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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*

реферат

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.

ключеви́че слова́

China;lakes;water transparency;remote sensing;MODIS

References

  1. 1.
    Bailey M C and Hamilton D P. 1997. Wind induced sediment resuspension: a lake-wide model. Ecological Modelling, 99(2/3): 217-228
  2. 2.
    Bailey S W and Werdell P J. 2006. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102(1/2): 12-23
  3. 3.
    Cai Y, Ke C Q, Li X G, Zhang G Q, Duan Z and Lee H. 2019. Variations of lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS data. Journal of Geophysical Research: Atmospheres, 124(2): 825-843
  4. 4.
    Carlson R E. 1977. A trophic state index for lakes. Limnology and Oceanography, 22(2): 361-369
  5. 5.
    Carper G L and Bachmann R W. 1984. Wind resuspension of sediments in a prairie lake. Canadian Journal of Fisheries and Aquatic Sciences, 41(12): 1763-1767
  6. 6.
    Chang N N, Luo L, Wang X C, Song J, Han J X and Ao D. 2020. A novel index for assessing the water quality of urban landscape lakes based on water transparency. Science of the Total Environment, 735: 139351
  7. 7.
    Chinese Academy of Sciences. 2020. Big earth data in support of the sustainable development goals 中国科学院. 2020. 地球大数据支撑可持续发展目标报告)
  8. 8.
    Duan H T, Ma R H, Zhang Y Z and Zhang B. 2009. Remote-sensing assessment of regional inland lake water clarity in northeast China. Limnology, 10(2): 135-141
  9. 9.
    Feng L, Hou X J and Zheng Y. 2019. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations. Remote Sensing of Environment, 221: 675-686
  10. 10.
    Feng L, Hu C M, Chen X L, Tian L Q and Chen L Q. 2012. Human induced turbidity changes in Poyang Lake between 2000 and 2010: observations from MODIS. Journal of Geophysical Research: Oceans, 117: C07006
  11. 11.
    He X Q, Bai Y, Pan D L, Huang N L, Dong X, Chen J S, Chen C T A and Cui Q F. 2013. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 133: 225-239
  12. 12.
    Kloiber S M, Brezonik P L, Olmanson L G and Bauer M E. 2002. A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sensing of Environment, 82(1): 38-47
  13. 13.
    Lee L H and Lee Y D. 2015. The impact of water quality on the visual and olfactory satisfaction of tourists. Ocean and Coastal Management, 105: 92-99
  14. 14.
    Lee Z, Shang S L, Hu C M, Du K P, Weidemann A, Hou W L, Lin J F and Lin G. 2015. Secchi disk depth: a new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169: 139-149
  15. 15.
    Liu C, Zhu L P, Wang J B, Qiao B J, Ju J T and Huang L. 2017. Remote sensing-based estimation of lake water clarity on the Tibetan Plateau. Progress in Geography, 36(5): 597-609
  16. 16.
    Liu D, Duan H T, Loiselle S, Hu C M, Zhang G Q, Li J L, Yang H, Thompson J R, Cao Z G, Shen M, Ma R H, Zhang M and Han W X. 2020. Observations of water transparency in China’s lakes from space. International Journal of Applied Earth Observation and Geoinformation, 92: 102187
  17. 17.
    Liu D, Yu S J, Cao Z G, Qi T C and Duan H T. 2021. Process-oriented estimation of column-integrated algal biomass in eutrophic lakes by MODIS/Aqua. International Journal of Applied Earth Observation and Geoinformation, 99: 102321
  18. 18.
    Ma R H, Duan H T, Hu C M, Feng X Z, Li A N, Ju W M, Jiang J H and Yang G S. 2010. A half-century of changes in China’s lakes: global warming or human influence?. Geophysical Research Letters, 37(24): L24106
  19. 19.
    Ma R H, Yang G S, Duan H T, Jiang J H, Wang S M, Feng X Z, Li A N, Kong F X, Xue B, Wu J L and Li S J. 2011. China’s lakes at present: number, area and spatial distribution. Science China Earth Sciences, 54(2): 283-289
  20. 20.
    McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432
  21. 21.
    Olmanson L G, Bauer M E and Brezonik P L. 2008. A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sensing of Environment, 112(11): 4086-4097
  22. 22.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  23. 23.
    Prasad K S, Bernstein R L, Kahru M and Mitchell B G. 1998. Ocean color algorithms for estimating water clarity (Secchi Depth) from SeaWiFS. Journal of Advanced Marine Science and Technology Society, 4(2): 301-306
  24. 24.
    Rhee G Y and Gotham I J. 1981. The effect of environmental factors on phytoplankton growth: temperature and the interactions of temperature with nutrient limitation. limnology and Oceanography, 26(4): 635-648
  25. 25.
    Shi K, Zhang Y L, Zhu G W, Liu X H, Zhou Y Q, Xu H, Qin B Q, Liu G and Li Y M. 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250m MODIS-Aqua data. Remote Sensing of Environment, 164: 43-56
  26. 26.
    Song K S, Liu G, Wang Q, Wen Z D, Lyu L, Du Y X, Sha L W and Fang C. 2020. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243: 111800
  27. 27.
    Vermote E F, Roger J C and Ray J P. 2015. MODIS surface reflectance user’s guide (Version 1.4)
  28. 28.
    Wang S L, Li J S, Zhang B, Lee Z, Spyrakos E, Feng L, Liu C, Zhao H L, Wu Y H, Zhu L P, Jia L M, Wan W, Zhang F F, Shen Q, Tyler A N and Zhang X F. 2020. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS. Remote Sensing of Environment, 247: 111949
  29. 29.
    Williamson A N and Grabau W E. 1973. Sediment concentration mapping in tidal estuaries//Third Earth Resources Technology Satellite-1 Symposium. Washington, DC: [s.n.]: 1347-1386

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