The data set contains the variations of water level, area, and volume for ten lakes in Jiangsu Province (Taihu Lake, Hongze Lake, Gaoyou Lake, Luoma Lake, Shijiu Lake, Gehu Lake, Yangcheng Lake, Baima Lake, Shaobo Lake and Dianshan Lake) from 2003 to 2019, which provides important parameters for the study of lake hydrological ecosystem balance in Jiangsu Province in recent years. The water level data of the ten lakes were obtained from altimetry satellites Envisat/RA-2, Cryosat-2, ICESat, and ICESat-2. The water area data were obtained from Landsat TM/OLI images bsed on Modified Normalized Difference Water Index. For the four lakes with complete water level data (Hongze Lake, Gaoyou Lake, Gehu Lake and Taihu Lake), the water volume changes from 2003 to 2019 were estimated according to the water level and area results. Compared with the in-situ water level data, the water level extracted from altimetry data showed significantly consistent (α = 0.05) for all the ten lakes, with the average absolute error of 0.168 m. The data set provides the variations of water level, area, and volume for the ten lakes in Jiangsu Province from 2003 to 2019, which can provide data support for the management and dispatching of water resources in Jiangsu Province.
KE Changqing, CHANG Xiangyu, CAI Yu, XIA Wentao
This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2020. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2020 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2020, and can be used for quantitative estimation of water resource.
LI Jia, LI Jianjiang, LI Xin, LIU Shaomin
