A dynamic normalized difference index for estimating soil organic matter concentration using visible and near-infrared spectroscopy. (March 2023) - Cao, Jianfei; Yang, Han
Highlights: A new dynamic normalized difference index (DNDI) was proposed and constructed to estimate SOM. The dynamic correction factor k was used to adjust the optimal wavelength range. Different spectral pre-processing methods were applied and compared. Correlation analysis were used to explore the robustness of DNDI. Abstract: Accurate, rapid, and non-destructive estimation of soil organic matter (SOM) is crucial for soil fertility diagnosis and precision farming. Due to the complicated and unstable spectral characteristics of SOM, few SOM spectral indexes have been proposed and widely used. In this paper, a new dynamic normalized difference index (DNDI) was proposed and constructed to estimate SOM using visible and near-infrared spectroscopy. A dynamic correction factor α was used to adjust the optimal wavelength range to obtain more robust features of SOM. Different spectral pre-processing methods were applied and compared. The support vector machine (SVM) model and Partial least square regression (PLSR) model were calibrated based on DNDI and applied to estimate SOM. To this end, a total of 111 soil samples were collected in the southern coastal plain of Laizhou Bay. The results showed that the DNDI index constructed by wavelength optimization could have a higher correlation with SOM than the two-dimensional normalized difference index (NDI). DNDI had the maximum correlation of 0.88 from the first derivative of reflectance, and the NDI correlations were most improved by standard normal variate transform (SNV), with the maximum correlation reaching 0.81. For SOM estimation models, DNDI exhibited better model performance, yielding a validation R 2, RMSE, and RPD of 0.78, 0.17 g·kg −1, and 2.01, respectively. Our algorithm has strong application potential for estimating other soil properties and enhancing the application of ground- and satellite-based sensing.