Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

Addressing the issue of data sparsity and gaps caused by missing values, this study employs an information diffusion approach to effectively spread information from sparse sample points to monitoring locations. By thoroughly extracting insights from a limited dataset, it achieves more precise interpolation outcomes. To validate the superiority of the information diffusion interpolation technique under conditions of sparse samples, we utilize sea surface temperature (SST) data from the offshore waters of China as a case study. We compare three interpolation methods: Kriging, Gaussian information diffusion, and asymmetric information diffusion. The calculations and comparisons of interpolation results are conducted across varying sample sizes. The findings indicate that in situations with relatively sparse samples, asymmetric information diffusion yields the most favorable results, with Kriging and Gaussian diffusion exhibiting comparable performance. In cases of extremely sparse samples, asymmetric information diffusion yields the lowest interpolation error, followed by Gaussian diffusion, while Kriging performs the least effectively. Thus, when confronted with sample sparsity, the application of the information diffusion interpolation method can yield notably improved results.