سابِق|تال

The proposal， practice and preliminary application of land-based （ground-based， shore-based） remote sensing of water environment

  • role： First author第一作者
  • Affiliation：

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

  • Email：ylzhang@niglas.ac.cn
  • Introduction：张运林，1976年生，男，研究员，研究方向为物理湖泊学、水色遥感以及有色可溶性有机物循环。E-mail： ylzhang@niglas.ac.cn
ZHANG Yunlin1,  
  • Affiliation：

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

SHI Kun1,  
  • Affiliation：

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

ZHANG Yibo1,  
  • Affiliation：

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

SUN Xiao1,  
  • Affiliation：

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

LI Na1,  
  • Affiliation：

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

HUANG Xin1,  
  • Affiliation：

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

WANG Weijia1,  
  • Affiliation：

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

ZHOU Yongqiang1,  
  • Affiliation：

    Hangzhou Hikvision Digital Technology Co., Ltd, Hangzhou 310051, China

GAO Yanghui2,  
  • Affiliation：

    Hangzhou Hikvision Digital Technology Co., Ltd, Hangzhou 310051, China

CAI Hong2,  
  • Affiliation：

    Hangzhou Hikvision Digital Technology Co., Ltd, Hangzhou 310051, China

GAO Jing2

ملخص

Cross-section monitoring is an important cornerstone to accurately grasp the surface water quality and water environment change characteristics, to explore water environment formation mechanism, to evaluate and assess water environment trend, to treat and repair water pollution, and to manage water quality. Improving the level of three-dimensional, automatic and intelligent monitoring is an important direction of future ecological environment monitoring, which can effectively improve the level of modern ecological environment monitoring ability.The traditional cross-section manual sampling monitoring is time-consuming and laborious, and the time and space frequency are very low resulting in the discrete data and the poor timeliness. High frequency on-line monitoring of underwater sensors can solve the continuous observation in time, but the sensor is easy to wear and tear and disturbed by water environment, resulting in unstable monitoring accuracy, limited monitoring indexes, large errors between automatic monitoring results of key parameters such as total nitrogen and total phosphorus and conventional laboratory monitoring analysis data. In addition, high management and maintenance costs are needed for the on-line monitoring of underwater sensors. Satellite remote sensing can realize the inversion of key water quality parameters in different spatial scales, but it is difficult to solve the high temporal resolution continuous observation. Meanwhile, it is difficult to ensure high-precision monitoring due to the effects of cloud and rain conditions and atmospheric correction.In this study, we firstly propose the concept of land-based (ground-based, shore-based) water environment remote sensing to effectively supplement and improve the existing satellite remote sensing and national surface water environment cross-section monitoring technology and methods. Using domestic hyperspectral imager developed by the Hangzhou Hikvision Digital Technology Co., Ltd. and Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, we carry out the application practice of land-based (ground-based, shore-based) water environment remote sensing under complex weather and water conditions in Lake Taihu. Based on the in situ measurement of remote sensing reflectance and key water quality parameters, we calibrate and validate the remote sensing estimation models of Secchi disc depth, total suspended matter, total nitrogen, and total phosphorus with high precision. Overall, the estimation precisions of four key water quality parameters are close or higher than 80% indicating the land-based (ground-based, shore-based) remote sensing can be used for the accurate monitoring of water quality under complex weather and water conditions. High frequency dynamic processes of key water quality parameters are observed by implanting remote sensing estimation models into the hyperspectral imager, which can be used to finely characterize time evolution process of water environment. The practice of remote sensing of water environment in Lake Taihu shows that it has a wide application prospect and market value to install domestic hyperspectral imager to carry out high frequency accurate observation of key water quality parameters in the national surface water monitoring section.Of course, more in situ observation and measurement for different waters are needed to improve or develop universal models for more parameters monitoring. Coupled with satellite, unmanned aerial vehicle and land-based (ground-based, shore based) remote sensing, the sky-air-ground integrated remote sensing monitoring system can be established. Combined with cross-section manual monitoring and automatic observation, the collaborative monitoring can be carried out to give full play to their respective advantages, which truly realize the long-term reconstruction and real-time high-frequency dynamic monitoring of key water quality parameters serving water environment management.

مفهوم

land-based (ground-based, shore-based) remote sensing;water quality parameters;total suspended matter;nutrients;total phosphorus;secchi disc depth

