Abstract:
Objective The objective of this study is to investigate the influence mechanism of soil moisture content on hyperspectral inversion of purple soil organic matter content, and establish the hyperspectral estimation model of purple soil organic matter.
Method Purple soil in sloping farmland was taken as the research object. On the basis of indoor allocation of soil moisture content, spectral reflectance and organic matter contents were used as data sources. The original spectral reflectance was transformed by continuum removal, first-order differential and second-order differential. Correlation analysis and two-factor analysis of variance were used to screen the sensitive bands of soil organic matter and soil moisture content. Partial least squares regression and back propagation neural network models were constructed, followed by establishing partial least squares regression and backpropagation neural network models.
Result ① Moisture had a significant influence on spectral reflectance when the soil water content ranged from 4% to 18%, but this influence decreased when water content was between 18% and 28%. The spectral reflectance of soil did not exhibit a significant change with the increase in organic matter content, and reached its peak when the soil organic matter content was 1.937%. ② Soil moisture exhibited higher correlation with spectral reflectance compared to organic matter content, with the original spectral reflectance showing the highest correlation (|r| = 0.96). ③ A neural network model considering interaction achieved optimal accuracy and stability at a soil water content of 23%. The coefficient of determination for both modeling set and validation set was 0.97, with root mean square errors of 1.34 g kg−1 and 1.46 g kg−1 respectively.
Conclusion Considering interaction between soil moisture and organic matter, the inversion of soil organic matter content by neural network model can effectively improve the simulation accuracy. This study would provide theoretical reference for remote sensing monitoring of organic matter in purple soil.