It is difficult to analyse soil properties quantitatively with multispectral remote sensing data. An alternative solution is to determine the main spectral characteristic control points of soil hyperspectral reflectance curves by sensitivity analysis methods. Hyperspectral reflectance is simulated using the control points based on multispectral reflectance collected from satellites in this study. The laboratory hyperspectral reflectance and its continuum-removed curve of Phaeozem and the parent material (PM) of samples collected from Heilongjiang Province, China were analysed, and the spectral characteristic control points determined. Hyperspectral simulating linear and quadratic models based on laboratory reflectance were then built. Results show that montmorillonite and illite are the dominant minerals in Phaeozem PM. Organic matter content determines the spectral characteristics of Phaeozem and makes it suitable for reflectance simulation in the spectrum range of 1000 nm and less; the higher the organic matter content the greater the spectral absorption area. There are two absorption valleys at 500 and 660 nm, which determine the spectral characteristic control points of Phaeozem between 450 and 930 nm, namely 450, 500, 590, 660 and 930 nm. Both the linear and quadratic simulation models built with the characteristic control points accurately describe Phaeozem reflectance, which proves that the characteristic control points are selected reasonably and representatively. The hyperspectral simulation method based on multispectral reflectance closely represents the characteristics of Phaeozem hyperspectral reflectance, partly removes noise and improves the precision of predicting organic matter content. Therefore the method is feasible and useful for data compression of Phaeozem hyperspectral reflectance, soil and vegetation indices building, and quantitative remote sensing in the Phaeozem Zone, northeast China.
Liu, H., Zhang, X., Yu, W., Zhang, B., Blackwell, J., & Song, K. (2011). Simulating models for Phaeozem hyperspectral reflectance. International Joural of Remote Sensing, 32(13), 3819-3834. https://doi.org/10.1080/01431161003778708