# Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

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## Abstract

**:**

^{2}= 0.921, MAE

_{V}= 0.512 g/kg, RMSE

_{V}= 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.

## 1. Introduction

## 2. Materials

#### 2.1. Study Area

#### 2.2. Gathering and Preparation of the Soil Samples

#### 2.3. Hyperspectral Satellite Data Acquisition, Pre-Processing, and First Derivative Processing

## 3. Methods

#### 3.1. Characteristic Spectral Band Selection

#### 3.1.1. Correlation Coefficient (CC)

#### 3.1.2. Least Absolute Shrinkage and Selection Operator (Lasso)

#### 3.2. Prediction Models

#### 3.2.1. Multiple Linear Regression (MLR)

#### 3.2.2. Partial Least Squares Regression (PLSR)

#### 3.2.3. Random Forest (RF)

#### 3.3. Model Accuracy Evaluation

^{2}), root mean square error (RMSE), and mean absolute error (MAE) were used as metrics to assess how well various models performed [10]. The related equation is this:

^{2}value, the lower the RMSE and MAE, and the more accurate the model.

#### 3.4. Model Stability Evaluation

## 4. Results

#### 4.1. Statistical Description of SOM Content

#### 4.2. Spectral Reflectance Characteristics of Soil Samples

#### 4.2.1. Original Reflectance (OR)

#### 4.2.2. First Derivative Reflectance (FDR)

#### 4.3. Characteristic Spectral Band Selection

#### 4.3.1. CC Characteristic Band Choice

#### 4.3.2. Lasso Characteristic Spectral Band Choice

#### 4.4. The Results of Prediction Models

^{2}values higher than 0.85, demonstrating the RF models’ potent predictive power. FDR-Lasso-RF was the best prediction model. In its validation set, R

^{2}= 0.921, MAE = 0.512 g/kg, RMSE = 0.645 g/kg. Compared with other models, R

^{2}was the highest, and MAE and RMSE were the lowest, indicating that the FDR-Lasso-RF model had the optimal SOM prediction performance. In addition to the FDR-Lasso-RF model having a good prediction effect, the OR-Lasso-PLSR and FDR-Lasso-PLSR models also had certain prediction abilities, and their R

^{2}were 0.448 and 0.411. The MLR model’s calibration set and validation set accuracy varied greatly, and its capacity for model estimation was subpar. The RF model’s calibration set and validation set both had identical prediction accuracy and a high level of SOM estimation. The prediction outcomes based on FDR were also superior to OR for the RF model.

## 5. Discussion

#### 5.1. Benefits of FDR Processing

#### 5.2. Benefits of Lasso Feature Band Selection

^{2}= 0.921, MAE

_{V}= 0.512 g/kg, RMSE

_{V}= 0.645 g/kg. This was because the SOM-independent spectral information contained in the full-band and CC feature selection bands interfered with the estimation results. The Lasso variable selection method can retain feature information, remove noise information, and improve model prediction accuracy.

#### 5.3. Importance Analysis of RF Model Input Variables

#### 5.4. Optimal Model SOM Mapping

## 6. Conclusions

- (1)
- In comparison to OR, FDR treatment can increase model fitting accuracy by emphasizing SOM spectral characteristic information in the soil spectrum.
- (2)
- The Lasso feature selection method can effectively extract the SOM feature and spectral bands, reduce the data dimension, highlight key information, and enhance model estimation capabilities.
- (3)
- The RF model provides excellent SOM content prediction and model stability. The FDR-Lasso-RF model is the best prediction model, with R
^{2}= 0.921, MAE_{V}= 0.512 g/kg, and RMSE_{V}= 0.645 g/kg in its validation set. - (4)
- Hyperspectral satellite data make up for the lack of large-scale observation of laboratory hyperspectral data and provide a feasible method for rapid, large-scale, and accurate prediction and mapping of SOM content in agricultural areas.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Map of the study area ((

**a**): map of Henan Province; (

**b**) ZY1-O2D hyperspectral image and location of the study area’s sampling locations; (

**c**) the study area’s distribution of agricultural areas).

**Figure 3.**Soil reflectance curves with different soil organic matter content ((

**a**) is OR; (

**b**) is FDR).

**Figure 5.**Lasso characteristic spectral band selection ((

**a**) OR-Lasso characteristic selection; (

**b**) FDR-Lasso characteristic selection).

**Figure 6.**Scatter plot of predicted and measured SOM values for the full-band RF model ((

**a**) OR-RF-Calibration; (

**b**) OR-RF-Validation; (

**c**) FDR-RF-Calibration; (

**d**) FDR-RF-Validation).

**Figure 7.**Scatter plot of predicted and measured SOM values for the CC signature band RF model ((

**a**) OR-CC-RF-Calibration; (

**b**) OR-CC-RF-Validation; (

**c**) FDR-CC-RF-Calibration; (

**d**) FDR-CC-RF-Validation).

**Figure 8.**Scatter plot of predicted and measured SOM values for the Lasso signature band RF model ((

**a**) OR-Lasso-RF-Calibration; (

**b**) OR-Lasso-RF-Validation; (

**c**) FDR-Lasso-RF-Calibration; (

**d**) FDR-Lasso-RF-Validation).

Satellite Payloads | ZY-1-02D |
---|---|

Launch Time | 12 December 2019 |

Number of Bands | 76 (VNIR), 90 (SWIR) |

Spectral Range (nm) | 400–2500 |

Spectral Resolution (nm) | 10 (VNIR), 20 (SWIR) |

Spatial Resolution (m) | 30 |

Swath Width (km) | 60 |

Revisit Cycle (d) | 3 |

Set | N | Max (g/kg) | Min (g/kg) | Mean (g/kg) | SD (g/kg) | CV (%) |
---|---|---|---|---|---|---|

