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.
| collect time | 2017/01/01 - 2019/12/31 |
|---|---|
| collect place | Global |
| data size | 6.3 GiB |
| data format | TIFF |
| Coordinate system | WGS84 |
| Projection |
Data sourced from HARVARD DAtaverse( https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HKBAQQ )。
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.
The data quality is good.
| # | number | name | type |
| 1 | 2016YFA0600300 | National key R & D plan |
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
| # | title | file size |
|---|---|---|
| 1 | _ncdc_meta_.json | 4.8 KiB |
| 2 | 基于干旱胁迫下的灌溉表现和机器学习方法的全球最大灌溉范围和中央支点灌溉系统数据集 |
Irrigation products irrigation performance central pivot irrigation system GMIE-100
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