# Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}

_{C}) = 0.887, calibration root mean square error (RMSE

_{C}) = 2.488%, standard deviation (SD) = 6.733%, and r = 0.942. However, the partial least squares regression (PLSR) model had the strongest predictive ability, with validation coefficient of determination (R

^{2}

_{V}) = 0.787, validation root mean square error (RMSE

_{V}) = 3.247%, and relative prediction deviation (RPD) = 2.071. The variable importance in projection (VIP) method could not only improve model efficiency but also increased model accuracy. R

^{2}

_{C}of the optimal PLSR model was 0.733, RMSE

_{C}was 3.028%, R

^{2}

_{V}was 0.805, RMSE

_{V}was 3.100%, RPD was 1.976, and Akaike information criterion (AIC) was 151.050. The three-band optimized spectral indices with fractional differential pretreatment could to a certain extent break through the limitation of visible near-infrared spectrum in SMC estimation due to the lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery.

## 1. Introduction

^{2}

_{V}> 0.6, relative prediction deviation (RPD) > 1.5) and were generally better than other models established by indoor hyperspectral data and simulated Hyperion band hyperspectral data, which proved the feasibility and potential of Hyperion hyperspectral images in soil attribute quantitative remote sensing [6]. Tiwari et al. established an artificial neural network (ANN) model for quantitative inversion of SOC based on Hyperion hyperspectral images and ground-measured hyperspectral data, and concluded that the ANN model was a powerful tool for inversion of SOC in farmland areas [7]. Gomez et al. conducted a study on the uncertainty analysis in the construction of the quantitative inversion model of soil clay content (SCC) based on airborne hyperspectral images acquired by the AISA-DUAL airborne imaging spectrometer. The research results provided certain guidance for improving the accuracy of the model [8,9]. At the same time, the sensitivity of carrying hyperspectral images to atmospheric and scale effects in the hyperspectral quantitative inversion of SCC had been also studied by Gomez et al., and the research results were expected to provide a theoretical basis for the development and application of related hyperspectral sensors [10]. Scholars such as Vaudour and Ouerghemmi used airborne hyperspectral images (AISA-Eagle images and HyMap images) to carry out relevant studies on the quantitative inversion of SOC (RMSE

_{V}= 1.44 g/kg) and SCC (R

^{2}

_{V}= 0.61, RPD = 1.5 when normalized difference vegetation index (NDVI) < 0.55), which achieved good results by the PLSR model [11,12]. Peon et al. combined airborne hyperspectral imagery acquired by the airborne hyperspectral scanner (AHS) and Hyperion satellite hyperspectral imagery to carry out a quantitative inversion study of SOC content, and finally concluded that the SMLR modeling method based on the spectral indices can achieve better inversion results (R

^{2}

_{V}= 0.60 − 0.62 for AHS and R

^{2}

_{V}= 0.49 − 0.61 for Hyperion) [13]. Liu et al. initially discussed the feasibility of applying the transfer learning method based on convolutional neural network to the quantitative inversion of SOC in HyMap imagery; its accuracy was R

^{2}

_{V}= 0.601, RMSE

_{V}= 8.62, and RPD = 1.54 [14]. Ge et al., on the basis of the method of optimized spectral indices combined with machine learning modeling, initially carried out research on the monitoring of the SMC of farmland with UAV hyperspectral imagery acquired by the Headwall Nano-Hyperspec airborne hyperspectral imaging spectrometer. The inversion model had high accuracy (R

^{2}

_{V}= 0.907, RMSE

_{V}= 1.477, and RPD = 3.396), which fully proved the potential of the SMC inversion by UAV hyperspectral remote sensing [15].

^{2}

_{V}of the model based on ground hyperspectral data was 0.95, the RMSE

_{V}was 4.38 g/kg, and the RPD was 3.36. The estimation model based on the remote sensing image spectrum also had good accuracy, and the R

^{2}

_{V}was 0.91, RMSE

_{V}was 4.82 g/kg, and RPD was 3.32. Yasenjiang et al. [27] used the optimized spectral indices to estimate the salt content of three soils with temporal and spatial heterogeneity, and revealed the effectiveness of the optimized spectral indices to eliminate heterogeneity to a certain extent and improve universality. Li Chen et al. [28] carried out active explorations in the quantitative estimation of SMC on the basis of the optimized spectral indices method. The characteristics of SMC and soil spectral curve were comprehensively analyzed by obtaining field-measured SMC data and corresponding indoor soil surface hyperspectral reflectance data, and the accuracy of the traditional spectral modeling method and the optimized spectral indices method in the estimation of SMC was compared. It was considered that the SMC estimation model based on RSI (R

_{1407}, R

_{1459}) can more accurately estimate the SMC of coastal saline soil. Wang [29] et al. used the fractional differential method to preprocess the original hyperspectral data, and then used the optimized spectral indices method to calculate the r between the hyperspectral data and soil organic matter (SOM). It was found that the fractional differential pretreatment can also improve the r to a certain extent. The highest r was 0.52 at 1.2-order differential, while the combination of fractional differential and optimized spectral indices had a more significant effect on improving the r. The r can be increased to 0.86, which played an important role in improving the accuracy of the model. In some other studies, it was also concluded that optimized spectral indices combined with spectral transformation could achieve better modeling results and had good inversion feasibility [30,31]. Nijat et al. used the multi-dimensional modeling method to quantitatively retrieve the SSC on the basis of the WorldView-2 multi-spectral imagery [32], and the results obtained were consistent with the views of Li.

