the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery
Abstract. Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at http://dx.doi.org/10.12157/IOCAS.20240410.002 (Gao et al., 2024).
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RC1: 'Comment on essd-2024-125', Qianguo Xing, 02 Jun 2024
There is a long history of floating green macroalgae blooms in the Yellow Sea, which might be dated back to the year of 1999 (Lline 130, the introduction is wrong). On the basis of optical and microwave data of MODIS, Sentinel-1, the authors presented a weekly dataset of green tide coverage in the Yellow Sea since 2008. With the consideration of the continuing occurrence of large scale green tides in the Yellow Sea in recent years, the dataset in this paper would be helpful for exploring the dynamics of the green tide as well as the causes. This paper may be published after making some improments listed as below.
1. For the dataset, why the weekly one is important when the daily and monthly dataset are available (Hu et al., 2023)? No biomass is presented or discussed in the paper, which is a drawback. Please clarify these issues. And, the time series of maximum daily coverages and weekly coverages should be presented in the results.Â
2. For the growth model of green tide, the discussion seems to be misleading. As shown by Xing et al., 2018, 2019, the maximum biomass (or coverage area) of green tide is not only regulated by the initial biomass on the seaweed cultivation rafts but also mainly the lasting period in the eutrophicated turbid waters of the Jiangsu shoal where the green macroalgae has a high daily growth rate. The lasting period is regulated by the patterns of wind and sea surface current, for which I am not sure that the HYCOM would provide the accurate data over the study area. The biomass of green tide in 2023 give a good case on the growth model of green tide. The authors may check and revise the relevant sections of this paper. I suggest to remove the section 3.5.
3. The species of green tide is Ulva prolifera. Pelase correct the term.Â
4. The overflying time of different sensors and the impacts on the results should be presented and discussed.Â
5. The verification in the section of 3.2, is not useful, and may be removed.Â
6. Fig.13 subfigures should be presented with a same granule size.Â
7. The abstract does not mention the discontinuity in the daily coverage data. The "continuity" of the weekly data versus the daily data is a significant difference and should be emphasized.
8. Regarding GANet, what is the rationale for selecting specific GLCM features? Since GLCM texture includes many features, which ones are most important for green tide detection?
9. Lines 49-50: The phrase "Unaffected by clouds and rain, ... by clouds and rain" is repeated and should be revised.
10. In Figure 2 on page 6, MODIS is mentioned for optical images. It is recommended to specify Sentinel-1 SAR images when discussing SAR images.
11. In line 226, the phrase "when feeding image slices into the AlageNet model" appears in a paragraph discussing the GANet model. Please clarify the intended model. Based on the context, it seems this should refer to the GANet model, not the AlageNet model.
12. The authors developed two models, AlgaeNet and GANet. Why not create a unified model to identify green algae using both SAR and MODIS images?
13. Why is the image slice size for the AlgaeNet model 128x128 pixels, while for the GANet model it is 256x256 pixels? Please clarify the reasoning behind these different dimensions.
14. In Table 2, why is the number of testing samples smaller for the new GANet model?
15. Is the comparison in Figure 11 meaningful? What is the difference between the two calculations of weekly average coverage?
Citation: https://doi.org/10.5194/essd-2024-125-RC1 - AC1: 'Reply on RC1', Le Gao, 10 Jun 2024
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RC2: 'Comment on essd-2024-125', Anonymous Referee #2, 03 Jun 2024
With the help of machine learning, Dr Gao et al., provides a nice dataset of weekly green tide in the Yellow Sea. Several methodology and data sources were considered. They performed a cross validation to show the quality of the generated dataset. I believe this dataset will be useful for the further studies with other purposes; for example, a joint analysis with the wind or sea surface velocity, to have a better understanding the underlying dynamics. I would like to recommend a publication after several minor revisions. I list my comments below.
1 in line 16: There should be a space between ")" and "This".
2 Figure 2, 9, 11, 12: the quality of these figures are bad. Please provide a high resolution version of these figures.Â
3 line 140: please provide a value of "the submerged portions", for example, 0.5 meter beneath the sea surface.
4 line 162: please remove the duplication of "the" when mention "The fifth generation atmospheric reanalysis data"
5 line 203: please provide the full name of "VGG16"
6 line 206: "We used the unique physical multichannel combination of all bands of MODIS surface reflectance products as input". Is it possible to have an optimization combination of these bands? Or in other way, do we have contamination problem when all bands are involved?
7 line 215: "1.10 km2" should be "1.10 km^2"
8 line 232: "256 256" pixels should be "$256\times 256$"
9 line 233: "These enhancements have ... to 85.41%". Please specific from what value to "85.41%".
10 Figure 5: "view of the white square part". But in the figure 5, it is a green square. Please correct this typo.
11 line 264: "beneath a certain water depth": see comment 3. please provide a value here.
12 Figure 8: the box in the left panel is unclear.Â
13 line 320: the terminology "dissipation" is used. Is it possible to find another proper terminology? This is because in the fluid dynamics, "dissipation" means something else. For example, the energy dissipation means the conversion the kinetic energy to heat. Â
14 Figure 9: For a large value variation, we often use log-log plot. I strongly suggest authors to replot this figure in a log-log view to see possibility of lognormal or other distribution of areas.Â
15 Figure 10: please keep the label of "(g)" in the same style as others.
16 Figure 11: R^2=1 and RMSE=0 seems too good to be true. Please double check this result.
17 line 370: the Gompertz curve model is used to fit the data without further justification. Please provide more comments here.
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Citation: https://doi.org/10.5194/essd-2024-125-RC2 - AC2: 'Reply on RC2', Le Gao, 10 Jun 2024
Data sets
The green tide coverage product in the Yellow Sea during 2008-2022 Le Gao, Yuan Guo, and Xiaofeng Li http://dx.doi.org/10.12157/IOCAS.20240410.002
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