The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
WANG Xinchi
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).
FENG Min, CHE Xianghong
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