# Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

Research Field | Sensors | Factor Monitored | Application | Reference |
---|---|---|---|---|

Water | Ocean Optics USB4000 | Chlorophyll a | Estimation of chlorophyll-a in turbid inland waters | [33] |

ASD | Fucoxanthin, zeaxanthin, chlorophyll a and chlorophyll b | Quantification of diatom biomass in Microphytobenthic (MPB) biofilms (non-destructively) | [34] | |

ASD, ATM-2 | Grain size | Characterization and management of the beach environment | [35] | |

Plants | Airborne HyMap | Foliar nitrogen | prediction of sagebrush canopy nitrogen from an airborne platform | [36] |

Perkin Elmer Lamdba 19 | Leaf pigment, Chlorophyll, Carotenoid, Nitrogen, Carbon | Spectroscopy of plant biochemistry | [37] | |

ASD | Leaf chlorophyll | Retrieval of spatially-continuous leaf chlorophyll content | [38] | |

ASD | Major plant species | Classification of Hyperspectral images | [39] | |

ASD | Fusarium circinatum Stress | Early detection of Fusarium circinatum-induced stress in Pinus radiata seedlings. | [40] | |

ProSpecTIR-VS, ASD | Plant stress | The Plant Stress Detection Index (PSDI) used as plant stress indicator | [41] | |

ASD | Mangrove leaves | Mangrove classification | [42] | |

ASD | Water stress | Prediction of Grain and biomass yield of wheat based on water stress indices | [43] | |

ASD, Ocean Optics (QE65000, Jaz) | pH | Determination of pH in Sala mango | [44] | |

ASD | Zn content | Monitoring Zn nutrient levels under field conditions | [45] | |

ASD | Leaf chlorophyll | Validation of satellites’ vegetation products | [46] | |

Soils | ASD | Soil nitrogen, carbon, carbonate, and organic matter | Assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons (non-destructively). | [6] |

ALPHA FT-IR | Soil carbon | Soil carbon validation at large scale | [13] | |

HySpex VNIR-1600 | Soil carbon, nitrogen, aluminum, iron and manganese | Improvement of soil classification, assessment of elemental budgets and balances and understanding of soil forming processes and mechanisms. | [14] | |

ASD | Soil bulk density, moisture content, clay, silt, and sand | Estimating the physical properties of paddy soil | [47] |

## 2. Materials and Methods

**Figure 1.**Schema showing an overview of the inputs and analysis steps of the work reported in this paper to produce the LCMCS prediction models.

#### 2.1. Experiment

#### 2.1.1. Sample Preparation

City | Soil Types |
---|---|

Changzhou | Fluvo-aquic soil, Salinized fluvo-aquic soil |

Renqiu | Fluvo-aquic soil, Salinized fluvo-aquic soil |

Fengfeng | Cinnamon soil |

**Figure 2.**(

**a**) Vicinity map of Hebei Province, China; (

**b**) Vicinity map of the Changzhou, Fengfeng, and Renqiu study sites within Hebei; Soil sample collection sites from subsided land (red regions) of Changzhou (

**c**); Renqiu (

**d**) and Fengfeng (

**e**).

#### 2.1.2. Measurement and Data Processing

#### 2.1.3. Spectral Transformations

#### 2.1.4. Retrieval Model

#### 2.2. Methods

#### 2.2.1. Local Correlation Maximization De-Noising Method (LCM)

- (1)
- Decomposing the original and transformed spectrum into five layers using a wavelet de-noising method that is based on the Sym8 matrix function.
- (2)
- Calculating the correlation coefficients for the measured TN content compared with both initial (including original and transformed spectrum, the same hereafter) and decomposed spectral reflectance (1–5 levels in this study), in the range of 350–2500 nm.
- (3)
- Finding the optimal decomposition level of each band, which has the maximum correlation coefficient among initial and decomposed spectra (1–5 levels) at each wavelength; then, the corresponding correlation coefficient and decomposed band are taken as the local optimal correlation coefficient (LOCC) and optimal band (OB). After all the LOCCs and OBs are acquired, the overall LOCC and OB are used to determine the optimal correlative curve (OCC) and the optimal spectra (OSP), respectively. Finally, the OSP and OCC of original and transformed spectra are obtained, Figure 3 shows the overall approach.

**Figure 3.**Schema showing an overview about obtaining of the optimal correlative curve (OCC) and the optimal spectra (OSP).

