## 3 Results  
### Data Level and Month Selection  
As shown in Table 5, in most cases, Level-1 data demonstrate stronger correlations between soil salinity and spectral data compared to Level-2 data. For instance, on January 15, the CO band in Level-1 data shows a correlation of 0.32 with salinity, significantly higher than 0.16 in Level-2. This trend, observed in March, August, and December data as well (with the exception of February 16), suggests that Level-1 data are more reliable in the CO band. Although the differences between Level-1 and Level-2 are relatively small in other bands, Level-1 data generally have slightly higher correlations overall. For example, on August 26, the B, G, R, SWIR1, and SWIR2 bands in Level-1 data all show slightly higher correlations. The only exception is the February 16 data, where Level-2 data exhibit marginally stronger correlations in the CO, B, G, R, and NIR bands. However, since the correlations on February 16 are generally low (all below 0.15), this date is excluded. Consequently, this study used the more strongly correlated Level-1 data for analysis.  
Figure 5: Workflow of this research.  
\begin{table}
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When selecting an appropriate month for remote sensing of soil salinity, the following key factors were considered:  
1. Correlation between Soil salinity and Spectral Bands. As shown in Table 5, data from January 15 and August 26 demonstrate significantly higher correlations between soil salinity and spectral data compared to other months. Specifically, on August 26, the CO, B, G, and R bands in Level-1 data all exhibit correlations above 0.4, with values of 0.32, 0.29, 0.27, and 0.21, respectively, while the corresponding bands in February, March, and December all fall below 0.2. From a correlation perspective, August 26 performs better than January 15.  
2. Collinearity between Spectra bands. High collinearity may lead to information redundancy, which is unfavorable for building an accurate model. As Figure 6 indicates, on August 26, the CO band is highly correlated (above 0.7) with B, G, R, and SWIR2 bands. On January 15, while the CO band shows high correlation with B, G, and R, its correlation with SWIR1 and SWIR2 is very low. Additionally, on August 26, the B, G, and R bands also exhibit high correlation with SWIR2, while on January 15, B shows low correlation with SWIR2. The NIR and SWIR2 bands on January 15 show high correlation, while on August 26, the correlation is only \(-\)0.17. Finally, the total absolute sum of correlations between each band (excluding self-correlations) were calculated, with January 15 data totaling 11.57, lower than August 26's 13.04, indicating lower collinearity in the January 15 data.  
3. Expert and Literature Insights.  
Consultations with agricultural remote sensing experts revealed that vegetation cover can interfere with the accuracy of salinity inversion, making it easier to observe salt accumulation during non-growing seasons. Additionally, [PERSON] et al. [42] noted that soil salinity inversion may be influenced by air humidity. Humidity variations can affect the absorption and reflection of electromagnetic radiation, thereby impacting prediction stability. The Yellow River Delta is relatively dry in January, while August is more humid, suggesting January is better suited for salinity inversion.  
In summary, although the August 26 data perform better in terms of correlation, the January 15 data are superior in terms of collinearity and the expert and literature support. Therefore, both January 15 and August 26 data were used to construct PLSR, RF, and BPNN models enabling the determination of the most suitable month for inversion.