Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra

Zhang, Zipeng; Ding, Jianli; Zhu, Chuanmei; Wang, Jingzhe

doi:10.1016/j.saa.2020.118553

Cited by 60 publications

(28 citation statements)

References 52 publications

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“…The appearance of imaging spectroscopy in the 1980s brought optical remote sensing into a new stage of hyperspectral remote sensing [11]. Due to its rapidity, reduced labor intensity, cost-effectiveness, and non-destruction compared with conventional chemistry experiments [12,13], since the 1990s, the application of visible-near-infrared (Vis-NIR) (350-2500 nm) spectroscopy in soil science and agricultural management has attracted increasing attention, and the number of papers published in related fields has grown rapidly (e.g., [14]). The variation of the spectral curve is caused by the difference in the absorption and reflection characteristics of electromagnetic waves for different material components, and little sample preparation is required [12].…”

Section: Introductionmentioning

confidence: 99%

“…Multiplicative scatter correction (MSC) [35] and the standard normal variate (SNV) [28] can effectively reduce the influence of spectral differences caused by different scattering intensities. Derivative transformations represented by the first derivative (FD), second derivative (SD) [13], and logarithmic transformation (LG) [24] have been used to remove the baseline while improving the correlation to the sample concentrations and the linear trend [5]. A continuous wavelet transformation (WT) [36] and a Fourier transform (FT) [37], belonging to the high-frequency noise removal method, have been used to enhance the features in the spectrum [5].…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the heterogeneity of the geographical environment, no single (or combination of) preprocessing method is suitable for different geographic soilscapes, and the same is true for wavelength variable selection and modeling methods [13,59]. Additionally, the reflectance of the soil spectrum in Vis-NIR (350-2500 nm) is generally low (compared with any other typical object), and the extension of the inversion model based on measured spectra is limited.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

Guo

Gao

et al. 2021

Remote Sensing

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Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

Guo

Gao

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

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“…Zhang et al. (2020) employed nine pre‐processing methods, for example, Savitzky‐Golay smoothing (SG), first derivatives (FD), multiplicative scatter correction (MSC), standard normal variate (SNV) and detrend, and continuum removal (CR) to transform soil spectra. Only SG produced the positive effect on SOM model in saline soil.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Chao

Qiao

et al. 2021

Agronomy Journal

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Visible and near-infrared reflectance (Vis-NIR) spectroscopy is considered a promising tool for the estimation of soil properties. Soil clay content and soil organic matter (SOM) are main components affecting soil spectra. Accurate assessment of clay content and SOM is essential before achieving accurate prediction for other soil properties. Selecting the proper spectral transformation technique and optimal calibration method are important processes to improve model performance. In this study, a total of 240 soil samples were collected from the main area of winter wheat (Triticum aestivum L.) fields in the Southwest region of Shanxi province, northern China. Six spectral pre-treatments and three multivariate methods were utilized to realize the estimation of clay content and SOM. Finally, the important spectral wavelengths were identified

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“…The Savitzky-Golay (S-G) smoothing method can reduce the high-frequency noise of spectral data resulting from instrument vibration or electromagnetic interference [19]. However, the main disadvantage of the S-G method is that the smoothing window size is not fixed, which requires complex optimization according to specific spectral data to select the optimal window size [20,21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

et al. 2020

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The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.

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Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra

Cited by 60 publications

References 52 publications

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

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