Enhancing the estimation of low-spectral-sensitivity soil properties: a case study of active organic carbon using vis-NIR hyperspectral data

Accurate estimation of soil properties with low spectral sensitivity remains a major challenge in hyperspectral modelling due to their weak spectral signals and high variability. Active organic carbon (AOC), a biologically and chemically reactive component of soil organic carbon, exemplifies such properties. This study investigates the potential of visible and near-infrared (Vis-NIR) hyperspectral data for AOC estimation by integrating advanced preprocessing techniques, feature extraction methods, and machine learning models. Hyperspectral data were collected from 292 soil samples in southern Sichuan Province, China. Six single preprocessing methods – Savitzky-Golay smoothing (SG), de-trend (DT), first derivative (D1), multiplicative scatter correction (MSC), standard normal variate (SNV), and max-min scaling (MMS) – and nine combined preprocessing approaches were applied to enhance spectral sensitivity. Feature band selection was performed using correlation analysis (CA) and principal component analysis (PCA), and estimation models were constructed using partial least square regression (PLSR), kernel ridge regression (KRR), and support vector machine (SVM). The results revealed that combined preprocessing methods, particularly D1-SNV and D1-MSC, substantially improved model performance, increasing R² by up to 47% and reducing RMSE by up to 34% compared to models using raw spectra. The SVM model with D1-SNV preprocessing achieved the highest accuracy (R² = 0.750, RMSE = 0.072 g kg⁻¹), while the KRR model with D1-MSC preprocessing produced an R² of 0.698. These findings demonstrate the efficacy of Vis-NIR hyperspectral data, combined with optimized preprocessing and feature selection, in accurately estimating low-spectral-sensitivity soil properties. The study provides a robust framework for hyperspectral modelling, offering valuable insights for soil quality monitoring and sustainable agricultural management.