Abstract
Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.
Highlights
Sea Surface Temperature (SST) is an important driver and tracer of the global atmosphere and ocean circulations in terms of air–sea interaction [1,2,3,4]
The results showed that the reconstructed MODIS and AMSR2 Sea SurfaceTemperature (SST) through Data Interpolate Convolutional AutoEncoder (DINCAE) yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 ◦ C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 ◦ C, respectively
We proposed a data fusion approach for generating high-resolution seamless daily SST based on machine learning (i.e., DINCAE and Random Forest (RF)) over the Kuroshio
Summary
Sea Surface Temperature (SST) is an important driver and tracer of the global atmosphere and ocean circulations in terms of air–sea interaction [1,2,3,4]. SST has been widely used in ocean studies due to its extensive spatiotemporal coverage, such as the monitoring of oceanfront, eddy, and turbulence [5,6,7,8,9]. Two types of satellite sensors have been used to retrieve SST: thermal infrared and passive microwave sensors. Thermal infrared sensors (e.g., Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)) provide relatively high spatial (i.e., 1 km) and temporal (i.e., subdaily) resolution SST data. They often suffer from cloud contamination, sun glint effect, and aerosols, resulting in missing data especially in the Western Pacific Ocean.
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