A Genetic Algorithm–Assisted Deep Neural Network Model for Merging Microwave and Infrared Daily Sea Surface Temperature Products - Xiao, Changjiang; Hu, Chuli; Chen, Nengcheng; Zhang, Xiang; Chen, Zeqiang; Tong, Xiaohua (Frontiers in Environmental Science, 2021)
Sea surface temperature (SST) is an important factor in the global ocean-atmosphere system, being vital in a variety of climate analyses and air-sea interaction researches. However, estimating daily SST with both high precision and high spatial completeness remains a challenge. This paper attempts to solve this problem by merging two complementary daily SST products, i.e., the 25km-resolution Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SST and 4km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) SST, using a genetic algorithm-assisted deep neural network model (GA-DNNM). The merged SST with a spatial resolution of 4 km and a temporal resolution of 1 day is achieved. Experiments in the Asia and Indian-Pacific Ocean (AIPO) region in 2005 were conducted to demonstrate feasibility and advantages of the proposed method. Results showed that the spatial coverages of original MODIS SST and AMSR-E SST are ranging from 25.0% to 48.1%, and 31.5% to 47.6% respectively, while the merged SST achieves a spatial coverage ranging from 56.1% to 73.1%, with improvements ranging from 50.2% to 131.7% relative to the original MODIS SST. Comparisons with drifting buoy observations indicate that the merged SST are accurate with an average bias of 0.006°C and an average RMSE of 0.502°C in places where MODIS SST data is missing before merging in the AIPO area, and with an average bias of -0.082°C and an average RMSE of 0.603°C for the merged SST in whole study area.