Reconstruction of arctic SST data and generation of multi-source satellite fusion products with high temporal and spatial resolutions
Yuheng Li, Weifu Sun, Jie Zhang, Junmin Meng, Yujia Zhao
Abstract
The use of remote sensing data, whether for change detection, spatial and temporal analysis, or numerical simulation of water bodies, requires a complete data set. Here, high temporal and spatial resolution sea surface temperature (SST) data for the Arctic are produced based on optimal interpolation and data reconstruction algorithms with a temporal resolution of 12 h and a spatial resolution of 9 km. The accuracy of the reconstructed data sets is verified using Argo measurements to demonstrate the validity and reliability of the data interpolation empirical orthogonal functions method. The root mean square error of the fusion data (a temporal resolution of 12 h) is relatively stable, ranging from 0.49°C to 0.62°C. In comparison with remote sensing systems (RSS) MW-IR SST and Advanced Very High Resolution Radiometer (AVHRR) OISST, the accuracy of the fusion data (a temporal resolution of 24 h) is in the range of 0.48–0.65°C, while that of the MW-IR SST is in the range of 0.48–0.60°C and AVHRR OISST is in the range of 0.66–0.79°C. The fusion data is more accurate than those of AVHRR OISST, and there is no obvious difference with RSS MW-IR SST.