Exploration of the spatial non-stationary relationship between soil texture and spectra from a multiscale perspective

doi:10.14088/j.cnki.issn0439-8114.2021.10.008

Abstract

Abstract: A total of 210 samples collected from the Qilu lake basin were used. The performances of partial least squares regression （PLSR）, geographically weighted regression （GWR） and multi-scale geographically weighted regression （MGWR） in measuring soil texture were evaluated, and the spatial heterogeneity of the relationship between soil texture and spectra under different scales were explored. The results showed that the R², mean absolute error and root mean square error of MGWR were better than those of GWR and PLSR, which indicated that MGWR outperformed other models. This was because MGWR can better fit the relationship between soil texture （clay, silt and sand contents） and spectra by considering different scales of different variables coefficients. MGWR results revealed that the spatial scale of spatial response of different spectral latent variables to clay, silt and sand content was different. Specifically, the spectral latent variable LV1 had the largest response to soil texture. Its standardized coefficients of clay, silt and sand contents were 0.373, 0.426 and 0.422, respectively.

Key words: soil texture, hyperspectral data, multiscale geographically weighted regression, spatial non-stationary relationship

CLC Number:

S152.3

ZHENG Yu-zhen, CHEN Yi-yun, CHEN Min, WU Zi-hao, JIANG Jiang-jun-nan. Exploration of the spatial non-stationary relationship between soil texture and spectra from a multiscale perspective[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(10): 38-45.

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