Soil Organic Matter Estimation Modeling Using Fractal Feature of Soil for vis-NIR Hyperspectral Imaging

Abstract

To produce a fast, accurate estimation for soil organic matter (SOM) by soil hyperspectral methods, we developed a novel intelligent inversion model based on multiscale fractal features combined with principal component analysis (PCA) of hyperspectral data. First, we calculated the local generalized Hurst exponent of the spectral reflectivity by multiscale multifractal detrended fluctuation analysis (MMA) while determining the sensitive spectral bands. PCA was employed to access the maximum principal component features of the sensitive bands used as the model input. Finally, two intelligent algorithms, random forest (RF), and a support vector machine (SVM), were utilized for establishing the SOM estimation model. The soil hyperspectral data possesses the typical nature of long-range correlation, presenting distinct fractal structures at different scales and fluctuations. The sensitive bands were from 359 nm to 405 nm, and were not impacted by window fitting size. The accuracy of the models of MMA-based sensitive bands is superior to that of the original bands. The PCA processing brings additional model performance improvement. The MMA-based models combined with RF is recommended for SOM estimation.