The Yellow River Basin is mostly characterized by arid and semi-arid climates, with inherently inadequate water resources. It is the most severely affected basin by drought among the major river basins in China. With global environmental and climatic changes, droughts in the Yellow River Basin have become more frequent, and research on drought monitoring in this basin has become a hot topic of current interest. This dataset is based on MODIS vegetation and land surface temperature products. Through quality control processes such as cloud removal and reconstruction for annual data, annual datasets of Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature Vegetation Dryness Index (TVDI) for the Yellow River Basin from 2003 to 2022 have been produced. The spatial scope of this dataset is 32°10′N–41°50′N, 95°53′E–119°05′E, with a data format of GeoTiff and a spatial resolution of 1 km. Compared to other drought index datasets.
This dataset can represent the drought patterns in the Yellow River Basin on an annual timescale and reflect the trends of drought changes in the basin over a time series, providing basic data support for drought disaster monitoring in the Yellow River Basin.
This drought index dataset consists of four compressed files, namely TCI_YellowRiverBasin.zip, VCI_YellowRiverBasin.zip, VHI_YellowRiverBasin.zip, and TVDI_YellowRiverBasin.zip. The compressed files contain annual data for the Yellow River Basin from 2002 to 2023 for the four drought indices, in the form of .tif image files. The naming format for the tif files is: Drought_Index.YYYY.1_km_year.tif.
Using Google Earth Engine (GEE), NVDI and LST data from MODIS products were obtained, and then filtered and clipped according to the time span and regional scope to acquire data for the Yellow River Basin from 2003 to 2022. Quality control was performed on the data using the quality control bands (QC bands) of the MODIS products, such as cloud removal, to obtain preliminary data. Additionally, the maximum value composite method was adopted to synthesize the maximum NDVI data within the year.
The quality of the data product is related to the quality of the NDVI and LST data. In this study, the quality of the produced data product is mainly ensured by controlling the quality of the NVDI and LST data used.
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https://cstr.cn/31253.11.sciencedb.09116
10.57760/sciencedb.09116