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Concerns about phytoplankton bloom trends in global lakes

Matters Arising to this article was published on 17 February 2021

The Original Article was published on 14 October 2019

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Fig. 1: Examples showing the problems associated with L5TM-based bloom intensity (BNIR) estimated with equation 2 in Ho et al.1.

Data availability

The Landsat data can be obtained from the US Geological Survey at https://glovis.usg.gov. The in situ spectral and Chlα data used in this paper are available in the Supplementary Information.

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Acknowledgements

This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060402), the National Natural Science Foundation of China (numbers 41971304, 41890852 and 41890851) and by High-level Special Funding of the Southern University of Science and Technology (numbers G02296302, G02296402). We thank Guangzhou Water Color Ocean Technology Co., Ltd and Easy Ocean Technology Ltd for their help in collecting in situ data.

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Authors

Contributions

L.F. initiated the project and wrote an initial draft of the manuscript, and Y.D., X.H. and Y.X. performed the data processing and analysis. All authors participated in interpreting the results and revising the manuscript.

Corresponding author

Correspondence to Lian Feng.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Relationship between the surface reflectance in the NIR band (ρNIR) and Chla.

The correlations for different Chla ranges (colour-coded) and individual lakes are non-significant (P > 0.05). The data are from in situ measurements collected from 15 lakes in China across waters with varying eutrophic status. ρNIR values are the equivalent L5TM NIR reflectances aggregated using in situ hyperspectral measurements and the L5TM spectral response function (see the aggregation method in Kalmen et al.96).

Extended Data Fig. 2 Examples showing the effects of high sediment loads on the bloom intensity (BNIR) calculations in eight of the lakes studied in Ho et al.1.

The left panels of the paired images show the true-colour composites for L5TM images, and the right panels show the corresponding BNIR maps after applying the hue and Fmask masks. The sediment plumes (indicated by red arrows) with high BNIR values (~0.1) could still be classified as intense blooms with the hue mask defined in Ho et al.1. The examination of historical L5TM images show that sediment plumes could occur in at least 58 (82%) of the 71 lakes studied in Ho et al.1.

Extended Data Fig. 3 Reflectance spectra of submerged vegetation.

a, The spectral reflectances of two types (Ceratophyllum demersum and Myriophyllum verticillatum) of submerged vegetation collected from Taihu Lake in China (a shallow lake ~200 km away from Hongze Lake) on 24 October 2019, using the PSR+3500 field-portable spectrometer manufactured by Spectral Evolution. Also plotted are the spectral reflectances of different blooms and the normalized spectral responses in the L5TM NIR band, which were obtained from extended data figure 7 in Ho et al.1. The spectral features of submerged vegetation, particularly the reflectance in the NIR band, are very similar to those of intense phytoplankton blooms. b, c, Photographs taken while conducting the in situ measurements.

Extended Data Fig. 4 Spectral features of different types of waters in L5TM images.

a, b, The spectral data were obtained from the arrow-indicated pixels in Fig. 1 (a from Songkhla Lake and b from Hongze Lake). ρTOA is the top-of-atmosphere reflectance, ρr is the reflectance from molecular scattering (or Rayleigh scattering, estimated using the method in Gordon10) and ρrc is the difference between ρTOA and ρr.

Extended Data Fig. 5 Examples of pixels with intense blooms erroneously masked by Fmask in eight of the lakes studied in Ho et al.1.

The left panels of the paired images show the true-colour composites for the L5TM images, and the right panels show the resultant separation of pixels determined using Fmask. Clearly, intense blooms (greenish in the red squares) have been classified as other classes instead of as water. The examination of the lakes studied in ref. 1 showed that most of the severe blooms with surface scum were missed owing to the improper use of Fmask.

Extended Data Fig. 6 Daily areas of algal bloom in Taihu Lake between 2000 and 2008, determined using MODIS observations by Hu et al.12.

Red points represent MODIS observations with the same overpassing dates as L5TM (that is, daily MODIS observations have concurrent L5TM images) and indicate the difficulty in characterizing long-term bloom dynamics. For example, whereas black dots show a clear increase in bloom area after 2005, such a trend is difficult to capture with the red dots.

Extended Data Table 1 Previous studies with in situ datasets that showed substantial effects of water turbidity (or total suspended sediments, TSS) on the reflectance of the water column in the NIR band
Extended Data Table 2 Previous studies with in situ datasets that showed that NIR reflectance could be substantially enhanced owing to the presence of submerged vegetation
Extended Data Table 3 List of lakes studied in Ho et al.1 with abundant submerged vegetation identified

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Supplementary Data

This file contains the in situ NIR refletance and Chla (in mg/m3) measurements collected in 15 lakes in China, and the sample dates and locations.

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Feng, L., Dai, Y., Hou, X. et al. Concerns about phytoplankton bloom trends in global lakes. Nature 590, E35–E47 (2021). https://doi.org/10.1038/s41586-021-03254-3

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