Abstract
Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.
Highlights
Soil is a part of the natural environment and one of the most valuable natural resources
One of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands
This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients
Summary
Soil is a part of the natural environment and one of the most valuable natural resources. To conduct a predictive soil properties model, it is more efficient to rely on learning-based methods such as the linear regression approach. An overview of these approaches and how they provide optimal results under certain circumstances is given in [7] [8]. One of the problems, encountered in the case of hyperspectral data, is related to information redundancy between different spectral bands. This redundancy creates multi-collinearity in the explanatory variables and makes the regression coefficients unstable and difficult to interpret. The challenge in this study is to exploit all the spectral richness of the data by providing complementary rather than redundant information
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