Preprints
https://doi.org/10.5194/essd-2021-6
https://doi.org/10.5194/essd-2021-6

  18 Jan 2021

18 Jan 2021

Review status: this preprint is currently under review for the journal ESSD.

A New Global Gridded Sea Surface Temperature Data Product Based on Multisource Data

Mengmeng Cao1,, Kebiao Mao1,2,, Yibo Yan1, Jiancheng Shi3, Han Wang1, Tongren Xu4, Shu Fang5, and Zijin Yuan1 Mengmeng Cao et al.
  • 1Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
  • 2School of Physics and Electronic-Engineering, Ningxia University, Yinchuan, 750021, China
  • 3National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
  • 4State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
  • 5School of Earth Sciences and Resources, China University of Geosciences,Beijing, 100083, China
  • These authors contributed equally to this work and should be considered co-first authors.

Abstract. Sea surface temperature (SST) is an important geophysical parameter that is essential for studying global climate change. Although sea surface temperature can currently be obtained through a variety of sensors (MODIS, AVHRR, AMSR-E, AMSR2, Windsat, in situ sensors), the temperature values obtained by different sensors come from different ocean depths and different observation times, so different temperature products lack consistency. In addition, different thermal infrared temperature products have many invalid values due to the influence of clouds, and passive microwave temperature products have very low resolutions. These factors greatly limit the applications of ocean temperature products in practice. To overcome these shortcomings, this paper first took MODIS SST products as a reference benchmark and constructed a temperature depth and observation time correction model to correct the influences of the different sampling depths and observation times obtained by different sensors. Then, we built a reconstructed spatial model to overcome the effects of clouds, rainfall and land interference that makes full use of the complementarities and advantages of SST data from different sensors. We applied these two models to generate a unique global 0.041° gridded monthly SST product covering the years 2002–2019. In this dataset, approximately 25 % of the invalid pixels in the original MODIS monthly images were effectively removed, and the accuracies of these reconstructed pixels were improved by more than 0.65 °C compared to the accuracies of the original pixels. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses. The product will be of great use in research related to global change, disaster prevention and mitigation and is available at http://doi.org/10.5281/zenodo.4419804 (Cao et al., 2021).

Mengmeng Cao et al.

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A New Global Gridded Sea Surface Temperature Data Product Based on Multisource Data Mengmeng Cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, and Zijin Yuan https://doi.org/10.5281/zenodo.4419804

Mengmeng Cao et al.

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Short summary
we constructed a temperature depth and observation time correction model to eliminate the sampling depth and temporal differences among different data. Then, we proposed a reconstructed spatial model that filters and removes missing pixels and low-quality pixels contaminated by clouds from raw SST images and retrieves real sea surface temperatures under cloud coverage based on multisource data to generate a high-quality unified global SST product with long-term spatiotemporal continuity.