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.

Multisource data and data fusion technology is an effective way to obtain large-scale and refined sea surface temperature values. A research team from the Institute of Agricultural Resources and Regional Planning (IARRP), CAAS made use of the one-dimensional ocean mixed layer model and constructed a temperature depth and observation time correction model to adjust for the different sampling depths and observation times obtained by different sensors.

Based on the MODIS SST data, the team then built a reconstructed SST spatial model and presents a new monthly SST product with full spatiotemporal coverage based on multisource data from 2002 to 2019.

This dataset effectively addresses the issues of inconsistent observation times and sampling depth of multisource data and compensates for the insufficiency of reconstructing actual SST pixels under clear-sky conditions rather than under clouds in some studies with very limited information, achieving good temporal and spatial coverage.

It has been verified that the data is highly reliable and precise. This research provides important data support for mesoscale ocean phenomenon analysis and global climate change research.

The research results were published in Earth System Science Data (IF 11.333). This research and data preparation were funded by the National Natural Science Foundation of China (41921001), the National Key Research and Development Program of China (2018YFC1506502, 2018YFC1506602), and the Open Fund Project of the State Key Laboratory of Remote Sensing Science (1610132020014, of SLRSS201910). PhD student Cao Mengmeng is the first author, and research fellow Mao Kebiao from the IARRP, CAAS is the corresponding author and co-first author.

Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan, Z.: A new global gridded sea surface temperature data product based on multisource data, Earth Syst. Sci. Data, 13, 2111–2134, 2021.

https://doi.org/10.5194/essd-13-2111-2021

http://doi.org/10.5281/zenodo.4419804