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Monthly mean remote sensing water transparency dataset of large lakes in China during 2000—2020

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

  • Email：dliu@niglas.ac.cn
  • Introduction：刘东，1987年生，男，副研究员，研究方向为湖泊水环境遥感。E-mail： dliu@niglas.ac.cn
LIU Dong1,  
  • Affiliation：

    State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

ZHANG Min2,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

CAO Zhigang1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

SHEN Ming1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

QI Tianxi1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

MA Jinge1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

    College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China

  • Email：htduan@niglas.ac.cn
  • Introduction：段洪涛，1979年生，男，研究员，研究方向为湖泊遥感与智慧流域。E-mail： htduan@niglas.ac.cn
DUAN Hongtao13*

resumen

Lake water transparency can comprehensively reflect the lake water environment, has significant relationships to some water quality parameters, and greatly important for water environment monitoring. This study aims to introduce the generation processes, characteristics, and application values of a new monthly mean water transparency dataset for large lakes in China with a water area of >20 km2. The remote sensing algorithm for retrieving water transparency proposed by Liu et al. (2020) was applied to MODIS surface reflectance data and stored on the Google Earth Engine cloud platform to realize rapid calculation and mapping of monthly mean water transparency in different lakes in China from 2000 to 2020. The MODIS surface reflectance data contain one state band, which was used to remove nonwater pixels such as cloud, cloud shadow, and land. The output data were stored in GeoTIFF grid format, which saved the pixel-based water transparency value and the geographic coordinate information. The GeoTIFF format file was also convenient for different software platforms. The dataset covers 412 large lakes in different lake zones of China. Specifically, Inner Mongolia-Xinjiang Lake Zone (IMXL), the Tibetan Plateau Lake Zone (TPL), the Yunnan-Guizhou Plateau Lake Zone (YGPL), the Northeast Plain and Mountain Lake Zone (NPML), and the Eastern Plain Lake Zone (EPL) have 40, 262, 11, 20, and 79 lakes, respectively.This study also provided some application examples of the dataset. First, the dataset indicates that the lakes in China had high water transparency values in the west but low values in the east. In 2019, the area-weighted water transparency values in the IMXL, TPL, YGPL, NPML, and EPL zones were 174.54 cm, 276.67 cm, 254.93 cm, 43.41 cm, and 53.93 cm, respectively. Second, the comparison results of lakes Fuxian and Poyang show the two typical types of seasonal variations in water transparency. For the deep Lake Fuxian, water transparency was determined by phytoplankton content; it had low values in summer. On the contrary, for the shallow Lake Poyang, water transparency was controlled by sediment resuspension; it had low values in winter with strong wind. Third, according to water transparency, we divided the Chinese lakes into four types. Lakes in Type I with high water clarity were majorly located in the west. Lakes in Type IV with low water clarity were mainly distributed in the east. Fourth, water transparency was applied to assess the lake water environment under the sustainable development goals. In the previous two decades, water transparency values in the IMXL, TPL, and NPML zones showed a significantly increasing trend, but water transparency values in the EPL and YGPL zones showed a decreasing trend.To our knowledge, this dataset is the first monthly mean water transparency dataset, which covers nearly all large lakes in China. The monthly scale time resolution allows the dataset to obtain outstanding advantages for dynamically monitoring the lake water environment in China. In the future, the open sharing dataset is greatly important to promote the development of lake water environment research in China.

palabra clave

China;lakes;water transparency;remote sensing;MODIS

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