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Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

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Abstract

Aims

Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem.

Methods

This study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models.

Results

The best accuracy corresponding to an R2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity.

Conclusion

The radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas.

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Acknowledgments

Authors are grateful for the financial support provided by the National Natural Science Foundation of China, and Third Xinjiang Comprehensive Scientific Expedition (No. 42061065, No. 41561089, and No. 2022xjkk03010102) .

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Authors and Affiliations

  1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, People’s Republic of China

    Sentian Xiao, Ilyas Nurmemet & Jing Zhao

  2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, People’s Republic of China

    Sentian Xiao, Ilyas Nurmemet & Jing Zhao

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  1. Sentian Xiao
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Correspondence to Ilyas Nurmemet.

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Xiao, S., Nurmemet, I. & Zhao, J. Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China. Plant Soil 498, 451–469 (2024). https://doi.org/10.1007/s11104-023-06446-0

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