Abstract
Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R’; and first derivative of reciprocal, (1/R)’. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent “overfitting”. (2) Of the 54 pretreatments investigated, WPD-(1/R)’-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study).
Highlights
Soil organic matter (SOM) is an important indicator of soil fertility [1]
The aim of this study was as follows—(i) to estimate the practicability of using visible, near infrared (NIR), and MIR spectroscopy for assessing the SOM content; (ii) to elucidate the correlation between the SOM content and the different processing data of soil spectra; (iii) to predict the SOM content based on spectral data subjected to different pretreatments, compare the SOM estimation results for the different spectral data pretreatments, and select the most effective preprocessing method for predicting the SOM content based on the PLS regression (PLSR) approach; and (iv) to explore whether denoising can reduce correlation between adjacent bands
(1) After the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ pretreatments, the bands with stronger correlations with the SOM content became more dispersed; this was beneficial to the subsequent dimensionality reduction operation, as it effectively reduced the correlation between the adjacent bands and prevented “overfitting.” (2) In the Yitong area of Jilin Province, the WPD-(1/R)’-PCADR pretreatment of the 54 different pretreatments investigated in this study yielded the model with the highest accuracy for estimating the SOM content
Summary
Soil organic matter (SOM) is an important indicator of soil fertility [1]. It is rich in a variety of organic acids and humic acids, which have a certain ability to dissolve soil mineral and can promote the absorption of nutrients. Kawamura et al suggested that the soil oxalate-extractable P content can be predicted using visible-near infrared (NIR) spectroscopy [4]. Researchers have used some types of data preprocessing methods as they analyzed soil spectral information. Liu et al applied several spectral data pretreatments during sample selection to construct models for predicting the SOM content using visible and NIR spectroscopy [10]. Vohland et al used different methods to select the spectral variables for improving model accuracy and assessing the indicators of arable soil quality [12]. For the sake of improving the accuracy of the models, it is very important to select the appropriate preprocessing methods (for the hyperspectral data) before modeling, such as denoising, dimensionality reduction, and data form transformation. The combinations of these single pretreatment methods were compared and used for spectrum data processing
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