Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

Lin, Zhidan; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, Changhua; Zhang, Z. Y.; Song, Lexin; Liu, Y.

doi:10.1007/s10812-017-0505-4

Cited by 31 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vis-NIRS is a green, eco-friendly, simple, and effective qualitative and quantitative spectroscopic analytical technology [17]. Coupled with suitable chemometric methods, NIR has been successfully applied in many fields such as the petrochemical [18,19], agricultural [20][21][22], food [17,23,24], pharmaceutical [25], and traditional Chinese medicine industries [26]. In recent years, Vis-NIR has also shown great potential in forestry applications [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…The correlation coefficient of the calibration set was 0.9648, the corrected root mean square error was 0.0027, and the correlation coefficient of the verification set was 0.9432. Other models with good predictive performance use different spectral pretreatment methods, such as multiplicative scatter correction, Standard Normal Variate, and second derivative [21,[46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

View full text Add to dashboard Cite

This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

View full text Add to dashboard Cite

show abstract

“…However, for practical applications, the spectral information overlaps severely. Selecting the feature wavelengths of SOM is the key to improving the predictive capability of model [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Wavelength Selection for Estimating Soil Organic Matter Contents Through the Radiative Transfer Model

Yuan

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the "training data". This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-meansquare error of prediction (RMSEP) < 0.234%, coefficient of determination (R 2)> 82.9%) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R 2 : 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.

show abstract

“…Considering feature extraction, dimensionality reduction (e.g., principal component analysis (PCA)), waveband selection (e.g., successive projections algorithm (SPA)), and vegetation index (VI) extraction are the three commonly-used strategies in relating sensitive spectral features to the information of plant species [34][35][36]. Moreover, different sample subset partition methods (e.g., stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) may cause different classification results [37,38]. However, very few studies have investigated the combination of feature extraction and sample subset partition in the classification of mangrove species.…”

Section: Introductionmentioning

confidence: 99%

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Wang

Xia

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

show abstract

Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

Cited by 31 publications

References 17 publications

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Wavelength Selection for Estimating Soil Organic Matter Contents Through the Radiative Transfer Model

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Contact Info

Product

Resources

About