Developing novel spectral indices for precise estimation of soil pH and organic carbon with hyperspectral data and machine learning

Accurate soil pH and soil organic carbon (SOC) estimations are vital for sustainable agriculture, as pH affects nutrient availability, and SOC is crucial for soil health and fertility. Hyperspectral imaging provides a faster, non-destructive, and economical alternative to standard soil testing. The study utilizes imaging spectroscopic data from the Africa Soil Information Service (AfSIS) and Land Use and Coverage Area Frame Survey (LUCAS-2009) hyperspectral datasets, capturing spatially distributed spectral information. Machine learning (ML) approaches using high-dimensional spectral bands can be computationally expensive, while those using spectral indices are typically limited to multispectral data. This study addresses these challenges by comparing soil pH and SOC prediction using ML models, with both existing spectral indices and individual hyperspectral bands as input features. Results demonstrate that hyperspectral bands outperform existing indices in predictive accuracy, with R \(^{2}\) $_{}^2$ values ranging from 0.8 to 0.94 for both soil pH and SOC. To further enhance prediction performance, this study proposes novel spectral indices-soil pH index (SPI) and soil organic carbon index (SOCI)-specifically designed for hyperspectral data using principal component analysis (PCA) and artificial neural networks (ANN). The proposed SPI and SOCI indices address multicollinearity issues and high dimensionality in raw spectral bands, significantly improving predictive accuracy. The SPI and SOCI indices achieve R \(^{2}\) $_{}^2$ values of 0.86 for AfSIS soil pH, 0.945 for LUCAS-2009 soil pH, 0.952 for AfSIS SOC, and 0.963 for LUCAS-2009 SOC. These results show that the proposed spectral indices provide a practical solution for precision agriculture, enhancing soil pH and SOC estimations.