Abstract
In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.
Highlights
Forests and soil are important terrestrial ecosystems, they have the role of regulating climate, improving soil quality, protecting biodiversity, and mitigating geologic hazards [1,2,3,4]
Zhu et al [18] built synergy interval partial least square (SiPLS), support vector machine (SVM), and random forest (RF) models for soil samples based on six spectral preprocessing methods, and the results showed that suitable preprocessing methods can improve model performance by eliminating noise and extracting effective information
Using SiPLS or SiPLS coupled with uninformative variable elimination (UVE), the feature variables preferred by Directly using SiPLS or SiPLS coupled with UVE, the feature variables preferred by both algorithms were input into the soil organic matter (SOM) content prediction model, and the validation set both algorithms were input into the SOM content prediction model, and the validation was input to evaluate the model
Summary
Forests and soil are important terrestrial ecosystems, they have the role of regulating climate, improving soil quality, protecting biodiversity, and mitigating geologic hazards [1,2,3,4]. Soil plays an important role in the material cycle, energy flow, and information transfer in forest ecosystems, providing the necessary nutrients required for plant growth [5,6,7]. The content of soil organic matter (SOM) is an important indicator of soil nutrients, which plays an important role in improving soil quality, increasing soil productivity, and enhancing soil erosion resistance [12]. The application of visible near infrared reflectance spectroscopy (VIS-NIRS) to characterize the physical and chemical information of soil samples has become a popular method to predict soil properties and components. While traditional laboratory methods for SOM determination are time-consuming, labor-intensive, and limited, VisNIRS is simple, efficient, environmentally friendly, and non-destructive, with the rapid and accurate determination of organic matter content in a large number of samples at a regional scale
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
