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

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