Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Abstract

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

“…Pretreatment removes extraneous interference and improves the performance of estimation models. Common spectral pre-processing techniques include mathematical transformation [8][9][10][11][12], Savitzky-Golay (SG) [11,[13][14][15], continuum removal (CR) [15][16][17], multiplicative scatter correction (MSC) [11,17], and standard normal variate (SNV) [11,17]. Spectral band selection aims to select the optimal variables from the raw spectra, in order to enhance the spectral sensitivity of soil properties.…”

“…Spectral band selection aims to select the optimal variables from the raw spectra, in order to enhance the spectral sensitivity of soil properties. Several Machine learning (ML) methods have been proposed for spectral band selection, such as competitive adaptive reweighting sampling (CARS) [8,[16][17][18], principal component analysis (PCA) [14,15,19,20], locally linear embedding (LLE) [14], multidimensional scaling (MDS) [14], metaheuristic algorithms [21], and rough set algorithms [22]. In addition, partial least squares regression (PLSR) [13,20,23], artificial neural networks [4], random forest (RF) [2,17,24], and support vector machine (SVM) [24,25] are common methods of soil spectral modeling.…”

Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

“…As a new research trend in machine learning, ensemble learning [ 14 ] integrates the results of multiple learning methods to improve the generalization ability and prediction accuracy of the original method. Research using estimation models represented by random forest (RF) [ 15 ] is also increasing. Li [ 16 ] used random forests to predict the sugar content of different types of fruits (e.g., apples and pears) and compared the prediction effects of PLS.…”

Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through near‐infrared spectroscopy and chemometrics

“…OH, SO 4 and CO 3 groups). [27][28][29] The average spectral reflectance of the different classes of soil organic matter (SOM) was assessed by Zhu et al 30 using visiblenear-infrared (Vis-NIR). The authors evaluated 168 samples of soils with pasture vegetation cover, cultivable soils with fertilization and soils without vegetation cover.…”