#### 2.2.2. Partial Least Square Regression (PLS Regression) Method

#### 2.2.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)

_{1,1}and O

_{1,2}are used to grade the memberships of fuzzy sets A and B. Usually, a bell function is used as follows:

_{i}, b

_{i}, and c

_{i}are the premise parameters.

_{i}, q

_{i}, r

_{i}are the design parameters.

#### 2.2.4. Local Correlation Maximization-Complementary Superiority (LCMCS)

- (1)
- Spectral transforms. Spectral transforms help reduce the influence of noise; therefore, each REF is transformed into FDR, log(1/R) and (log[1/R])′.
- (2)
- LCM analysis. To maximize the use of TN response information and eliminate the interference of noisy data, OSP and OCC of the original and transformed spectrum are obtained by LCM de-noising method, which has significant correlativity with TN content.
- (3)
- Complementary superiority. OSP and measured TN values are used in PLS regression analysis, and several principal components (five principal components in this study) are acquired. These principal components and the measured TN contents are then used in ANFIS analysis, and the LCMCS models are established.
- (4)
- Model-verifying. Sample data are used for model calibration and verification. In this study, from the 280 samples in each treatment, 150 samples were used for model calibration and the remaining 130 samples were used for model verification. Then, the best model was selected as the final model using the LCMCS method.

#### 2.2.5. Model Evaluation Standard

^{2}, root mean square error of calibration (RMSEC) and mean relative error of calibration (MREC). The estimation results were evaluated by root mean square error of validation (RMSEV) and mean relative error of validation (MREV). A good model will have a high R

^{2}, low root mean square errors (RMSEC and RMSEV), and small mean relative errors (MREC and MREV).

Dataset | NS | EP | |
---|---|---|---|

Calibration | 150 | 55 C | R^{2}, RMSEC, MREC |

50 R | |||

45 F | |||

Validation | 130 | 45 C | R^{2}, RMSEV, MREV |

45 R | |||

40 F |

## 3. Results and Discussion

#### 3.1. Interpretation of Soil Spectral Reflectance

^{−1}). The samples of the Fengfeng site had much more TN than samples of Cangzhou and Renqiu. Figure 5 also indicates that soil reflectance generally decreases with increasing TN content. A TN of 18.70 mg∙kg

^{−1}shows lower reflectance values than the others, probably because of its greater TN content. In the entire visible-near-infrared spectrum, three remarkable water absorption peaks were observed at 1400, 1905 and 2200 nm. Although the differences of spectral characteristics caused by TN are apparent, it is still extremely difficult to reveal the relationships between spectra and TN content directly, especially when a greater number samples are considered. Organic nitrogen is a major constituent of SOM, therefore soil reflectance decreases possible correlation with SOM, which can affect estimation accuracy of TN prediction models obviously [21,80,81]. And the SOM interference would be left behind to further research. In this study, many processing algorithms were employed for the data mining and analysis.

#### 3.2. OSP Acquisition

**Figure 6.**Wavelength dependence on coefficients of correlation between total soil nitrogen (TN) and first derivative differential of the soil spectra: initial (

**a**); decomposed (1–5 levels) (

**b**–

**f**); optimal correlative curve (OCC) (

**g**); and (

**h**) first derivative differential reflectance curve of soil sample (Initial, decomposed [5 level] and the optimal spectra [OSP]).

**Table 4.**Correlation analysis between total soil nitrogen (TN) and the first derivative differential FDR (initial and decomposed).

TSP | MPCB (nm) | CC | MNCB (nm) | CC | AACC |
---|---|---|---|---|---|

FDR | 1397 | 0.669 | 766 | −0.672 | 0.253 |

FDR (DL = 1) | 1397 | 0.689 | 1419 | −0.692 | 0.266 |

FDR (DL = 2) | 1395 | 0.697 | 1421 | −0.721 | 0.331 |

FDR (DL = 3) | 1394 | 0.695 | 1422 | −0.704 | 0.422 |

FDR (DL = 4) | 2205 | 0.714 | 1214 | −0.715 | 0.482 |

FDR (DL = 5) | 2316 | 0.725 | 1223 | −0.706 | 0.500 |

**Figure 7.**Optimal correlative curve of the original reflectance and its different transformation forms.

**Table 5.**Comparisons of the optimal correlative curve (OCC) of the first derivative differential (FDR) and the first derivative differential of reciprocal logarithm (log[1/R])′.

