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Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil

Author

Listed:
  • Qing Zhong

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China)

  • Mamattursun Eziz

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
    Xinjiang Laboratory of Arid Zone Lake Environment and Resources, Xinjiang Normal University, Urumqi 830054, China)

  • Rukeya Sawut

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
    Xinjiang Laboratory of Arid Zone Lake Environment and Resources, Xinjiang Normal University, Urumqi 830054, China)

  • Mireguli Ainiwaer

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
    Xinjiang Laboratory of Arid Zone Lake Environment and Resources, Xinjiang Normal University, Urumqi 830054, China)

  • Haoran Li

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Liling Wang

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China)

Abstract

Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R 2 = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil.

Suggested Citation

  • Qing Zhong & Mamattursun Eziz & Rukeya Sawut & Mireguli Ainiwaer & Haoran Li & Liling Wang, 2023. "Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13948-:d:1243682
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    References listed on IDEAS

    as
    1. Yun Xue & Bin Zou & Yimin Wen & Yulong Tu & Liwei Xiong, 2020. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra," Sustainability, MDPI, vol. 12(11), pages 1-16, May.
    2. Lei Han & Rui Chen & Huili Zhu & Yonghua Zhao & Zhao Liu & Hong Huo, 2020. "Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    3. Nazupar Sidikjan & Mamattursun Eziz & Xinguo Li & Yonghui Wang, 2022. "Spatial Distribution, Contamination Levels, and Health Risks of Trace Elements in Topsoil along an Urbanization Gradient in the City of Urumqi, China," Sustainability, MDPI, vol. 14(19), pages 1-18, October.
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    Cited by:

    1. Chang Meng & Mei Hong & Yuncai Hu & Fei Li, 2024. "Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas," Sustainability, MDPI, vol. 16(10), pages 1-23, May.
    2. Xayida Subi & Mamattursun Eziz & Ning Wang, 2024. "Improving the Estimation Accuracy of Soil Organic Matter Content Based on the Spectral Reflectance from Soils with Different Grain Sizes," Land, MDPI, vol. 13(7), pages 1-16, July.

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