本数据集使用 WGS84 坐标系，GMIE-100 的文件格式为 GeoTIFF。灌溉比例的 GMIE-100 以单波段图像表示。GMIE-100 的像素值与相应空间网格的灌溉比例相对应，范围为 0-1，背景值为 -99。对于需要定期灌溉的地区，数据是利用 2017-2019 年间的干旱月份制作的；对于偶尔需要灌溉的地区，数据是利用 2010-2019 年间最干旱的月份制作的。全球共有 67 个瓦片，如果大部分瓦片与陆地重叠，则最大范围为 21°×21°，否则，范围将根据陆地范围进行调整。文件为 GMIE-100-log_lat.tif，其中lat 和log表示中心点经纬度的四舍五入。瓦片域可在文件 “tiles of GMIE-100.shp ”中找到。GCPIS 以 zip 文件中的 shapefiles 格式存储。", "ds_source": "
为了划定灌溉耕地，我们利用采集的样本计算了 2017 年至 2019 年干旱月份的归一化差异植被指数（NDVI）阈值，以及最干旱月份的 NDVI 与十年平均值的偏差。", "ds_process_way": "
在每个灌溉测绘区 （IMZ） 内，确定了 2017 年至 2019 年生长季节期间发生的干旱月份或 2010 年至 2019 年最干旱的月份。为了划定灌溉农田，利用收集的样本计算了 2017 年至 2019 年干旱月份的归一化差异植被指数 （NDVI） 阈值以及最干旱月份的 NDVI 与十年平均值的偏差。通过将结果与这两种方法之间的更高精度相结合，生成了 100 米分辨率的全球最大灌溉范围数据集 （GMIE-100）。
", "ds_quality": "总体精度达到 83.6%，GMIE-100在100米处的空间分辨率超过了主要灌溉地图的空间分辨率，为支持农业用水量估算和区域粮食安全评估提供了更详细的信息。
", "ds_acq_start_time": "2010-01-01 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": null, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5324815686, "ds_files_count": 2, "ds_format": "tiff，shp", "ds_space_res": "100m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "EPSG:4326", "ds_thumbnail": "b7516293-afb5-4d96-8c56-e7a98fbd9014.png", "ds_thumb_from": 2, "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": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45", "170.99" ], "quality_level": 3, "publish_time": "2024-05-22 10:34:57", "last_updated": "2026-01-14 10:39:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.DVN.DB6471.2024", "i18n": { "en": { "title": "GMIE: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method", "ds_format": "tiff，shp", "ds_source": "In order to delineate irrigated farmland, we used collected samples to calculate the Normalized Difference Vegetation Index (NDVI) threshold for the dry months from 2017 to 2019, as well as the deviation between the NDVI for the driest month and the ten-year average..", "ds_quality": "
The overall accuracy reaches 83.6%, and the spatial resolution of GMIE-100 at 100 meters exceeds the spatial resolution of major irrigation maps, providing more detailed information to support agricultural water consumption estimation and regional food security assessment", "ds_ref_way": "", "ds_abstract": "
This dataset uses the WGS84 coordinate system, and the file format of GMIE-100 is GeoTIFF. The GMIE-100 irrigation ratio is represented in a single band image. The pixel value of GMIE-100 corresponds to the irrigation ratio of the corresponding spatial grid, with a range of 0-1 and a background value of -99. For areas that require regular irrigation, the data was produced using the dry months from 2017 to 2019; For areas that occasionally require irrigation, the data was produced using the driest months from 2010 to 2019. There are a total of 67 tiles worldwide. If most of the tiles overlap with land, the maximum range is 21 ° x 21 °. Otherwise, the range will be adjusted according to the land range. The file is GMIE-100 log_lat.tif, where lat and log represent the rounding of the longitude and latitude of the center point. The tile field can be found in the file \"tiles of GMIE-100. shp\". GCPIS is stored in shapefiles format in a zip file.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "100m", "ds_projection": "EPSG:4326", "ds_process_way": "Within each irrigation mapping area (IMZ), the dry months that occurred during the growing season from 2017 to 2019 or the driest months from 2010 to 2019 were identified. In order to delineate irrigated farmland, the normalized difference vegetation index (NDVI) threshold for the dry months from 2017 to 2019 and the deviation between the NDVI for the driest month and the ten-year average were calculated using collected samples. By combining the results with higher accuracy between these two methods, a global maximum irrigation range dataset (GMIE-100) with a resolution of 100 meters was generated", "ds_ref_instruction": "" } }, "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": [ "GMIE-100型", "灌溉产品", "灌溉性能", "灌溉类型" ], "ds_subject_tags": [ "地理学", "地球科学其他学科" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "田富有", "email": "tianfy@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "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": "田富有", "email": "tianfy@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "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": "田富有", "email": "tianfy@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "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": "水文" }