## 3 Results
### Visualization of Salinity Distribution in the Yellow River Delta  
From the above results, it found that random forest performed the best in soil salinity inversion for the Yellow River Delta. Thus, the trained random forest model was applied to the atmosprically corrected Landsat 8 OLI Collection 2 Level-1 data from January 15 (already clipped into Kenli county according to the Shapefile). First, the image and the trained model were imported into Python. Then, applying the previously used data processing methods, including 29 indicators calculations and 6 data transformation methods  
Figure 11: Summary of the performance of the three models for August 26. The darker the color in the R\({}^{2}\) area, the higher the R\({}^{2}\) value; the lighter the color, the lower the R\({}^{2}\) value. Figure 10: Summary of the performance of the three models for January 15. (reciprocal \((\mathbf{1}/\mathbf{R})\), logarithm \(\mathbf{InR}\), exponential \(\boldsymbol{e^{R}}\), square root \(\boldsymbol{R^{0.5}}\), first derivative \(\boldsymbol{R}^{{}^{\prime}}\), and second derivative \(\boldsymbol{R}^{{}^{\prime}}\)), the random forest model utilized these data (original bands, spectral indicators, and mathematically transformed variables) to predict the salinity values of each raster, ultimately defining a color for each soil salinity level and creating a color map. The soil salinity distribution of the Yellow River Delta is shown in Figure 12.  
Additionally, the number of pixels for each color was obtained in python and then the surface area and the percentage of salinity for each class were calculated (Table 8). To further elucidate the regional variations in soil salinity and the underlying geographical and hydrological driving factors, we introduced land use and soil classification data (see Figure 13). The land use map (a) shows land use data based on Sentinel-2 imagery with a 10 m resolution, while the soil classification map (b) is based on soil type data with a 1 km resolution. As shown in Figures 12 and 13a, it is evident that our soil salinity inversion results highly align with the actual patterns. Figure 12: Prediction of soil salinity in Yellow River Delta. \begin{table}
\begin{tabular}{c c c c} \hline
**Soil Salinity Class (g/kg)** & **Pixels Number** & **Surface Area (km\({}^{2}\))** & **Percentage** \\ \hline Non-Saline [0–2] & 289,383 & 261 & 0.16 \\ \hline Slightly Saline [2–4] & 420,814 & 379 & 0.23 \\ \hline Moderately Saline [4–6] & 213,716 & 192 & 0.12 \\ \hline Strongly Saline [6–10] & 475,798 & 428 & 0.26 \\ \hline Extremely Saline [10+] & 436,527 & 392 & 0.23 \\ \hline SUM & 1,836,238 & 1652 & 1.0 \\ \hline \end{tabular}
\end{table} 
Table 8: The predicated area of soil salinity on each level. In terms of land use types, the distribution of non-saline and slightly saline areas largely overlaps with cropland. Specifically, non-saline areas occupy 16% (Table 8), a salinity level suitable for the growth of most plants, while slightly saline areas account for 23% (Table 8), a salinity level that only supports a few salt-tolerant plants. Therefore, at least 50% of the crops in the Yellow River Delta Kenli District are salt-tolerant crops such as cotton and wheat. On the other hand, cropland requires good irrigation conditions. From Figure 12, we can see that apart from the Yellow River flowing through the region, many lakes are distributed within the cropland, which also helps prevent the accumulation of soil salinity, thus reducing the degree of soil salinization. Additionally, as shown in Figure 12, areas of strongly and extremely saline are primarily concentrated near the coordinates 37\({}^{\circ}\)49\({}^{\prime}\) N, 119\({}^{\circ}\)1\({}^{\prime}\) E, close to the Yellow River mouth, where seawater intrusion significantly impacts the area. The moderately saline area often occurs alongside a strongly saline area. Table 8 shows that 12% of the soil is moderately saline while nearly 49% of the land is strongly saline or extremely saline, which is unsuitable for the growth of most plants.