## 2. Materials and Methods

#### 2.1. Study Area and Sampling Sites

#### 2.2. Data Collection

#### 2.2.1. Field Sampling and Spectral Measurements

#### 2.2.2. Zhuhai-1 Hyperspectral Imagery

#### 2.3. Fractional Order Differential Method

#### 2.4. Optimized Spectral Indices Method

#### 2.4.1. Two-Band Spectral Indices

#### 2.4.2. Three-Band Spectral Indices

#### 2.5. Modelling Strategies

#### 2.5.1. Optimal Variable Selection Method

- (1)
- PCC method

- (2)
- VIP method

_{jf}is the weight value of the j variable and the f component, SSY

_{f}is the sum of squares of the variances of the f component and the J variables, SSY

_{total}is the total sum of squares of the explanatory dependent variable, and F is the total number of components.

#### 2.5.2. Modeling Approaches

- (1)
- MLR model

- (2)
- PR model

- (3)
- NR model

- (4)
- PLSR model

- (5)
- CART model

- (6)
- RF regression model

- (7)
- MARS model

- (8)
- TGBM regression model

#### 2.5.3. Comparison of Model Accuracy

^{2}

_{C}, R

^{2}

_{V}, RMSE

_{C}, RMSE

_{V}, RPD, and Akaike information criterion (AIC) [24]. R

^{2}

_{C}and R

^{2}

_{V}determine the stability of the model. The closer the R

^{2}

_{C}and R

^{2}

_{V}is to 1, the better the stability of the model. RMSE

_{C}and RMSE

_{V}are used to characterize the accuracy of the model. The closer the RMSE

_{C}and RMSE

_{V}is to 0, the higher the accuracy of the model. The RPD threshold is divided into three categories. When a < 1.4, the model’s estimation ability is low, when b ≥ 1.4, b < 2.0, the model’s estimation ability is feasible. When c ≥ 2.0, the model’s estimation ability is good. In addition, AIC, as an index to evaluate the efficiency of the model can weigh the complexity of the estimated model and the goodness of the model fitted to the data. The smaller the value of AIC, the better the model can explain the data with the least free parameters, making it better at avoiding overfitting [32].

## 3. Results

#### 3.1. Analysis of SMC and Hyperspectral Characteristics

#### 3.1.1. Descriptive Statistical Analysis of SMC

#### 3.1.2. Soil Hyperspectral Characteristics Analysis

- (1)
- Soil average spectral curve analysis

- (2)
- Analysis of soil spectral characteristics under different humidity conditions

- (3)
- Analysis of soil spectral characteristics of different fractional differential orders

#### 3.2. PCC Analysis of SMC and Spectrum

#### 3.2.1. One-Dimensional PCC Analysis of SMC and Spectrum

#### 3.2.2. Two-Dimensional PCC Analysis of SMC and Spectrum

#### 3.2.3. Three-Dimensional PCC Analysis of SMC and Spectrum

#### 3.3. Model

_{C}, r, and R

^{2}

_{C}. As can be seen from the graph, among all the eight models, the TGBM model had the best overall performance, with a R

^{2}

_{C}of 0.887, a RMSE

_{C}of 2.488%, an SD of 6.733%, and a r of 0.942, while MLR, NR, RF, and CART had low accuracy. In addition, PLSR had the best comprehensive performance in conventional statistical models.

^{2}

_{V}was 0.787, RMSE

_{V}was 3.247%, and RPD was 2.071. The second was the MARS model with a R

^{2}

_{V}of 0.774, RMSE

_{V}of 3.084%, and RPD of 1.972. Therefore, in this paper, the PLSR model was used as the inversion model of SMC. This was because of its high accuracy, and because its model expression was practical.

