*2.6. Empirical Orthogonal Function Decomposition*

Empirical orthogonal function (EOF) decomposition, also known as eigenvector analysis, is a method to analyze the structural features of matrix data and extract the main data features [27]. Feature vector corresponds to space vector, also known as space feature vector or space mode, which reflects the spatial distribution characteristics of the factor field to a certain extent. The principal component (PC), also known as the time coefficient, corresponds to the time variation, which reflects the weight variation of the corresponding spatial mode with time. To investigate the causes of soil dryness in Guangxi, we further analyzed the correlation between the main variation model of SSMI (EOF-1) and its corresponding principal component (PC-1) and sea surface temperature (SST) from the perspective of remote correlation.

#### **3. Results**
