High Spatiotemporal Resolution Sea Surface Temperature From MERSI and AGRI Sensors Based on Spatial and Temporal Adaptive Sea Surface Temperature Fusion Model

Large-scale and high spatiotemporal resolution sea surface temperature (SST) products can provide important support for monitoring dynamic changes in the marine environment, energy development, and assimilation models. We develop an algorithm to obtain the high spatiotemporal resolution SST product by fusion of the high spatial resolution SST product from the medium resolution spectral imager (MERSI) on the Fengyun-3E (FY-3E) satellite and the high temporal SST product from the advanced geosynchronous radiation imager (AGRI) on the Fengyun-4E (FY-4E) satellite. During data preprocessing, MERSI products have large data volumes stored in chunks, and we use the multicore computer for batch re-projection to generate the matrix of temperature values and satellite observation times. The matrix has high spatial coverage and removes the anomalous data, increasing the stability of the algorithm. AGRI data have gaps due to cloud cover, and we build a pixel-by-pixel linear fitting model to extract more valid change information from multihours data, which also improve the coverage of fusion results. During the fusion process, we calculate the spectral and temporal weighting factors in real time, divide data into regular blocks for multicores fusion to get 1 km/1 h SST fusion product covering most areas of the Eastern Hemisphere. Using Argo buoy data to verify the accuracy of the results for 15 days, the root mean square error (RMSE) is
$1.089~^{\circ }$
C and the average deviation is
$0.867~^{\circ }$
C. Multicore parallel computing makes algorithm fast and efficient. The spatiotemporal resolution of results is improved significantly and the effect of missing original data of the results is reduced.