Applicability evaluation and method selection in detecting cyanobacterial bloom using HY-1C/D CZI data for inland lakes

  • 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:kxue@niglas.ac.cn
  • Introduction:E-mail kxue@niglas.ac.cn
XUE Kun1,  
  • role: Corresponding author通信作者
  • Affiliation:

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

  • Email:rhma@niglas.ac.cn
  • Introduction:E-mail rhma@niglas.ac.cn
MA Ronghua1*,  
  • Affiliation:

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

CAO Zhigang1,  
  • Affiliation:

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

HU Minqi1,  
  • Affiliation:

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

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Jiaxin12

ملخص

Intense cyanobacterial blooms often occur in eutrophic lakes, satellite data with high spatial resolution often has low temporal frequency, and nearly daily revisiting satellite data has coarse spatial resolution, limiting the monitoring of floating blooms in small lakes. The Coastal Zone Imager (CZI) onboard HY-1C/1D satellite provides a new data source to monitor the cyanobacterial bloom of inland lakes with 50 m spatial resolution, revisit time of 3 d. An index, namely the Adjusted Floating Algae Height (AFAH), is developed based on the difference between red band (Rrc(650)), and a baseline between green band (560 nm) and NIR band (near infrared, 825 nm). AFAH was then applied in four eutrophic lakes, for instance, Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun, and its advantages and uncertainties in monitoring cyanobacterial blooms were evaluated. The results showed that: (1) In cloudless conditions, AFAH, NDVI (Normalized Difference Vegetation Index), DVI (Difference Vegetation Index), and VB-FAH (Virtual baseline floating macroAlgae Height) have high accuracy larger than 0.93. AFAH has advantages over NDVI, EVI, and VA-FAH, as it is less sensitivity to the solar/viewing geometry, aerosol type and thickness, thin cloud, and sun glint. (2) The maximum gradient method is used to derive the AFAH threshold to extract bloom pixels, total of 180 images with cyanobacterial blooms are used to calculate the mean (0.041) and standard deviation (0.013) of AFAH threshold values. (3) The spatial distribution of cyanobacterial blooms from July, 2019 to July, 2021 in Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun is accordance with the previous studies. The areas with bloom frequency larger than 5% are 609.05 km2, 134.43 km2, 20.91 km2, and 14.50 km2 for Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun. The Zhushan Bay, Meiliang Bay, and western part of Lake Taihu, the western part of Lake Chaohu, and most part of Lake Dianchi and Lake Xingyun have high frequency of floating blooms. This study indicated that AFAH has good performance in detecting floating blooms using satellite data with only one NIR band, and multi-source satellite data should be combined in the following study in order to improve the temporal frequency of bloom monitoring.

مفهوم

water color remote sensing;eutrophic lakes;cyanobacterial bloom;HY-1C/D satellite;Coastal Zone Imager (CZI);baseline subtraction method

