Discriminant analysis using feature extraction from spectral domain responses to achieve accurate delineation for robust evaluation or classification of soil properties

Soil spectroscopy offers a method for quantitatively analysing soil chromophores and employing a screening approach. However, utilizing general models with this data at once doesn't consistently yield accurate results in determining soil parameters. Ambiguities in response within certain domains of the reflectance spectrum are observed. To address this issue, a method is proposed where quantities associated with spectral response are outputted within well-defined domains corresponding to specific parameters. This article introduces a novel approach centred on identifying features from reflectance spectra to assess soil properties and mitigate issues arising from a heterogeneous soil dataset. The focus is on swiftly automating the classification of soil spectral data by extracting features directly related to the varied composition of chromophores in the samples. These features are categorized for each chromophore parameter and analysed using linear discriminant analysis (LDA). The study employs the '309 soil samples' from the GEO-CRADLE open spectral soil library in Greece. Specifically, relevant data from six spectral responses are extracted to separate and quantify certain soil parameters such as clay content, NO3- levels, and SOM (Soil Organic Matter), despite their interrelations. We developed a method to visually represent the determination outcomes quantitatively by establishing three threshold parameter values: 1.6% for SOM, 19% for clay, and 13.6 ppm for NO3-. These determinations will span the explored ranges of '0.3 to 4.18%' for SOM, "3 to 48%" for clay, and '0 to 661.2 ppm' for NO3-. Utilizing supervised LDA proves notable classification rates of 93.3% for the SOM task, 90% for the Clay task, and 86.6% for NO3- with the specific cut-off value parameter. The final result is presented as a map showing the positions of various prediction samples, highlighting the percentages of clay and organic matter relative to 19% and 1.6%, respectively, as well as the nitrate concentration in these samples relative to 13.6 ppm.