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Chinese Journal of Ecology ›› 2023, Vol. 42 ›› Issue (9): 2286-2295.doi: 10.13292/j.1000-4890.202309.006

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Sensitivity analysis and quantitative inversion of multi-source remote sensing to soil salt content in dry and wet seasons in Ningxia.

WANG Yijing1, JIA Pingping2, CHEN Ruihua1, ZHANG Junhua3,4,5*   

  1. (1College of Geographical Sciences and Planning, Ningxia University, Yinchuan 750021, China; 2College of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; 3College of Ecology and Environmental Science, Ningxia University, Yinchuan 750021, China; 4Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, Yinchuan 750021, China; 5Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China).

  • Online:2023-09-10 Published:2023-09-05

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Abstract: Rapid and accurate inversion of soil salinity in arid areas is the premise to effectively prevent the expansion of salinization. To address sensitivity analysis and quantitative inversion of soil salt content in dry and wet seasons by means of ground hyperspectra and Landsat 8 OLI images, we obtained data of topsoil (0-20 cm) salt content, ground hyperspectra and Landsat 8 OLI image in the dry season (April) and wet season (October) in Pingluo County, Ningxia. Linear and nonlinear functions were used to test the sensitivity of the spectral data and corresponding salinity index. We established the models to estimate soil salt content based on ground hyperspectral and image data using partial least squares regression (PLSR), support vector machine (SVM), and back propagation neural network model (BPNN). The average soil salt content in dry and wet seasons was 6.17 and 4.28 g·kg-1, respectively, indicating serious soil salinization. The sensitive bands and salinity index of ground hyperspectra and image spectra to soil salinity differed among seasons. The resampling band and salinity index of ground hyperspectra showed extremely high sensitivity to soil salinity. The stability and prediction ability of BPNN estimation model of soil salt content were better than those of PLSR and SVM. Hyperspectral-BPNN in dry and wet seasons was the best estimation model, with the prediction accuracy of 0.739 and 0.819, and the relative analytical errors of 1.49 and 1.95, respectively. After calibrated by the resampled measured spectrum model, the estimation accuracy of the image spectra based model increased from 0.685 to 0.844 in the dry season and from 0.654 to 0.788 in the wet season, which effectively enhanced the accuracy in estimating soil salt content at large scale. We successfully made the spatial transformation of soil salt content from small to large scale. The results provided a scientific reference for identification and prevention of soil salinization in Yinbei of Ningxia.


Key words: salinization, salinity index, ground hyperspectrum, Landsat 8 OLI image, model.