## 3 Results
### EOF Analysis with Geophysical Data  
Four EOFs of the S1A_1K soil moisture, two EOFs of the MODIS NDVI, and three EOFs of the MODIS temperature remain significant after the North test. The EOFs and the corresponding PCs are shown in Figures 5-7, respectively. The first four EOFs of the soil moisture explain 77.2% of the variance in total, and the primary EOF alone captures 66.2%. The results indicate that the complex pattern of S1A_1K soil moisture can be largely explained by a small number of underlying spatial structures. As the corresponding  
Figure 4: Comparison between S1A_0.25 and the microwave soil moisture. Column 1: the averaged soil moisture in the wet seasons; Column 2: the averaged soil moisture in the dry seasons; Column 3: the \(ubRMSD\) of the microwave products calculated with S1A_0.25 pointwise; Column 4: [PERSON]’s correlation coefficient (\(R\)) of the microwave products calculated with S1A_0.25 pointwise.  
PC in Figure 6 shows no significant fluctuations, the first EOF of S1A_1K represents an averaged pattern of soil moisture: higher values gather in the southeast and northeast corner, while the southwest part shows lower values. EOF2 explains 6.2% of the total variance and is the dominant EOF in the wet seasons of 2015 and 2016, which indicates the pattern that higher soil moisture values in the northwest are more likely to occur with the increase of precipitation. The third structure, EOF3, which shows a balanced distribution, explains 2.9% of the total variance and dominates the soil moisture pattern immediately after the wet seasons. EOF4 explains 1.9% of the total variance and also shows a relatively balanced distribution, but only has a comparable dominance to EOF1 during the dry season and part of the wet season in 2015.  
The primary EOF of the MODIS NDVI explains 98.3% of the total variance, which indicates that this averaged pattern dominates over the whole period. Higher values tend to concentrate in the southwest of the NDVI map. EOF2 of the NDVI only explains 0.6% of the variance and dominates the NDVI patterns in September. The temperature pattern can also be largely explained by the primary EOF with 83.4%, where the values show an increasing trend from the west to the east. This tendency is the opposite in the second EOF, which explains only 3.0% of the variance. EOF3 of the temperature shows a descending trend from the south to the north, and explains only 1.4% of the variance. As none of the three EOFs shows a significant dominance over the observation period, the spatial pattern of temperature is more complex than soil moisture and the NDVI.  
The correlations between the EOFs of S1A_1K and geophysical factors were estimated by Pearson's correlation coefficients (\(R\)), and the results are shown in Table 3. For all of the EOFs of S1A_1K, the correlations of EOF1 with the geophysical factors are obviously higher than the others, although none of the correlations are particularly high. The results show that the percentage of clay and sand show relatively higher correlations with SM_EOF1, and the EOFs of the NDVI take second place. Elevation appears to have no significant correlations with the soil moisture patterns.  
Figure 5: The significant EOFs of S1A_1K, NDVI, and temperature. (**a**) The primary EOF of S1A_1K, with 66.2% of the total variance explained; (**b**) the 2 nd EOF of S1A_1K, with 6.2% of the total variance explained; (**c**) the 3 rd EOF of S1A_1K, with 2.9% of the total variance explained; (**d**) the 4 th EOF of S1A_1K, with 1.9% of the total variance explained; (**e**) the primary EOF of NDVI, with 98.3% of the total variance explained; (**f**) the 2 nd EOF of NDVI, with 0.6% of the total variance explained; (**g**) the primary EOF of temperature, with 83.4% of the total variance explained; (**h**) the 2 nd EOF of temperature, with 3.0% of the total variance explained; and (**i**) the 3 rd EOF of temperature, with 1.4% of the total variance explained.