# The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Sample Collection

#### 2.3. Spectral Measurement and Chemical Analysis

^{®}3 portable spectro-radiometer (Analytical Spectral Devices Inc., Boulder, CO, USA) with a spectral range of 350–2500 nm. Spectral measurement was conducted in a dark room, and a halogen lamp was used as the source of light at an incidence angle of 45°. The fiber probe was installed 12 cm above the sample surface at a zenith angle of 90°. For each sample, approximately 300 g of soil was placed in a 20-cm-diameter dish with a thickness of about 10 mm. Scans were made 10 times and averaged. SOM was indirectly determined by the potassium dichromate volumetric method in accordance with the Chinese standard (specification of soil test, SL237-1999) [40].

#### 2.4. Spectral Pretreatment

**1**is a vector of ones.

#### 2.5. Model Calibration

#### 2.5.1. Sample Selection Method

#### 2.5.2. Inclusion of Pretreatment in Sample Selection

#### 2.5.3. PLSR Models

#### 2.6. Performance of Models

## 3. Results

#### 3.1. Descriptive Statistics of Soil Samples

^{−1}to 47.34 g kg

^{−1}(Table 1). The coefficient of variation (CV) was 0.39, which indicated that SOM was of medium variability (0.1 < CV < 1.0) [66]. The skewness was −0.19 and was close to zero, thereby implying that the number of samples with low and high SOM contents was similar. The kurtosis was −1.06, which meant that there were less samples around the mean SOM content than in a normal distribution, and the distribution was relatively flat.

#### 3.2. Soil Spectral Characteristics

#### 3.3. Accuracy of SOM Prediction after Including Pretreatment in Sample Selection

#### 3.4. Proportion of Pretreatment’s Positive or Negative Influence on Sample Selection

#### 3.5. Euclidean Distance between Samples after Pretreatment

## 4. Discussion

#### 4.1. Influence of Pretreatment on Sample Selection

#### 4.2. How Pretreatment Affects Sample Selection

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Maps that show the location of the sampled region, the positions of the sampling sites, and the landscapes, as indicated by a Landsat 7 enhanced thematic mapper plus (ETM+) scan line corrector off (SLC-off) image with a composition of bands 4 (red), 3 (green), and 2 (blue).

**Figure 2.**The spectral reflectance of soil samples (n = 106). The principal positions of spectral absorption by organics and water are highlighted.

**Figure 3.**The results of pretreatment’s (hollow square) influence on sample selection. None (black circle) denotes the result of sample selection without pretreatment. SG denotes Savitzky–Golay smoothing (

**a**). FD denotes first derivative (

**b**). MC denotes mean centering (

**c**). log(1/R) denotes logarithmic function (

**d**). MSC denotes multiplicative scatter correction (

**e**). SNV denotes standard normal variate (

**f**). RPD denotes residual predictive deviation.

**Figure 4.**The results of the proportion of calibration sets when pretreatment influenced sample selection positively (dark gray bar) or negatively (light gray bar) and the average $\Delta \mathrm{RPD}$ (black bar). SG denotes Savitzky–Golay smoothing. FD denotes first derivative. MC denotes mean centering. log(1/R) denotes logarithmic function. MSC denotes multiplicative scatter correction. SNV denotes standard normal variate.

**Figure 5.**A boxplot of the RPD of the partial least squares regression (PLSR) model after including pretreatment in sample selection. FD denotes first derivative. log(1/R) denotes logarithmic function. MSC denotes multiplicative scatter correction. SNV denotes standard normal variate.

**Figure 6.**The Euclidean distance among samples of raw spectra (

**a**) and the change in Euclidean distance after the spectra were pretreated by first derivative (FD) (

**b**), logarithmic function (log(1/R)) (

**c**), multiplicative scatter correction (MSC) (

**d**), and standard normal variate (SNV) (

**e**). All the calibration samples are sorted in ascending order according to SOM content and then numbered #1, #2, …, #85.

