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

Chuanmei Zhu,Hongwei Wang,Jingzhe Wang,Shengtian Yang,Zipeng Zhang

doi:10.3390/s20061795

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.

Highlights

The mining and processing of mineral resources have shown no shortage of economic benefits.coal mining will disturb the soil layer, destroy vegetation and cause the soil to lose its Sensors 2020, 20, 1795; doi:10.3390/s20061795 www.mdpi.com/journal/sensorsSensors 2020, 20, 1795 utilization value [1]
The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the principal component analysis (PCA) variables (1DV) regardless of whether linear or random forests (RF) models were used
Hong et al [27] have reported that PCA results in a poorer prediction performance than the optimal band combination algorithm, because the physical meanings of the principal components (PCs) obtained by PCA are generally not as clear as those of the original spectral variables, thereby worsening the prediction performance

Summary

Introduction

The mining and processing of mineral resources have shown no shortage of economic benefits.coal mining will disturb the soil layer, destroy vegetation and cause the soil to lose its Sensors 2020, 20, 1795; doi:10.3390/s20061795 www.mdpi.com/journal/sensorsSensors 2020, 20, 1795 utilization value [1]. The mining and processing of mineral resources have shown no shortage of economic benefits. Coal mining will disturb the soil layer, destroy vegetation and cause the soil to lose its Sensors 2020, 20, 1795; doi:10.3390/s20061795 www.mdpi.com/journal/sensors. Sensors 2020, 20, 1795 utilization value [1]. These issues have posed a serious threat to the sustainable development of land resources and the ecological environment [2]. The area of land damaged by coal mining every year at the global scale is estimated to exceed 12.5 hm2 [3,4]. In China, large open-pit coal mines are mainly concentrated in ecologically fragile zones under drought and semi-drought [5]. The self-repairing ability of the soil in this region is relatively poor, and the ecological sensitivity is relatively strong [6]

Results

Discussion

Conclusion