TSP | CL | NB | MPCB (nm) | CC | MNCB (nm) | CC |
---|---|---|---|---|---|---|

FDR | ** | 2023 | 2316 | 0.725 | 1421 | −0.721 |

>0.40 | 1759 | 2316 | 0.725 | 1421 | −0.721 | |

>0.45 | 1654 | 2316 | 0.725 | 1421 | −0.721 | |

>0.50 | 1510 | 2316 | 0.725 | 1421 | −0.721 | |

>0.55 | 1291 | 2316 | 0.725 | 1421 | −0.721 | |

>0.60 | 949 | 2316 | 0.725 | 1421 | −0.721 | |

(log[1/R])′ | ** | 1655 | 1422 | 0.797 | 2205 | −0.739 |

>0.40 | 566 | 1422 | 0.797 | 2205 | −0.739 | |

>0.45 | 392 | 1422 | 0.797 | 2205 | −0.739 | |

>0.50 | 210 | 1422 | 0.797 | 2205 | −0.739 | |

>0.55 | 134 | 1422 | 0.797 | 2205 | −0.739 | |

>0.60 | 92 | 1422 | 0.797 | 2205 | −0.739 |

**Figure 8.**Optimal spectrum (OSP) of the first derivative differential (FDR) (

**a**) and the first derivative differential of reciprocal logarithm (log[1/R])′ (

**b**).

#### 3.3. Applicability of LCMCS Model

**Table 6.**Comparisons of the performance of models established by the local correlation maximization-complementary superiority method at different correlative levels of the first derivative differential (FDR (optimal spectrum [OSP]) and the first derivative differential of reciprocal logarithm (log[1/R])′ (OSP).

TSP | CL | LVs | Calibration (n = 150) | Validation (n = 130) | ||||
---|---|---|---|---|---|---|---|---|

R^{2} | RMSEC | MREC | R^{2} | RMSEV | MREV | |||

FDR | ** | 5 | 0.951 | 0.629 | 3.311 | 0.808 | 1.169 | 7.901 |

>0.40 | 5 | 0.946 | 0.667 | 3.818 | 0.829 | 1.095 | 7.901 | |

>0.45 | 5 | 0.923 | 0.793 | 4.909 | 0.834 | 1.076 | 6.969 | |

>0.50 | 5 | 0.920 | 0.808 | 5.231 | 0.823 | 1.105 | 6.890 | |

>0.55 | 5 | 0.927 | 0.767 | 4.781 | 0.831 | 1.080 | 7.051 | |

>0.60 | 5 | 0.917 | 0.821 | 5.168 | 0.797 | 1.184 | 8.068 | |

(log[1/R])′ | ** | 5 | 0.991 | 0.269 | 1.446 | 0.885 | 0.898 | 5.921 |

>0.40 | 5 | 0.939 | 0.704 | 4.220 | 0.681 | 1.529 | 9.613 | |

>0.45 | 5 | 0.910 | 0.854 | 5.009 | 0.817 | 1.123 | 7.602 | |

>0.50 | 5 | 0.953 | 0.616 | 3.615 | 0.785 | 1.240 | 8.178 | |

>0.55 | 5 | 0.954 | 0.608 | 3.037 | 0.779 | 1.234 | 7.626 | |

>0.60 | 5 | 0.957 | 0.588 | 2.968 | 0.776 | 1.255 | 7.815 |

^{2}= 0.991, RMSEC = 0.269 and MREC = 1.446) and validation (R

^{2}= 0.885, RMSEV = 0.898 and MREV = 5.921) analyses compared with other models. For the purpose of comparison, three issues were separately considered, and the corresponding solutions are as follows:

- (1)
- PLS regression method. In PLS regression models, decomposed FDR (5 level) and (log[1/R])′ (4 level), whose correlation coefficients reached to 0.725 and 0.797, respectively, were used in PLS analysis. Based on the 1293 selected effective bands of (log[1/R])′ (5 level), whose correlation coefficients were significant (p < 0.01), the optimal model of PLS method was obtained, which was selected as the final model of the PLS regression method.
- (2)
- Local correlation maximization method (LCM). Facing the second issue of how to reduce noise while retaining as much useful information as possible, OSP of FDR and (log[1/R])′ were used in PLS regression analysis. Based on the 1655 selected effective bands of (log[1/R])′ (OSP), whose correlation coefficients were significant (p < 0.01), the optimal model of the LCM method was obtained and selected as the final model of the LCM method.
- (3)
- Complementary superiority method (CS). The CS model, which had the advantages of PLS regression and ANFIS, was aimed at addressing the third issue. The same PLS regression models, decomposed FDR (5 level) and (log[1/R])′ (4 level) were used. Based on the 382 selected effective bands of (log[1/R])′ (4 level), whose correlation coefficients were greater than 0.40, the optimal model of CS method was created and the final model of LCM method was determined.

