Prediction of soil organic carbon in soil profiles based on visible–near-infrared hyperspectral imaging spectroscopy

Abstract

Complete and coherent soil profile information is important for studying soil morphological characteristics, vertical variation patterns of soil physicochemical properties, and identifying soil types. However, traditional soil survey methods use a limited number of discrete sample points at different depths as a data source to explore the correlation between soil depth and soil properties. Due to the variability of soil properties at vertical depth, there is a strong uncertainty in the conclusions drawn from the limited sample points. With the development of near-Earth remote sensing technology, the near-Earth hyperspectral imaging system can acquire hyperspectral images of soil profiles and characterize the fine texture differences and spectral variations of soil with continuous spectral curves and images, which is useful for inversion of soil properties. Soil organic carbon (SOC) is one of the important physicochemical properties to identify the genetic horizon, therefore, this paper explored the ability of visible–near-infrared (Vis-NIR) hyperspectral imaging spectroscopy to predict SOC in soil monoliths. The dataset contained five soil monoliths retrieved from the upper Yellow River, China. A random forest (RF) model was developed for predicting organic carbon, and the organic carbon content in the genetic horizon was used to establish a depth function to obtain the continuous content variation with the depth. These differences with and without depth function modeling, and the effect of three equal-spacing sampling schemes (1, 5, and 10 cm) on the depth function and the accuracy differences in five spectral statistics of the maximum (max), mean, median, mode, and minimum (min) within the image region of interest (ROI) were compared. The results indicated that (1) the depth function under the 1-cm sampling scheme predicted organic carbon the best, (2) the mode of the spectra within the ROI could yield a comparable or even a higher accuracy than that obtained with the mean, and (3) imaging spectroscopy could be used to visualize the distribution of organic carbon on soil profiles with relatively uniform morphology. These results indicated that imaging spectroscopy could be used for predicting organic carbon in undisturbed soil profiles.