## 4. Discussion

#### 4.1. Discussion on Optimized Spectral Indices

#### 4.2. Discussion on Fractional Order Differential

#### 4.3. Discussion on Spectral Scale

#### 4.4. Discussion on Models

#### 4.5. Discussion on Zhuhai-1 Hyperspectral Imagery

## 5. Conclusions

^{2}

_{C}of the optimal PLSR model was 0.733, RMSE

_{C}was 3.028%, R

^{2}

_{V}was 0.805, RMSE

_{V}was 3.100%, RPD was 1.976, and AIC was 151.050. The results showed that the fractional order differential three-band optimized spectral indices can to a certain extent break through the limitations of visible near-infrared spectroscopy in the estimation of SMC due to the lack of short-wave infrared spectra. We proved that it is possible to retrieve regional SMC using Zhuhai-1 hyperspectral imagery, which provides a new research idea for improving the accuracy of hyperspectral satellite remote sensing inversion.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**Soil average spectral curve. (

**a**) the average spectral curves of 171 soil samples in the 400–2400 nm band with 1 nm intervals; (

**b**) the average spectral curves in the 400–1000 nm band with 1 nm intervals; (

**c**) the average spectral curves in the 466–938 nm band with 8 nm intervals.

**Figure 6.**Soil spectrum curves under different humidity conditions. (

**a**) spectral curves selected from the indoor soil moisture control experimental spectral data; (

**b**) spectral curves obtained after averaging 171 soil samples according to the soil moisture grading standard.

**Figure 8.**One-dimensional r diagram between SMC and original spectrum. (

**a**) r diagram between SMC and 400–2400 nm spectra; (

**b**) r diagram between SMC and 400–1000 nm spectra; (

**c**) r diagram between SMC and 466–938 nm spectra.

Channel | Center | Channel | Center | Channel | Center | Channel | Center |
---|---|---|---|---|---|---|---|

B01 | 466 | B09 | 596 | B17 | 716 | B25 | 836 |

B02 | 480 | B10 | 610 | B18 | 730 | B26 | 850 |

B03 | 500 | B11 | 626 | B19 | 746 | B27 | 866 |

B04 | 520 | B12 | 640 | B20 | 760 | B28 | 880 |

B05 | 536 | B13 | 656 | B21 | 776 | B29 | 896 |

B06 | 550 | B14 | 670 | B22 | 790 | B30 | 910 |

B07 | 566 | B15 | 686 | B23 | 806 | B31 | 926 |

B08 | 580 | B16 | 700 | B24 | 820 | B32 | 940 |

Channel | Center | Channel | Center | Channel | Center | Channel | Center |
---|---|---|---|---|---|---|---|

B01 | 466 | B16 | 586 | B31 | 706 | B46 | 826 |

B02 | 474 | B17 | 594 | B32 | 714 | B47 | 834 |

B03 | 482 | B18 | 602 | B33 | 722 | B48 | 842 |

B04 | 490 | B19 | 610 | B34 | 730 | B49 | 850 |

B05 | 498 | B20 | 618 | B35 | 738 | B50 | 858 |

B06 | 506 | B21 | 626 | B36 | 746 | B51 | 866 |

B07 | 514 | B22 | 634 | B37 | 754 | B52 | 874 |

B08 | 522 | B23 | 642 | B38 | 762 | B53 | 882 |

B09 | 530 | B24 | 650 | B39 | 770 | B54 | 890 |

B10 | 538 | B25 | 658 | B40 | 778 | B55 | 898 |

B11 | 546 | B26 | 666 | B41 | 786 | B56 | 906 |

B12 | 554 | B27 | 674 | B42 | 794 | B57 | 914 |

B13 | 562 | B28 | 682 | B43 | 802 | B58 | 922 |

B14 | 570 | B29 | 690 | B44 | 810 | B59 | 930 |

B15 | 578 | B30 | 698 | B45 | 818 | B60 | 938 |

Degree of Drought | SMC |
---|---|

High humidity | >20% |

Suitable humidity | 15–20% |

Mild drought | 12–15% |

Moderate drought | 5–12% |

Severe drought | <5% |

Model | R^{2}_{V} | RMSE_{V} (%) | RPD |
---|---|---|---|

MLR | 0.694 | 3.181 | 1.888 |

PR | 0.684 | 3.870 | 1.495 |

NR | 0.726 | 3.631 | 1.518 |

PLSR | 0.787 | 3.247 | 2.071 |

RF | 0.662 | 3.771 | 1.744 |

MARS | 0.774 | 3.084 | 1.972 |

CART | 0.544 | 4.922 | 1.245 |

TGBM | 0.707 | 3.525 | 1.879 |

Number of Variables | R^{2}_{C} | RMSE_{C} (%) | R^{2}_{V} | RMSE_{V} (%) |
---|---|---|---|---|

54 | 0.727 | 3.061 | 0.796 | 2.545 |

27 | 0.733 | 3.028 | 0.805 | 3.100 |

9 | 0.738 | 3.002 | 0.761 | 2.627 |

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## Share and Cite

**MDPI and ACS Style**

Kahaer, Y.; Tashpolat, N.; Shi, Q.; Liu, S.
Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices. *Water* **2020**, *12*, 3360.
https://doi.org/10.3390/w12123360

**AMA Style**

Kahaer Y, Tashpolat N, Shi Q, Liu S.
Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices. *Water*. 2020; 12(12):3360.
https://doi.org/10.3390/w12123360

**Chicago/Turabian Style**

Kahaer, Yasenjiang, Nigara Tashpolat, Qingdong Shi, and Suhong Liu.
2020. "Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices" *Water* 12, no. 12: 3360.
https://doi.org/10.3390/w12123360