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Grant support

This work was supported by the Major Projects of National Natural Science Foundation of China (U20A20105), National Key Research and Development Project of China (2021YFA1000103, 2021YFA1000102), National Natural Science Foundation of China (Grant Nos. 61972416, 62272479, 62202498), Taishan Scholarship (tsqn201812029), Shandong Provincial Natural Science Foundation (ZR2021QF023), Fundamental Research Funds for the Central Universities (21CX06018A), Spanish project PID2019-106960GB-I00, Juan de la Cierva IJC2018-038539-I.

Analysis of institutional authors

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Article

Convformer: A Model for Reconstructing Ocean Subsurface Temperature and Salinity Fields Based on Multi-Source Remote Sensing Observations

Publicated to:Remote Sensing. 16 (13): 2422- - 2024-07-01 16(13), DOI: 10.3390/rs16132422

Authors: Song, Tao; Xu, Guangxu; Yang, Kunlin; Li, Xin; Peng, Shiqiu

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Abstract

Observational data on ocean subsurface temperature and salinity are patently insufficient because in situ observations are complex and costly, while satellite remote-sensed measurements are abundant but mainly focus on sea surface data. To make up for the ocean interior data shortage and entirely use the abundant satellite data, we developed a data-driven deep learning model named Convformer to reconstruct ocean subsurface temperature and salinity fields from satellite-observed sea surface data. Convformer is designed by deeply optimizing Vision Transformer and ConvLSTM, consisting of alternating residual connections between multiple temporal and spatial attention blocks. The input variables consist of sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Our results demonstrate that Convformer exhibits superior performance in estimating the temperature-salinity structure of the tropical Pacific Ocean. The all-depth average root mean square error (RMSE) of the reconstructed subsurface temperature (ST)/subsurface salinity (SS) is 0.353 degrees C/0.0695 PSU, with correlation coefficients (R-2) of 0.98663/0.99971. In the critical thermocline, although the root mean square errors of ST and SS reach 0.85 degrees C and 0.121 PSU, respectively, they remain smaller compared to other models. Furthermore, we assessed Convformer's performance from various perspectives. Notably, we also delved into the potential of Convformer to extract physical and dynamic information from a model mechanism perspective. Our study offers a practical approach to reconstructing the subsurface temperature and salinity fields from satellite-observed sea surface data.

Keywords

Deep learningLearning systemsMean square errorOcean remote sensingOceanographyPerformancePhysicPhysicsRemote sensingSalinity fieldsSatellitesSea surfacesSubsurface salinitySubsurface salinity (ss)Subsurface temperatureSubsurface temperature (st)Surface dataSurface temperatureSurface watersTransformerTropical pacificVariabilit

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, 2024 there are still no calculated indicators, but in 2023, it was in position 110/358, thus managing to position itself as a Q1 (Primer Cuartil), in the category Environmental Sciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-01:

  • WoS: 1
  • Scopus: 5

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: 4 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    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) .