干旱区研究

Arid Zone Research >

2024 , Vol. 41 >Issue 5: 894 - 904

DOI: https://doi.org/10.13866/j.azr.2024.05.16

Agricultural Ecology

Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging

  • HONG Guojun ,
  • XIE Junbo ,
  • ZHANG Ling ,
  • FAN Zhenqi ,
  • YU Caili ,
  • FU Xianbing ,
  • LI Xu
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  • 1. Jiangxi University of Science and Technology Jiangxi Provincial Institute of Regional Development, Nanchang 330200, Jiangxi, China
    2. School of Information Engineering, Tarim University, Alar 843300, Xinjiang, China
    3. Key Laboratory of Tarim Oasis Agricultural Ministry of Education, Tarim University, Alar 843300, Xinjiang, China
    4. College of Water Resources and Architectural Engineering, Tarim University, Alar 843300, Xinjiang, China
    5. College of Ocean of Shanwei Institute of Technology, Shanwei 516600, Guangdong, China

Received date: 2023-10-26

  Revised date: 2023-12-27

  Online published: 2024-05-29

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Abstract

Given the difficulties in the field measurement of soil salinization in Xinjiang and the difficulty in quickly and broadly evaluating the potential hazards of soil salinization, this study considers cotton fields in the Aral Reclamation Area of Xinjiang as the research object, and uses multispectral remote sensing image data from Sentinel-2 SR and Landsat-9 OLI to construct a high-dimensional data set by comprehensively integrating 20 spectral indices and combining spectral indices. The optimal feature subset is screened using the method of exhaustive feature combination and cross-validation, and the inversion accuracy of soil salinization is compared for four machine learning algorithms (i.e., XGBoost, random forest, deep neural network, and K-nearest neighbor) under different feature combinations. Simultaneously, the difference in accuracy between Sentinel-2 SR and Landsat-9 OLI remote sensing images in soil salinization inversion is analyzed. The results show that: (1) The model constructed based on XGBoost algorithm can achieve high-precision prediction of cotton field salinization, with R2 higher than 0.74, MSE lower than 0.04, and MAPE lower than 0.13. (2) Under the condition of feature combination 1, Sentinel-2 SR (S3+GBNDVI) and Landsat-9 OLI (SI+NDVI) remote sensing images achieved the highest prediction accuracy using XGBoost algorithm. (3) Sentinel-2 SR image data in cotton field salinization prediction (R2=0.73-0.88) is better than that of Landsat-9 OLI image data. This study realizes the precise monitoring of soil salinization in cotton fields in the Aral Reclamation Area of Xinjiang, which should provide an effective and timely technical reference for soil salinization control and prevention in cotton fields in reclamation areas.

Key words： multispectral remote sensing images; spectral indices; soil salinization; machine learning algorithms; Aral Reclamation Area

Cite this article

HONG Guojun , XIE Junbo , ZHANG Ling , FAN Zhenqi , YU Caili , FU Xianbing , LI Xu . Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging[J]. Arid Zone Research, 2024 , 41(5) : 894 -904 . DOI: 10.13866/j.azr.2024.05.16

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