## 5 Conclusions  
This study analyzes the use of Landsat 8 OLI remote sensing images for salinity inversion in the Yellow River Delta. The research area focuses on the Kenli county in Shandong Province, China. Two levels of Landsat 8 OLI Collection 2 data (Level-1 and Level-2) were downloaded, with the months having less than 10% cloud cover being January 15, February 16, March 3, August 26, and December 16. Pearson correlation analysis of the spectral values and field-sampled soil salinity was conducted to determine the most suitable data level and months for inversion. Then combining 29 spectral indicators and six data transformation methods (reciprocal \((\mathbf{1}/\mathbf{R})\), logarithm \(\mathbf{In}\mathbf{R}\), exponential \(\boldsymbol{e^{R}}\), square root \(\mathbf{R^{0.5}}\), first derivative \(\mathbf{R}^{{}^{\prime}}\) and second derivative \(\mathbf{R}^{{}^{\prime\prime}}\)) and used original bands, spectral indicators, and transformed variables to construct the following three models: partial least squares regression (PLSR), random forest (RF), and back propagation neural network (BPNN). By constructing different models, the most suitable model was selected for salinity inversion in the Yellow River Delta to visualize the salinity distribution. This study shows that the Level-1 data, corrected using FLAASH atmospheric correction, outperforms Level-2 data, with January being the most suitable month for salinity inversion among the selected months. The random forest model exhibited the best performance for salinity inversion in the Yellow River Delta, with training set. This indicates that the random forest model effectively reflects soil salinity. Both the random forest (RF) and back propagation neural network (BPNN) models constructed in this study performed well, indicating that the construction of spectral indicators and mathematical transformations can effectively enhance the accuracy of soil salinity inversion in saline-alkali land. We also found that the original bands SWIR1 and SWIR2, along with their differentiated bands (second-order difference for SWIR1 and first-order difference for SWIR2), and the spectral indicators CLEX constructed from SWIR1 and SWIR2, play a significant role in the development of soil salinity inversion models. Additionally, the NIR band and its derived spectral index ARVI also demonstrate notable importance in the models. Overall, models constructed using original bands, spectral indicators, and transformed variables can provide better performance in soil salinity inversion and monitoring in the Yellow River Delta. Conceptualization, G.N. and Y.G. ; methodology, X.Z. ; software, G.N. ; validation, G.N. and Y.G. ; formal analysis, Y.G. and Y.L. ; investigation, Z.R. and M.J.; resources, Y.Y. ; data curation, X.Z. ; writing--original draft preparation, G.N. ; writing--review and editing, X.Z. ; visualization, Y.G. ; supervision, X.Z. ; project administration, X.Z. ; funding acquisition, X.Z. and X.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the Science & Technology Specific Projects in the Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta, Grant No. : 2022 SZX33. This project was also supported by the National Key Research and Development Program of China and Provincial student innovation plan project of Shandong, the Qingdao Agricultural Remote Sensing Application Engineering Research Center, Qingdao Agricultural University, Grant Nos. : 2021 YED1900902 and 2023 YED1902703. Not applicable. The data supporting the findings of this study are available at GitHub at the following link: [[https://github.com/woshiniguosheng/kenli_soil_salinity.git](https://github.com/woshiniguosheng/kenli_soil_salinity.git)]([https://github.com/woshiniguosheng/kenli_soil_salinity.git](https://github.com/woshiniguosheng/kenli_soil_salinity.git)) (accessed on 15 December 2024).