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This research was funded by National Natural Science Foundation of China U1811464, U21A6001, and the CAS Key Laboratory of Science and Technology on Operational Oceanography Open Project Funding No OOST2021-03.

Analysis of institutional authors

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Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation

Publicated to:Remote Sensing. 14 (11): 2587- - 2022-06-01 14(11), DOI: 10.3390/rs14112587

Authors: Song, Tao; Wei, Wei; Meng, Fan; Wang, Jiarong; Han, Runsheng; Xu, Danya

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Abstract

Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method-convolutional long short-term memory network (ConvLSTM)-for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models' robustness and generalizability by considering ocean variability's significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 degrees C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data's spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.

Keywords

ArgoBrainClimate changeConvolutionConvolutional long short-term memoryConvolutional long short-term memory (convlstm)Decision treesDeeper oceanInferenceLong short-term memoryMean square errorModelOcean dynamicsOcean observationsOceanographyPacific-oceanReconstructionRemote sensingRemote sensing dataSalinity fieldsSatelliteSatellite observationsSignalsSpatio-temporal correlationSpatiotemporal correlationSubsurface temperatureSurface warming hiatusSurface watersTemperature and salinity fieldTemperature and salinity fieldsVertical profiles

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 31/202, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.26. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.07 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 6.28 (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-01, the following number of citations:

  • WoS: 15
  • Scopus: 18
  • OpenCitations: 16

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-01:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 16 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (SONG, TAO) .