References

  1. 1.
    Cao M M. 2021. Remote Sensing Monitoring of Algal Blooms in Hulun Lake based on a Novel Spectral Index ABDI. Huhhot: Inner Mongolia Normal University
  2. 2.
    Cao Z G, Ma R H, Liu J Q and Ding J. 2021. Improved radiometric and spatial capabilities of the coastal zone imager onboard Chinese HY-1C satellite for inland lakes. IEEE Geoscience and Remote Sensing Letters, 18(2):193-197
  3. 3.
    de Lucia Lobo F, Nagel G W, Maciel D A, de Carvalho L A S, Martins V S, Barbosa C C F and de Moraes Novo E M L. 2021. AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sensing, 13(15): 2874
  4. 4.
    Duan H T, Ma R H, Xu X F, Kong F X, Zhang S X, Kong W J, Hao J Y and Shang L L. 2009. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environmental Science and Technology, 43(10): 3522-3528
  5. 5.
    Duan H T, Cao Z G, Shen M, Ma J G and Qi T C. 2022. Review of lake remote sensing research. National Remote Sensing Bulletin, 26(1): 3-18
  6. 6.
    Fang X, Duan H T, Cao Z G, Shen M and Ge X S. 2018. Remote monitoring of cyanobacterial blooms using multi-source satellite data: a case of Yuqiao Reservoir, Tianjin. Journal of Lake Sciences, 30(4): 967-978
  7. 7.
    Feng L. 2021. Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing. Journal of Lake Sciences, 33(3): 647-652
  8. 8.
    GDAL/OGR Contributors. 2022. GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation. URL https://github.com/OSGeo/gdal/blob/master/CITATION
  9. 9.
    Gower J, King S, Borstad G and Brown L. 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26(9): 2005-2012
  10. 10.
    Ho J C, Michalak A M and Pahlevan N. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574(7780): 667-670
  11. 11.
    Ho J C, Stumpf R P, Bridgeman T B and Michalak A M. 2017. Using Landsat to extend the historical record of lacustrine phytoplankton blooms: a Lake Erie case study. Remote Sensing of Environment, 191: 273-285
  12. 12.
    Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113(10): 2118-2129
  13. 13.
    Hu C M, Lee Z P, Ma R H, Yu K, Li D Q and Shang S L. 2010. Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research: Oceans, 115(C4): C04002
  14. 14.
    Hu L, Gan S, Yuan X P, Li R B and Bi R. 2021. Study on the spatial distribution characteristics of cyanobacteria bloom in Dianchi Lake based on GF-5. Laser and Infrared, 51(2): 237-243
  15. 15.
    Hu M Q, Zhang Y C, Ma R H and Zhang Y X. 2018. Spatial and temporal dynamics of floating algal blooms in Lake Chaohu in 2016 and their environmental drivers. Environmental Science, 39(11): 4925-4937
  16. 16.
    Li Y M, Zhao H, Bi S and Lyu H. 2022. Research progress of remote sensing monitoring of case II water environmental parameters based on water optical classification. National Remote Sensing Bulletin, 26(1): 19-31
  17. 17.
    Liu H Q, Ren H K, Niu X X and Xia P. 2021. Extraction of cyanobacteria bloom in Chaohu Lake based on Sentinel-2 remote sensing images. Ecology and Environmental Sciences, 30(1): 146-155
  18. 18.
    Liu S J, Shi Y F, Zhai J Y, Zhou S X and Ji Z Y. 2021. Annual variations of Microcystis density and their relationships with water quality indices in Xingyun Lake. Environmental Chemistry, 40(7): 2064-2072
  19. 19.
    Luo X C, Hang X, Cao Y, Hang R R and Li Y C. 2019. Dominant meteorological factors affecting cyanobacterial blooms under eutrophication in Lake Taihu. Journal of Lake Sciences, 31(5): 1248-1258
  20. 20.
    Matthews M W, Bernard S and Robertson L. 2012. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 124: 637-652
  21. 21.
    Matthews M W and Odermatt D. 2015. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156: 374-382
  22. 22.
    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
  23. 23.
    Oyama Y, Matsushita B and Fukushima T. 2015. Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM+ shortwave infrared bands. Remote Sensing of Environment, 157: 35-47
  24. 24.
    Qi G H, Ma X S, He S Y and Wu P H. 2021. Long-term spatiotemporal variation analysis and probability prediction of algal blooms in Lake Chaohu (2009—2018) based on multi-source remote sensing data. Journal of Lake Sciences, 33(2): 414-427
  25. 25.
    Qi L, Hu C M, Duan H T, Cannizzaro J and Ma R H. 2014. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: theoretical basis and practical considerations. Remote Sensing of Environment, 154: 298-317
  26. 26.
    Qi L, Hu C M, Visser P M and Ma R H. 2018. Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnology and Oceanography, 63(4): 1711-1726
  27. 27.
    Qin B Q. 2020. Shallow lake limnology and control of eutrophication in Lake Taihu. Journal of Lake Sciences, 32(5): 1229-1243
  28. 28.
    Qiu Z F, Li Z X, Bilal M, Wang S Q, Sun D Y and Chen Y L. 2018. Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images. Optics Express, 26(21): 26810-26829
  29. 29.
    Rouse J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS//3rd Earth Resources Technology Satellite-1 Symposium. Washington, D.C.: NASA: 309-317
  30. 30.
    Shen F, Tang R G, Sun X R and Liu D Y. 2019. Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea. Remote Sensing of Environment, 235: 111484
  31. 31.
    Son Y B, Min J E and Ryu J H. 2012. Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data. Ocean Science Journal, 47(3): 359-375
  32. 32.
    Song K S, Fang C, Jacinthe P A, Wen Z D, Liu G, Xu X F, Shang Y X and Lyu L. 2021. Climatic versus Anthropogenic Controls of Decadal Trends (1983—2017) in Algal Blooms in Lakes and Reservoirs across China. Environmental Science and Technology, 55(5): 2929-2938
  33. 33.
    Vermote E F, Tanré D, Deuze J L, Herman M and Morcette J J. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686
  34. 34.
    Wang J H, He L Q S, Yang C, Dao G H, Du J S, Han Y P, Wu G X, Wu Q Y and Hu H Y. 2018. Comparison of algal bloom related meteorological and water quality factors and algal bloom conditions among lakes Taihu, Chaohu, and Dianchi (1981—2015). Journal of Lake Sciences, 30(4): 897-906
  35. 35.
    Wilson R T. 2013. Py6S: a Python interface to the 6S radiative transfer model. Computers and Geosciences, 51: 166-171
  36. 36.
    Wynne T T, Stumpf R P, Tomlinson M C and Dyble J. 2010. Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data. Limnology and Oceanography, 55(5): 2025-2036
  37. 37.
    Xing Q G and Hu C M. 2016. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113-126
  38. 38.
    Xue K, Ma R H, Duan H T, Shen M, Boss E and Cao Z G. 2019. Inversion of inherent optical properties in optically complex waters using Sentinel-3A/OLCI images: a case study using China’s three largest freshwater lakes. Remote Sensing of Environment, 225: 328-346
  39. 39.
    Xue K, Zhang Y C, Duan H T, Ma R H, Loiselle S and Zhang M W. 2015. A remote sensing approach to estimate vertical profile classes of phytoplankton in a eutrophic lake. Remote Sensing, 7(11): 14403-14427
  40. 40.
    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
  41. 41.
    Yue A, Zeng Q W and Wang H J. 2020. Remote sensing long-term monitoring of cyanobacterial blooms in yuqiao reservoir. Remote Sensing Technology and Application, 35(3): 694-701
  42. 42.
    Zhang M, Yang Z and Shi X L. 2019. Expansion and drivers of cyanobacterial blooms in Lake Taihu. Journal of Lake Sciences, 31(2): 336-344
  43. 43.
    Zhu G W, Qin B Q, Zhang Y L, Xu H, Zhu M Y, Yang H W, Li K Y, Min S, Shen R J and Zhong C N. 2018. Variation and driving factors of nutrients and chlorophyll-a concentrations in northern region of Lake Taihu, China, 2005—2017. Journal of Lake Sciences, 30(2): 279-295
  44. 44.
    Zong J M, Wang X X, Zhong Q Y, Xiao X M, Ma J and Zhao B. 2019. Increasing outbreak of cyanobacterial blooms in large lakes and reservoirs under pressures from climate change and anthropogenic interferences in the middle-lower Yangtze River Basin. Remote Sensing, 11(15): 1754

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