References

  1. 1.
    Chen J and Quan W T. 2012. Using Landsat/TM imagery to estimate nitrogen and phosphorus concentration in Taihu Lake, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1): 273-280
  2. 2.
    Duan H T, Wan N S, Qiu Y G, Liu G, Chen Q, Luo J H, Chen Y and Qi T C. 2020. Discussions and practices on the framework of monitoring system in eutrophic lakes and reservoirs. Journal of Lake Sciences, 32(5): 1396-1405
  3. 3.
    Hogg D C. 1980. Ground-based remote sensing and profiling of the lower atmosphere using radio wavelengths. IEEE Transactions on Antennas and Propagation, 28(2): 281-283
  4. 4.
    Huang J C, Zhang Y J, Arhonditsis G B, Gao J F, Chen Q W, Wu N C, Dong F F and Shi W Q. 2019. How successful are the restoration efforts of China's lakes and reservoirs? Environment International, 123: 96-103
  5. 5.
    Kaufman Y J, Tanré D, Dubovik O, Karnieli A and Remer L A. 2001. Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing. Geophysical Research Letters, 28(8): 1479-1482
  6. 6.
    Li S J, Wu Q, Wang X J, Piao X Y and Dai Y N. 2002. Correlations between reflectance spectra and contents of chlorophyll-a in Chaohu Lake. Journal of Lake Sciences, 14(3): 228-234
  7. 7.
    Li Z, Gu X, Wang L, Li D, Xie Y, Li K, Dubovik O, Schuster G, Goloub P, Zhang Y, Li L, Ma Y and Xu H. 2013. Aerosol physical and chemical properties retrieved from ground-based remote sensing measurements during heavy haze days in Beijing winter. Atmospheric Chemistry and Physics, 13(20): 10171-10183
  8. 8.
    Liu J, Zhou M, Chen X and Liu T L. 2014. Study on the construction and management of the quality automatic monitoring network of state surface water. Environmental Monitoring and Forewarning, 6(1): 10-13
  9. 9.
    Liu T Y, Kuang D B and Yin Q. 2004. Study on hyperspectral quantitative model of concentrations for chlorophyll-a of alga and suspended particles in Tailake. Journal of Infrared and Millimeter Waves, 23(1): 11-15
  10. 10.
    Ma R H and Dai J F. 2005. Chlorophyll-a concentration estimation with field spectra of water-body near Meiliang Bayou in Taihu Lake. Journal of Remote Sensing, 9(1): 78-86
  11. 11.
    Ma T, Zhao N, Ni Y, Yi J W, Wilson J P, He L H, Du Y Y, Pei T, Zhou C H, Song C and Cheng W M. 2020. China’s improving inland surface water quality since 2003. Science Advances, 6(1):
  12. 12.
    Mishra D R, Kumar A, Ramaswamy L, Boddula V K, Das M C, Page B P and Weber S J. 2020. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. Harmful Algae, 96: 101828
  13. 13.
    Qin B Q. 2020. Shallow lake limnology and control of eutrophication in Lake Taihu. Journal of Lake Sciences, 32(5): 1229-1243
  14. 14.
    Qin B Q, Li W, Zhu G W, Zhang Y L, Wu T F and Gao G. 2015. Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China). Journal of Hazardous Materials, 287: 356-363
  15. 15.
    Qin B Q, Paerl H W, Brookes J D, Liu J G, Jeppesen E, Zhu G W, Zhang Y L, Xu H, Shi K and Deng J M. 2019. Why Lake Taihu continues to be plagued with cyanobacterial blooms through 10 years (2007-2017) efforts. Science Bulletin, 64(6): 354-356
  16. 16.
    Qin B Q, Xu P Z, Wu Q L, Luo L C and Zhang Y L. 2007. Environmental issues of Lake Taihu, China. Hydrobiologia, 581(1): 3-14
  17. 17.
    Qin B Q, Zhang Y L, Zhu G W, Gong Z J, Deng J M, Hamilton D P, Gao G, Shi K, Zhou J, Shao K Q, Zhu M Y, Zhou Y Q, Tang X M and Li L. 2020. Are nitrogen-to-phosphorus ratios of Chinese lakes actually increasing? Proceedings of the National Academy of Sciences of the United States of America, 117(35): 21000-21002
  18. 18.
    Qin B Q, Zhu G W, Gao G, Zhang Y L, Li W, Paerl H W and Carmichael W W. 2010. A drinking water crisis in Lake Taihu, China: Linkage to climatic variability and lake management. Environmental Management, 45(1): 105-112
  19. 19.
    Shi K, Zhang Y L, Zhang Y B, Li N, Qin B Q, Zhu G W and Zhou Y Q. 2019. Phenology of phytoplankton blooms in a trophic lake observed from long-term MODIS data. Environmental Science and Technology, 53(5): 2324-2331
  20. 20.
    Shi K, Zhang Y L, Zhang Y B, Qin B Q and Zhu G W. 2020. Understanding the long-term trend of particulate phosphorus in a cyanobacteria-dominated lake using MODIS-Aqua observations. Science of the Total Environment, 737: 13976
  21. 21.
    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 250 m MODIS-Aqua data. Remote Sensing of Environment, 164: 43-56
  22. 22.
    Shi K, Zhang Y L, Zhu G W, Qin B Q and Pan D L. 2018. Deteriorating water clarity in shallow waters: Evidence from long term MODIS and in-situ observations. International Journal of Applied Earth Observation and Geoinformation, 68: 287-297
  23. 23.
    Wu J Y, Chen C Z, Zhao C, Sun Y and Hu T Y. 2020. Prospects for national eco-environmental monitoring during the 14th Five-Year Plan period. Environmental Conformity Assessment, 12(4): 62-67
  24. 24.
    Wu S Z, Xia Q and Liu H L. 2000. Analysis of river basin water pollution in China. Environmental Science and Technology, (2): 1-6
  25. 25.
    Yang G S, Ma R H, Zhang L, Jiang J H, Yao S C, Zhang M and Zeng H A. 2010. Lake status, major problems and protection strategy in China. Journal of Lake Sciences, 22(6): 799-810
  26. 26.
    Zhang L P, Xia J and Hu Z F. 2009. Situation and problem analysis of water resource security in China. Resources and Environment in the Yangtze Basin, 18(2): 116-120
  27. 27.
    Zhang Y L, Qin B Q, Zhu G W, Shi K and Zhou Y Q. 2018. Profound changes in the physical environment of Lake Taihu from 25 years of long‐term observations: implications for algal bloom outbreaks and aquatic macrophyte loss. Water Resources Research, 54(7): 4319-4331
  28. 28.
    Zhou Y Q, Ma J R, Zhang Y L, Qin B Q, Jeppesen E, Shi K, Brookes J D, Spencer R G M, Zhu G W and Gao G. 2017. Improving water quality in China: Environmental investment pays dividends. Water Research, 118: 152-159

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website