SCMNet: Toward Subsurface Chlorophyll Maxima Prediction Using Embeddings and Bi-GRU Network

Wang, Ao; Huang, Baoxiang; Yang, Jie; Chen, Ge; Radenkovic, Milena

doi:10.1109/jstars.2023.3325922

Cited by 2 publications

(4 citation statements)

References 37 publications

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“…Analogous results have been found in northern India Ocean (Hu et al, 2023). Compared with previous studies applied on surface measurements only (SST and surface Chl-a), adding vertical physics properties (water temperature and salinity) as input variables leads the NSE increasing from ~70% to ~90% (Sammartino et al, 2018;Chen et al, 2022a;Wang et al, 2023a). Here, we emphasize that in the tropical area of the NWPO, surface Chl-a and vertical temperature profiles are two effective input variables in accurately estimating vertical Chl-a profiles.…”

Section: Sensitivity Of Input Variablessupporting

confidence: 79%

“…In the subarctic gyres of the NWPO, surface Chl-a presented an increased tendency during 1997-2020 (Chen et al, 2022b). However, given the prevailing SCMs in the NWPO (Cornec et al, 2021;Chen et al, 2022a;Wang et al, 2023a), caution is warranted when extending conclusions from surface Chl-a to water-column integrated Chl-a. Meanwhile, the lack of long-term 3-D Chl-a structure means that trends in water-column integrated Chl-a in the NWPO remain an open question.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Sammartino et al (2018) utilized an artificial neural network (ANN) model (single hidden layer) on sea surface temperature (SST) and surface Chl-a data, to infer the vertical profiles of Chl-a in the Mediterranean Sea. Similarly, also based on SST and surfaceocean Chl-a datasets, Chen et al (2022a) and Wang et al (2023a) analyzed SCMs in the northwestern Pacific Ocean (NWPO) using deep learning models. However, their studies commonly overlooked potential impacts of network design along ocean depths in accuracy to the results.…”

Section: Introductionmentioning

confidence: 99%

“…They possess the capability to autonomously extract intricate features from raw data and demonstrate higher scalability in managing extensive and complex datasets (Li et al, 2020). Moreover, DNN models have been certified to exhibit superior performance in retrieving vertical bio-optical properties (e.g., Chl-a, nitrate) compared to ANN models (Chen et al, 2022a;Wang et al, 2023a, b) and RF models (Wang et al, 2023a) in the NWPO. Therefore, through the precise reconstruction of 3-D Chl-a using an enhanced DNN model, our study aims to an improved understanding of the variability of SCMs characteristics and longterm trends in depth-integrated Chl-a in the NWPO.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

Zhao,

Gong,

Gong

et al. 2024

Front. Mar. Sci.

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Insufficient studies in characterizing vertical structure of Chlorophyll-a (Chl-a) in the ocean critically limit better understanding about marine ecosystem based on global climate change. In this study, we developed a Gaussian-activation deep neural network (Gaussian-DNN) model to assess vertical Chl-a structure in the upper ocean at high spatial resolution. Our Gaussian-DNN model used the input variables including satellite data of sea surface Chl-a and in-situ vertical physics profiles (temperature and salinity) in the northwestern Pacific Ocean (NWPO). After validation test based on two independent datasets of BGC-Argo and ship measurement, we applied the Gaussian-DNN model to reconstruct temporal evolution of 3-D Chl-a structure in the NWPO. Our modelling results successfully explain over 80% of the Chl-a vertical profiles in the NWPO at a horizontal resolution of 1° × 1° and 1 m vertical resolution within upper 300 meters during 2004 to 2022. Moreover, according to our modelling results, the Subsurface Chlorophyll Maxima (SCMs) and total Chl-a within 0-300 m depths were extracted and presented seasonal variability overlapping longer-time trends of spatial discrepancies all over the NWPO. In addition, our sensitivity testing suggested that sea-water temperatures predominantly control 3-D structures of the Chl-a in the tropical NWPO, while salinity played a key role in the temperate gyre of the NWPO. Here, our development of the Gaussian-DNN model may also be applied to craft long term, 3-D Chl-a products in the global ocean.

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Section: Sensitivity Of Input Variablessupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

Zhao,

Gong,

Gong

et al. 2024

Front. Mar. Sci.

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A global four‐dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles

Xue,

Wang,

Yue

et al. 2024

Geoscience Data Journal

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Lack of a long‐term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC‐Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC‐Argo has shorter time series coverage and fewer profiles compared to Core‐Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core‐Argo and BGC‐Argo profiles and then developing a spatial, temporal and depth‐specific gridded DOC dataset, named G4D‐DOC. Validation results demonstrate that G4D‐DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D‐DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D‐DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D‐DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D‐DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.

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SCMNet: Toward Subsurface Chlorophyll Maxima Prediction Using Embeddings and Bi-GRU Network

Cited by 2 publications

References 37 publications

Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

A global four‐dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles

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