clazz与name字段无空值，编码与名称一一对应无冲突。", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "塔吉克斯坦,哈特隆州", "ds_acq_lon_east": 70.45333333333333, "ds_acq_lat_south": 36.9225, "ds_acq_lon_west": 67.79166666666667, "ds_acq_lat_north": 38.69916666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 5682170, "ds_files_count": 2, "ds_format": "*.shp", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7a4e034e-6c14-4db0-9bf4-3156b6ccea31.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "b6ee8072-db07-4053-bcbc-78e22d72c5ec", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "210" ], "quality_level": 0, "publish_time": "2026-10-14 15:19:56", "last_updated": "2026-10-14 15:19:56", "protected": false, "protected_to": "2027-09-05 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7833.2026", "i18n": { "en": { "title": "Major Crop Planting Structure of Khatlon Region (2023)", "ds_format": "*.shp", "ds_source": "
This dataset uses ESA Sentinel-2 (S_2A) multi-spectral satellite images as the main data source (blue, green, red, 3 red edges, dual near infrared, water vapor and dual short wave infrared, a total of 11 bands). Obtained through the Google Earth Engine cloud platform, the imaging time is from May to October 2023; at the same time, the plot vector boundary data accumulated by the project and crop type training samples obtained through field ground surveys are used.", "ds_quality": "
A method that combines whole scene cloud volume threshold filtering and median synthesis is used to control cloud pollution; multi-temporal NDVI and spectral characteristics are used to enhance crop phenology separability; 30% independent verification samples are used for accuracy evaluation, and the overall accuracy is 75.46%. The Kappa coefficient is 0.65, and the classification results are highly consistent with the actual ground conditions; post-processing by the plot mode method effectively suppresses salt and pepper noise and ensures the consistency of internal plots; After integrity check, there are no null values in the clazz and name fields of all image spots, and there is no conflict between the codes and names one by one.", "ds_ref_way": "", "ds_abstract": "
This dataset is the spatial distribution data of cultivated land crop types/planting structures in Khatlon Oblast, Tajikistan in 2023, with plots (planar patches) as the basic unit. Each pattern carries two attribute fields: clazz is the crop/land type code (0 - 33 coding system), and name is the corresponding Chinese name (such as cotton, sunflower, corn, rice, wheat, fruit tree, grassland, etc.). The data is stored in ESRI Shapefile format, the geographical coordinate system is WGS-84 (EPSG:4326), and the spatial resolution is derived from 10-meter-level satellite imagery. The dataset reflects the spatial distribution pattern of major crops in the study area in 2023.\r\n
Crop type vector data with area patches (plots) as the basic unit. Each patch contains two core attributes:\r\n
clazz: Crop/land type coding (0 - 33 coding system)\r\n
name: Corresponding to Chinese name (cotton, sunflower, corn, etc.)\r\n
The coordinate system is WGS-84 (EPSG:4326), the format is ESRI Shapefile, and the spatial resolution is derived from the Sentinel-2 10m image.", "ds_time_res": "", "ds_acq_place": "Tajikistan, Khatlon Oblast", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
First, three time windows are divided into seedling stage (May 1-June 1), peak growth season (July 1-August 1), and mature harvest period (September 1-October 15) according to crop phenological period. Image filtering is carried out using the whole scene cloud amount threshold (≤10%/15%); NDVI time series characteristics and 11-band median synthetic spectral characteristics are constructed for the three time phases respectively, and a total of 33 characteristic variables characterize crop phenological differences.\r\n
Secondly, a random forest classifier is used for crop classification. The training samples are randomly divided into the training set and the verification set according to a ratio of 7:3, and samples are taken at a scale of 10 meters;\r\n
Finally, carry out mode statistics on the pixel-level classification results based on the plot as a unit (10-meter scale), take the dominant type within the plot as the crop type of the plot, and only retain the area features and export them as Shapefiles; standardize the attributes of the exported data: add a Chinese name field according to the code map table", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "allow_update_data": false, "created_from": "admin", "ds_topic_tags": [ "种植结构" ], "ds_subject_tags": [ "农学" ], "ds_class_tags": [], "ds_locus_tags": [ "哈特隆州" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ { "true_name": "钱静", "email": "jing.qian@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "孙博", "email": "sunbo@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "邢秀为", "email": "xw.xing@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "category": "生态" }