the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023
Abstract. Long time series of spatiotemporally continuous phytoplankton functional type (PFT) products are essential for understanding marine ecosystems, global biogeochemical cycles, and effective marine management. In this study, by integrating artificial intelligence (AI) technology with multi-source marine big data, we have developed a Spatial–Temporal–Ecological Ensemble model based on Deep Learning (STEE-DL), and then generated the first AI-driven Global Daily gap-free 4 km PFTs product from 1998 to 2023 (AIGD-PFT), significantly enhancing the accuracy and spatiotemporal coverage of quantifying eight major PFTs (i.e., Diatoms, Dinoflagellates, Haptophytes, Pelagophytes, Cryptophytes, Green Algae, Prokaryotes, and Prochlorococcus). The input data encompass physical oceanographic, biogeochemical, spatiotemporal information, and ocean color data (OC-CCI v6.0) that have been gap-filled using a Discrete Cosine Transform with a Penalized Least Square (DCT-PLS) approach. The STEE-DL model utilizes an ensemble strategy with 100 ResNet models, applying Monte Carlo and bootstrapping methods to estimate optimal PFT values and assess model uncertainty through ensemble means and standard deviations. The model's performance was validated using multiple cross-validation strategies—random, spatial-block, and temporal-block—combined with in-situ data, demonstrating STEE-DL's robustness and generalization capability. The daily updates and seamless nature of the AIGD-PFT product capture the complex dynamics of coastal regions effectively. Finally, through a comparative analysis using a triple-collocation (TC) approach, the competitive advantages of the AIGD-PFT product over existing products were validated. The AIGD-PFT product not only provides the foundation for detailed analyses of PFT trends, interannual variability, and the impacts of climate change on phytoplankton composition across various temporal and spatial scales, but also has the potential to facilitate precise quantification of marine carbon flux and enhances the accuracy of biogeochemical models. A video demonstration is available at https://doi.org/10.5446/67366 (Zhang and Shen, 2024a). The complete product dataset (1998–2023) can be freely downloaded at https://doi.org/10.11888/RemoteSen.tpdc.301164 (Zhang and Shen, 2024b).
- Preprint
(17247 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 03 Jul 2024)
-
RC1: 'Comment on essd-2024-122', Anonymous Referee #1, 19 Jun 2024
reply
General comments:
The paper by Zhang et al. presents the first AI-driven product for Phytoplankton Function Types (PFT) for the global ocean (AIGD-PFT). The AIGD-PFT consists of a L4 gap-free product including 8 PFT at daily and 4-km resolution for the period 1998-2023. AIGD-PFT is generated using an extended ensemble modelling approach (STEE-DL), which is based on machine and deep learning technologies and includes 100 models. Each model is built on statistical relationships between the physical environment and phytoplankton community and incorporates in situ HPLC data, ocean colour satellite observations whose missing data have been reconstructed throughout a cost-efficient DCT-PLS method, physical data from reanalysis and biogeochemical inputs from hindcast simulations.
Overall, the study falls within the scope of ESDD, methods are robust, and the manuscript is well written and detailed. Moreover, I believe that the AIGD-PFT product will be a very useful tool for all scientists interested in detecting climate-induced changes in the phytoplankton community. Therefore, I recommend this paper for publication, although I feel that some clarifications should be addressed to strengthen the way it is presented.
Specific comments:
- Authors present the AIGD-PFT as the product with the longest time span, covering 26 years (i.e., 1998-2023). However, I double checked the data sets used to create it and found some discrepancies that need to be clarified. In particular, except for the ESA-OC-CCI data set, which covers the whole period, I found that SST data from https://doi.org/10.48670/moi-00169 and biogeochemical variables from https://doi.org/10.48670/moi-00019 are available until October 2022 and December 2022, respectively, while SSS from https://doi.org/10.48670/moi-00016 is available from January 2022 to June 2024. So, I am not sure how authors create a 26-year product using some data sets that do not cover the same period.
- As reported in Sect. 2.2.3, all physical and biogeochemical data have been resampled to a 4 km resolution, and I believe that this was done to match the high spatial resolution of the ESA-OC-CCI product. However, any time data are resampled to a higher resolution, a greater but false accuracy is introduced due to the assumption that all new pixels have the same value when it may only be true for one pixel. This is why, as far as I know, the remapping direction is typically from high to low resolution. I would therefore ask authors to discuss this choice and, if possible, include a reference to previous works applying the same strategy. An interesting paper that may help the discussion can be found at https://journals.ametsoc.org/view/journals/apme/60/11/JAMC-D-20-0259.1.xml.
- Page 8, line 151: The sentence needs to be reworded because, as reported in the Product Guide (https://docs.pml.space/share/s/fzNSPb4aQaSDvO7xBNOCIw), the latest ESA-OC-CCI product (v6.0) also merges observations from OLCI-3A and OLCI-3B.
- I found the method used by authors to fill OC data gaps well described in Sect. 2.2.2. However, I think that specifying the number of available data before and after the filling procedure would be interesting and emphasize the effort authors have made. This information could also be presented by replacing Figure 3 with two Hovmöller diagrams showing the number of observations before and after the filling as function of time and latitude.
- The choice to include the 8 PFTs as listed in the manuscript should be justified. I think that adding reference(s) should be enough to do that.
- The definition of ResNet models (i.e., residual neural networks) is given in Sect. 2.3.1, but I think it should be provided earlier as they are mentioned before Sect. 2.3.1.
- I suggest authors to go through the manuscript and split some long sentences to make the text more readable. For example, the second sentence in the abstract, which starts on line 2 and ends on line 14, can be split into at least three sentences.
- I found some errors in the reference list (e.g., Zhang and Shen, 2024a,b,c). Please, check them carefully against the references as cited in the abstract and main text.
To conclude, I would like to mention that, as stated by authors, model interpretability is beyond the scope of this manuscript and will be a focus of a future work. I look forward to that. So, keep up the good progress!
Citation: https://doi.org/10.5194/essd-2024-122-RC1
Data sets
AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023 Yuan Zhang, Fang Shen, Renhu Li, Mengyu Li, Zhaoxin Li, Songyu Chen, and Xuerong Sun https://doi.org/10.11888/RemoteSen.tpdc.301164
Video supplement
AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023 Yuan Zhang, Fang Shen, Renhu Li, Mengyu Li, Zhaoxin Li, Songyu Chen, and Xuerong Sun https://doi.org/10.5446/67366
Video abstract
AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023 Yuan Zhang, Fang Shen, Renhu Li, Mengyu Li, Zhaoxin Li, Songyu Chen, and Xuerong Sun https://doi.org/10.5446/67366
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
474 | 82 | 16 | 572 | 15 | 10 |
- HTML: 474
- PDF: 82
- XML: 16
- Total: 572
- BibTeX: 15
- EndNote: 10
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1