A new global gridded sea surface temperature data product based on multisource data

Cao, Mengmeng; Mao, Kebiao; Yan, Yonggang; Shi, Jiancheng; Wang, Han; Xu, Tong; Fang, Shu; Yuan, Zijin

doi:10.5194/essd-13-2111-2021

Cited by 13 publications

(8 citation statements)

References 77 publications

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“…Furthermore, we observed from the statistical graphs depicting the error of the three datasets compared to Modis-A (Figures [9][10][11] that while the error exhibited more significant fluctuations at night compared to during the day, the error magnitude itself was relatively lower and more consistent at night. Considering the complexity of the inversion algorithm and the impact of cloud coverage, it is possible that the quality of the data during nighttime may be superior to that of the daytime.…”

Section: Discussionmentioning

confidence: 88%

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

Feng

Yin

et al. 2023

Remote Sensing

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The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.

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Section: Discussionmentioning

confidence: 88%

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

Feng

Yin

et al. 2023

Remote Sensing

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“…Currently, ocean temperature data from diverse sources such as Argo floats, oceanic vessels, and observation platforms are steadily increasing. However, the limited coverage of in situ ocean observation data poses challenges related to the non-uniform spatial distribution and discrete temporal patterns, resulting in the difficulty in acquiring largescale synchronized SST data [5][6][7]. In contrast to traditional SST monitoring methods, remote sensing technology has the characteristics of having a large scale, wide coverage, and strong continuity, which can cover a wide range of ocean regions and provide global ocean data.…”

Section: Introductionmentioning

confidence: 99%

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

Li,

Wei,

Zhang

et al. 2024

Remote Sensing

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The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

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“…Microwave sensors can observe seawater despite having lower spatial resolution, as they can penetrate clouds, fog, and dust in almost all situations except during the heaviest rainfall [9][10][11]. Infrared sensors provide higher-resolution SST observations, but their inability to penetrate cloud cover leads to data interruptions and gaps, as the sensors cannot effectively capture SST values obscured by clouds [12][13][14][15][16]. These data gaps can lead to inconsistencies in SST records, affecting our ability to detect trends, limit cloud radiation feedback, and pose challenges in the precise analysis and modeling of oceanic and atmospheric processes [17].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Putra,

Hsu

2024

IJGI

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Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

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A new global gridded sea surface temperature data product based on multisource data

Cited by 13 publications

References 77 publications

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

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