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Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data

Author

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  • Jing Zhao

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China)

  • Ilyas Nurmemet

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830017, China)

  • Nuerbiye Muhetaer

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China)

  • Sentian Xiao

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China)

  • Adilai Abulaiti

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China)

Abstract

Soil salinization is one of the major problems affecting arid regions, restricting the sustainable development of agriculture and ecological protection in the Keriya Oasis in Xinjiang, China. This study aims to capture the distribution of soil salinity with polarimetric parameters and various classification methods based on the Advanced Land Observing Satellite-2(ALOS-2) with the Phased Array Type L-Band Synthetic Aperture Radar-2 (PALSAR-2) and Landsat-8 OLI (OLI) images of the Keriya Oasis. Eleven polarization target decomposition methods were employed to extract the polarimetric scattering features. Furthermore, the features with the highest signal-to-noise ratio value were used and combined with the OLI optimal components to form a comprehensive dataset named OLI + PALSAR2. Next, two machine learning algorithms, Support Vector Machine (SVM) and Random Forest, were applied to classify the surface characteristics. The results showed that better outcomes were achieved with the SVM classifier for OLI + PALSAR2 data, with the overall accuracy, Kappa coefficient, and F1 scores being 91.57%, 0.89, and 0.94, respectively. The results indicate the potential of using PALSAR-2 data coupled with the classification in machine learning to monitor different degrees of soil salinity in the Keriya Oasis.

Suggested Citation

  • Jing Zhao & Ilyas Nurmemet & Nuerbiye Muhetaer & Sentian Xiao & Adilai Abulaiti, 2023. "Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7452-:d:1137758
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    References listed on IDEAS

    as
    1. Jumeniyaz Seydehmet & Guang Hui Lv & Ilyas Nurmemet & Tayierjiang Aishan & Abdulla Abliz & Mamat Sawut & Abdugheni Abliz & Mamattursun Eziz, 2018. "Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China," Sustainability, MDPI, vol. 10(3), pages 1-22, February.
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