Whole set | 539 | 28.446 | 12.413 | 20.316 | 2.501 | 12.309 |

Calibration set | 431 | 28.446 | 12.413 | 20.195 | 2.527 | 12.513 |

Validation set | 108 | 27.412 | 13.792 | 20.797 | 2.344 | 11.271 |

Spectral | Number of Bands | Wavelength Range (nm) | Maximum Correlation Band (nm) | Correlation Coefficient R |
---|---|---|---|---|

OR | 133 | 400–413, 551–697 | 1661 | −0.495 |

766–1157, 1308–2500 | ||||

FDR | 149 | 405–628, 654–843 | 611 | −0.621 |

868–954, 988–1594 | ||||

1627–1779, 1829 | ||||

1862–2166, 2216–2500 |

Method | Number of Bands | Wavelength (nm) |
---|---|---|

OR-Lasso | 27 | 400, 490–500, 516, 636–671, 722–731, 808, 825, 988, 1123 |

1241, 1425, 1459, 1695, 1846, 1930, 2082, 2166, 2233 | ||

2468–2500 | ||

FDR-Lasso | 6 | 559, 567, 714, 731, 1964, 2048 |

Model | Method | Calibration Set | Validation Set | ||||
---|---|---|---|---|---|---|---|

${\mathbf{R}}_{\mathbf{C}}^{2}$ | ${\mathbf{MAE}}_{\mathbf{C}}$ | ${\mathbf{RMSE}}_{\mathbf{C}}$ | ${\mathbf{R}}_{\mathbf{V}}^{2}$ | ${\mathbf{MAE}}_{\mathbf{V}}$ | ${\mathbf{RMSE}}_{\mathbf{V}}$ | ||

(g/kg) | (g/kg) | (g/kg) | (g/kg) | ||||

MLR | OR | 0.646 | 1.166 | 1.503 | 0.256 | 1.751 | 2.203 |

OR-CC | 0.616 | 1.203 | 1.564 | 0.239 | 1.712 | 2.159 | |

OR-Lasso | 0.453 | 1.471 | 1.863 | 0.296 | 1.536 | 1.925 | |

FDR | 0.625 | 1.202 | 1.545 | 0.200 | 1.789 | 2.279 | |

FDR-CC | 0.601 | 1.242 | 1.594 | 0.172 | 1.821 | 2.315 | |

FDR-Lasso | 0.414 | 1.508 | 1.929 | 0.385 | 1.409 | 1.799 | |

PLSR | OR | 0.330 | 1.598 | 2.066 | 0.307 | 1.528 | 1.942 |

OR-CC | 0.312 | 1.618 | 2.094 | 0.298 | 1.528 | 1.954 | |

OR-Lasso | 0.316 | 1.611 | 2.083 | 0.448 | 1.337 | 1.704 | |

FDR | 0.316 | 1.599 | 2.087 | 0.286 | 1.540 | 1.972 | |

FDR-CC | 0.317 | 1.599 | 2.087 | 0.285 | 1.539 | 1.972 | |

FDR-Lasso | 0.393 | 1.525 | 1.963 | 0.411 | 1.369 | 1.761 | |

RF | OR | 0.900 | 0.603 | 0.797 | 0.880 | 0.633 | 0.807 |

OR-CC | 0.898 | 0.614 | 0.806 | 0.871 | 0.657 | 0.838 | |

OR-Lasso | 0.908 | 0.601 | 0.764 | 0.901 | 0.537 | 0.721 | |

FDR | 0.913 | 0.567 | 0.743 | 0.897 | 0.582 | 0.750 | |

FDR-CC | 0.913 | 0.570 | 0.734 | 0.893 | 0.590 | 0.762 | |

FDR-Lasso | 0.923 | 0.553 | 0.701 | 0.921 | 0.512 | 0.645 |

Model | Method | Calibration Set | Validation Set |
---|---|---|---|

RF | OR | 0.160 | 0.302 |

OR-CC | 0.157 | 0.291 | |

OR-Lasso | 0.156 | 0.309 | |

FDR | 0.162 | 0.305 | |

FDR-CC | 0.160 | 0.296 | |

FDR-Lasso | 0.167 | 0.307 |

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## Share and Cite

**MDPI and ACS Style**

Guo, H.; Zhang, R.; Dai, W.; Zhou, X.; Zhang, D.; Yang, Y.; Cui, J.
Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region. *Agronomy* **2022**, *12*, 2111.
https://doi.org/10.3390/agronomy12092111

**AMA Style**

Guo H, Zhang R, Dai W, Zhou X, Zhang D, Yang Y, Cui J.
Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region. *Agronomy*. 2022; 12(9):2111.
https://doi.org/10.3390/agronomy12092111

**Chicago/Turabian Style**

Guo, Hengliang, Rongrong Zhang, Wenhao Dai, Xiaowen Zhou, Dujuan Zhang, Yaohuan Yang, and Jian Cui.
2022. "Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region" *Agronomy* 12, no. 9: 2111.
https://doi.org/10.3390/agronomy12092111