Abstract
Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg−1) and RMSEP (2.70 g kg−1) and highest Rc2 (0.92), Rp2 (0.91), and RPD (3.42 > 3.0). This model had excellent predictive performance of estimating SOM content in the study area.
Highlights
Soil organic matter content (SOM) is an important index to measure the level of soil function and soil quality, and detection of SOM content is an important approach to understand the local soil fertility [1, 2]
Compared with the range of SOM content (0.68–78.39 g kg−1) for both the whole dataset and the calibration set, the validation set had a narrower range with 4.79–39.16 g kg−1, because of the deficient soil samples
The average SOM content and coefficient of variation of whole set were 21.43 g kg−1 and 50.46% between the range of the values of calibration and validation set, respectively, while the descriptive statistical characteristics of SOM content in the calibration and validation set were similar to the six parameters of the whole set
Summary
Soil organic matter content (SOM) is an important index to measure the level of soil function and soil quality, and detection of SOM content is an important approach to understand the local soil fertility [1, 2]. Low-cost, large-scale, nondestruction, and rapid data acquisition, remote sensing technology has been proved to be a promising tool to strengthen or perfect traditional methods [7, 8] It provides a fresh approach for quantitative research of SOM content. Due to the lofty high spectral resolution, convenience, and controllability, the analysis on the laboratory Vis-NIR spectroscopy of soil is fashionable, especially. It is precisely the significant quantitative relationship between SOM and SOC; the estimation of SOM content by remote sensing has been proved as a feasible approach to grasp the condition of local SOC storage.
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