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In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.

Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature