Soil moisture content is an important index of drought monitoring, and temperature vegetation drought index can monitor drought by retrieving surface soil moisture. This data set is based on MODIS vegetation index and surface temperature products, combined with SRTM DEM data, using ndvi-lst feature space characteristics, extracting the dry and wet edge of the feature space, and obtaining the monthly temperature vegetation drought index data set of "China Pakistan Economic Corridor" in 2000-2017. The spatial range of this data set is 41 ° 25'24.49 "～ 23 ° 45'24.49" north latitude, 60 ° 53'57.97 "～ 79 ° 52'27.97" east longitude. The data format is GeoTIFF and the spatial resolution is 1km. The data set is composed of 216 data files. The data file is the monthly temperature vegetation drought index data. The file name is tvdi.ayyyddd.1 ﹐ km ﹐ month.tif. This data set can provide basic data and scientific and technological support for drought disaster monitoring of "China Pakistan Economic Corridor".
|collect time||2000/01/01 - 2017/12/31|
|data size||583.8 MiB|
The data used in this data set are MODIS vegetation index product mod13a3 and surface temperature product mod11a2, SRTM DEM products, as well as observation precipitation data and soil moisture data of meteorological stations, among which MODIS data is from NASA LP DAAC data center (https://lpdaac.usgs.gov); digital elevation model (DEM) data is from geospatial data cloud (www.csdata. ORG) SRTM data set provided; precipitation and soil moisture data are from the national meteorological data sharing service platform (https://data.cma.cn).
The MRT batch processing tool is used for data splicing, projection conversion, band extraction and resampling to obtain NDVI, surface temperature and relevant quality control data. Using ArcGIS tools, DEM data are spliced, resampled and cropped. In the production process of this data set, the spatial reconstruction method is used.
Compared with the observed precipitation and soil moisture data of meteorological station, the product has a negative correlation with SPI and soil moisture, and the correlation is good.
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