2026-10-12T23:51:38Z https://www.scidb.cn/oai

10.57760/sciencedb.j00232.0000o 2026-09-09T15:29:32Z

2026-09-09 A Dataset of Sentinel-5P cloud fraction for lakes and adjacent land in China (2019–2023) doi:10.57760/sciencedb.j00232.0000o en This dataset is based on the Sentinel-5P L3 cloud fraction product provided by the GEE platform. On the basis of retaining quality indicators (QA values) greater than 0.5, the cloud cover data of 334 lakes and their surrounding land in China from 2019 to 2023 were extracted in batches using the lake and adjacent land cloud cover matching algorithm. Taking the Taihu Lake Lake as an example, the data processing steps are as follows: first, create a buffer zone with the same area as the lake around the Taihu Lake Lake as the reference area of adjacent land; Secondly, the MODIS land cover classification data (MCD12Q1) was resampled to 1 km × 1 km using bilinear interpolation to match the spatial resolution of the Sentinel-5P cloud cover product; Then, based on MCD12Q1 data, the land use types were screened, and only water pixels were retained within the lake area, while cities and water categories in the land buffer zone were removed; Finally, calculate the average cloud amount (CF_lake) of the Taihu Lake Lake and the average cloud amount of adjacent land (CF_land), and the difference between the two is the difference of lake land cloud amount (∆ CF=CF_lake – CF_land). The dataset is stored in xlsx format, with "Sentinel-5P_Lake_2019_2023. xlsx" and "Sentinel-5P_Unwater-Unurban_Land_2019_2023. xlsx" representing lake cloud cover and adjacent land cloud cover data, respectively The time resolution is daily, and Hylak_i represents the numbering of each lake by HydroLAKES. Missing measurements are represented as NAN.  Sentinel-5P; lake-land cloud fraction difference; lake–land cloud fraction pairing algorithm Wang Bo Wang Wei PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.11834/jrs.20255061 Science Data Bank