**Figure 7.**A subset of 14 samples selected based on raw and pretreated spectra. The gray ellipse shows the major difference in sample selection between raw and pretreated spectra.

Sample | Number | SOM (g kg^{−1}) | CV ^{3} | Skewness | Kurtosis | |||||
---|---|---|---|---|---|---|---|---|---|---|

Range ^{1} | Min | Max | Median | Mean | SD ^{2} | |||||

Total | 106 | 43.28 | 4.06 | 47.34 | 28.64 | 27.43 | 10.59 | 0.39 | −0.19 | −1.06 |

Calibration | 85 | 43.28 | 4.06 | 47.34 | 28.69 | 27.45 | 10.67 | 0.39 | −0.20 | −1.03 |

Validation | 21 | 35.75 | 8.37 | 44.12 | 28.59 | 27.36 | 10.54 | 0.39 | −0.17 | −1.20 |

^{1}Range denotes the difference between the maximum and minimum observations.

^{2}SD denotes standard deviation.

^{3}CV denotes coefficient of variation. SOM, soil organic matter.

**Table 2.**A comparison of different pretreatments in terms of residual predictive deviation (RPD) according to the analysis of variance (ANOVA) with a Games–Howell post-hoc test.

Type of Pretreatment | ||||||
---|---|---|---|---|---|---|

Variable | N | None | FD | log(1/R) | MSC | SNV |

RPD (Mean ± Std. Deviation) | 76 | 1.73 ± 0.33 | 1.81 ± 0.34 (p = 0.62) | 1.86 ± 0.21 * (p = 0.03) | 1.92 ± 0.32 * (p = 0.00) | 1.86 ± 0.31 (p = 0.08) |

**Table 3.**Cross-validation of applying pretreatments to multivariate regression analysis for estimating soil organic matter (SOM).

Pretreatment | ${\mathbf{R}}_{\mathbf{c}\mathbf{v}}^{\mathbf{2}}$ | $\mathbf{R}\mathbf{M}\mathbf{S}{\mathbf{E}}_{\mathbf{c}\mathbf{v}}\text{}\mathbf{\left(}\mathbf{g}\mathbf{\xb7}\mathbf{k}{\mathbf{g}}^{\mathbf{-}\mathbf{1}}\mathbf{\right)}$ | RPD | $\mathbf{\Delta}\mathbf{R}\mathbf{P}\mathbf{D}$ |
---|---|---|---|---|

None | 0.72 | 5.71 | 1.87 | - |

SG | 0.72 | 5.71 | 1.87 | −0.00 |

FD | 0.80 | 4.74 | 2.25 | 0.38 |

MC | 0.67 | 6.09 | 1.75 | −0.12 |

log(1/R) | 0.59 | 6.82 | 1.56 | −0.31 |

MSC | 0.66 | 6.20 | 1.72 | −0.15 |

SNV | 0.74 | 5.47 | 1.95 | 0.08 |

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## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Liu, Y.; Chen, Y.; Zhang, Y.; Shi, T.; Wang, J.; Hong, Y.; Fei, T.; Zhang, Y.
The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy. *Remote Sens.* **2019**, *11*, 450.
https://doi.org/10.3390/rs11040450

**AMA Style**

Liu Y, Liu Y, Chen Y, Zhang Y, Shi T, Wang J, Hong Y, Fei T, Zhang Y.
The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy. *Remote Sensing*. 2019; 11(4):450.
https://doi.org/10.3390/rs11040450

**Chicago/Turabian Style**

Liu, Yi, Yaolin Liu, Yiyun Chen, Yang Zhang, Tiezhu Shi, Junjie Wang, Yongsheng Hong, Teng Fei, and Yang Zhang.
2019. "The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy" *Remote Sensing* 11, no. 4: 450.
https://doi.org/10.3390/rs11040450