本数据根据2017-2019年需要定期灌溉地区的干旱月份和2010-2019年需要偶尔灌溉地区的最干旱月份，基于干旱胁迫下的灌溉表现和机器学习方法得出。全球共有67个碎片，大部分与陆地重叠的瓦片最大范围为21°×21°。GMIE-100采用WGS84坐标系，经纬度投影（EPSG:4326），文件格式为GeoTIFF，灌溉比例用单波段图像表示，像素值对应各自空间网格的灌溉比例，范围为0-1，背景值为-99。该文件为GMIE-100-log_lat.tif，其中lat和log表示中心点的纬度和经度的舍入。tiles的域可以在“tiles of GMIE-100.shp”文件中找到。GCPIS以shapefiles格式存储在zip文件中。", "ds_source": "
数据来源于HARVARD DAtaverse(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HKBAQQ).", "ds_process_way": "
将干旱胁迫下的灌溉表现作为作物生产力稳定和作物耗水量的代表，对于每个灌溉制图区（lMZ），在生长季节确定2017-2019年期间的干旱月份和2010-2019年期间的最干旱月份。通过对2017 - 2019年干旱月份归一化植被指数（NDVl）阈值和最干旱月份NDVI偏差（NDVldev）的识别，实现了灌区与雨耕地的样本分离。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 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": "login-access", "ds_total_size": 6771752002, "ds_files_count": 75, "ds_format": "TIFF", "ds_space_res": "30米", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "经纬度投影（EPSG:4326）", "ds_thumbnail": "725385a7-ecd8-4af3-9f91-1ccf6e233ae2.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a3ce23a2-c353-4383-a544-65c8f218579f", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170" ], "quality_level": 3, "publish_time": "2024-11-28 11:00:55", "last_updated": "2026-01-14 10:52:15", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.DVN.DB6655.2024", "i18n": { "en": { "title": "The world's largest irrigation range and central pivot irrigation system dataset based on irrigation performance under drought stress and machine learning methods (2010-2019)", "ds_format": "TIFF", "ds_source": "
Data sourced from HARVARD DAtaverse（ https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HKBAQQ )。", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
This data is based on the drought months in areas that require regular irrigation from 2017 to 2019 and the driest months in areas that require occasional irrigation from 2010 to 2019, using irrigation performance under drought stress and machine learning methods. There are a total of 67 fragments worldwide, with most of the tiles overlapping with land having a maximum range of 21 °× 21 °. GMIE-100 adopts the WGS84 coordinate system, latitude and longitude projection (EPSG: 4326), and the file format is GeoTIFF. The irrigation ratio is represented by a single band image, and the pixel values correspond to the irrigation ratio of their respective spatial grids, with a range of 0-1 and a background value of -99. The file is GMIE-100-log_1at.tif, where lat and log represent rounding of the latitude and longitude of the center point. The domain of tiles can be found in the 'tiles of GMIE-100. shp' file. GCPIS is stored in shapefiles format in a zip file.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "30米", "ds_projection": "", "ds_process_way": "Using irrigation performance under drought stress as a representative of crop productivity stability and crop water consumption, for each irrigation mapping area (lMZ), determine the drought months during the growing season from 2017 to 2019 and the driest months during the period from 2010 to 2019. By identifying the normalized vegetation index (NDVL) threshold and the driest month NDVI deviation (NDV1dev) from 2017 to 2019, the sample separation between irrigation areas and rainfed land was achieved.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "灌溉产品", "灌溉性能", "中央枢轴灌溉系统", "GMIE-100" ], "ds_subject_tags": [ "地球科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "category": "其他" }