**Table 7.**Test result of the local correlation maximization-complementary superiority method (LCMCS), complementary superiority (CS), local correlation maximization (LCM) and partial least squares regression (PLS) models provides for total soil nitrogen (TN) content.

Model | TSP | LVs | Calibration (n = 150) | Validation (n = 130/45 C/45 R/40 F) | ||||||
---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSEC | %MREC | R^{2} | RMSEV | %MREV | |||||

LCMCS | (log[1/R])′ | 5 | 0.991 | 0.269 | 1.446 | 0.885 | 0.898 | 0.861 C | 5.921 | 6.463 C |

0.713 R | 5.412 R | |||||||||

1.103 F | 5.883 F | |||||||||

LCM | (log[1/R])′ | 8 | 0.916 | 0.804 | 5.498 | 0.799 | 1.191 | 1.130 C | 7.972 | 8.899 C |

0.863 R | 6.839 R | |||||||||

1.529 F | 8.205 F | |||||||||

CS | (log[1/R])′ | 5 | 0.953 | 0.620 | 3.473 | 0.817 | 1.147 | 1.131 C | 7.572 | 8.394 C |

0.945 R | 6.958 R | |||||||||

1.353 F | 7.337 F | |||||||||

PLS | (log[1/R])′ | 8 | 0.830 | 1.141 | 7.756 | 0.747 | 1.373 | 1.354 C | 9.525 | 10.38 C |

1.148 R | 9.415 R | |||||||||

1.608 F | 8.683 F |

^{2}= 0.747, RMSEV = 1.373, MREV = 9.525%; Table 7); this indicates that the PLS regression method based on spectral transforms and wavelet analysis is suitable for subsided land due to excessive extraction of different resources as discussed above. When the second issue was considered, the LCM model did perform better than the PLS regression model with the R

^{2}of 0.799, RMSEV of 1.191 and the MREV of 7.972%; its accuracy to predict was obviously enhanced at all three sites, Changzhou, Renqiu and Fengfeng. Moreover, a small improvement occurred in the CS model when compared with the LCM model, although the precision in Renqiu was reduced from 6.839% to 6.958%. The results of the LCM and CS models indicate that when second and third issues were considered, the predictive effects can be improved significantly. However, it can be seen from the comparison that the LCMCS model (Figure 9a) produced lower prediction errors during both the calibration (R

^{2}= 0.991, RMSEV = 0.269 and MREV = 1.446%) and validation (R

^{2}= 0.885, RMSEV = 0.898, MREV = 5.921%) when compared with models built by other three methods (Figure 9b–d). Moreover, at all three sites, Cangzhou (RMSEV = 0.861, MREV = 6.463%), Renqiu (RMSEV = 0.713, MREV = 5.412%) and Fengfeng (RMSEV = 1.103, MREV = 5.883%), the estimation accuracy of the LCMCS model was also the closest to the ideal. In addition, overall models indicted that the estimation accuracy in Cangzhou was the poorest, followed by Fengfeng (except PLS model). The cause of this results and the influence degree of model estimation results from the land subsidence would be left behind to further research.

**Figure 9.**Comparisons of measured and predicted values by the local correlation maximization-complementary superiority method (LCMCS) (

**a**); complementary superiority (CS) (

**b**); local correlation maximization (LCM) (

**c**) and partial least squares regression (PLS) (

**d**) methods.

## 4. Conclusions/Outlook

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**MDPI and ACS Style**

Lin, L.; Wang, Y.; Teng, J.; Xi, X. Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method. *Sensors* **2015**, *15*, 17990-18011.
https://doi.org/10.3390/s150817990

**AMA Style**

Lin L, Wang Y, Teng J, Xi X. Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method. *Sensors*. 2015; 15(8):17990-18011.
https://doi.org/10.3390/s150817990

**Chicago/Turabian Style**

Lin, Lixin, Yunjia Wang, Jiyao Teng, and Xiuxiu Xi. 2015. "Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method" *Sensors* 15, no. 8: 17990-18011.
https://doi.org/10.3390/s150817990