# Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R

^{2}, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Image Acquisition and Preprocessing

#### 2.3. Soil Sampling and Laboratory Procedures

#### 2.4. Construction of Soil Salinity Retrieval Index

#### 2.5. Validation

_{REG}) were calculated and classed as VI indexes. The salinity index stands for the soil salinity index. It is represented by six algebras (SI-T, SI1, SI2, SI3, NDSI, and SRSI), with Soil Remote Sensing Index (SRSI) being the transformation and synthesis index of the Soil Salinity Index SI1 and the vegetation index NDVI (Table 2). The brightness index (BI) is determined using the R and NIR bands.

^{2}), root mean square error (RMSE), and residual predictive deviation (RPD) were employed to evaluate the regression results. R

^{2}represents the consistency with which the model was established and validated. If R

^{2}is near to one, the model is more robust and has a better fitting degree. The RMSE is used to evaluate the model’s prediction performance. The lower the RMSE, the better the model’s prediction ability. The RPD is the ratio of the measured value’s standard deviation to the predicted error. When RPD is less than 1.4, the model cannot predict measured values; 1.4 ≤ RPD < 2 indicates that the model can roughly predict those values, and RPD more than or equal to 2.0 shows that the model has exceptional prediction ability. Models with high R

^{2}and RPD values perform better in terms of prediction and stability [42].

## 3. Results

#### 3.1. Statistical Analysis of Soil Samples

#### 3.2. Selection of Sensitive Bands

#### 3.3. Construction of Soil Salinity Retrieval Model

#### 3.4. Correlation Analysis

#### 3.5. Retrieval Accuracy

^{2}= 0.625 and 0.633) among the five methods and then was BPNN, SVM, PLSR, and MLR in order of modeling and validation accuracies (Table 8). However, only the RPD of the RF model topped 1.4, which is the rough sample prediction threshold. Therefore, in the test area, NDVI is not suited for accurate soil salinity retrieval.

^{2}values of RF, BPNN, SVM, PLSR, and MLR based on SSRI (Table 10) showed stronger fitting impacts than the retrieval model based on NDVI and SRSI (Table 8 and Table 9). Furthermore, the modeling and validation accuracies of the five techniques (RF, BPNN, SVM, PLSR, and MLR) were all higher than 0.6, and the RPD of the RF model is more than 2.2 (Table 10), which indicates that the RF has adequate soil salinity retrieval capacity.

^{2}and RMSE of the modeling set using the SSRI-based RF method were 0.724 and 1.746; and the R

^{2}, RMSE, and RPD of the validation set were 0.745, 1.879, and 2.211 (Figure 2), which were the highest. The optimal retrieval model of soil salinity in the test area is the SSRI-based RF method.

## 4. Discussions

^{2}and RMSE were 0.724 and 1.764, respectively; and the validation R

^{2}, RMSE, and RPD were 0.745, 1.879, and 2.211, respectively, which were the highest among all the models built using the five prediction approaches based on SSRI, vegetation index, and salinity index.

^{−}and SO

_{4}

^{2−}and the main cations being Na

^{+}and Ca

^{2+}[11,43]. Previous research found that although NaCl has no spectral characteristics in the visible and near-infrared bands, NaCl is correlated with gypsum [44]. Gypsum possesses absorption qualities in the visible and near-infrared bands, which can help reveal soil salinity spectral information. Xu et al. (2018) found that gypsum has molecular vibration absorption spectrum features in the NIR band, visible and NIR band can collect SO

_{4}

^{2−}spectral information [45]. Furthermore, studies have shown that salinized soil has higher reflectance in the visible and NIR bands than non-salinized soil [15,46]. Hence, spectral information of salinized soil retrieved from RS data can be used to estimate soil salinity in visible and near-infrared bands.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Green, S.M.; Dungait, J.; Tu, C.; Buss, H.L.; Sanderson, N.; Hawkes, S.J.; Xing, K.; Yue, F.; Hussey, V.L.; Peng, J.; et al. Soil functions and ecosystem services research in the Chinese karst critical zone. Chem. Geol.
**2019**, 527, 119107. [Google Scholar] [CrossRef] [Green Version] - Norris, C.E.; Quideau, S.A.; Landhusser, S.M.; Drozdowski, B.; Hogg, K.E.; Oh, S.W. Assessing structural and functional indicators of soil nitrogen availability in reclaimed forest ecosystems using 15 n-labeled aspen litter. Can. J. Soil Sci.
**2018**, 98, 357–368. [Google Scholar] [CrossRef] - Ghassemi, F.; Jakeman, A.J.; Nix, H.A. Salinisation of Land and Water Resources: Human Causes, Extent, Management and Case Studies; CAB international: Canberra, Australia, 1995; pp. 1–3. [Google Scholar]
- Wang, Z. Spatial and Temporal Variability of Soil Moisture and Salinity, Affecting Factors and Forecasting Model in the Typical Area of the Yellow River Delta. 2017. Available online: https://d.wanfangdata.com.cn/thesis/D01212536 (accessed on 8 February 2018).
- Fourati, H.T.; Bouaziz, M.; Benzina, M.; Bouaziz, S. Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques. Environ. Monit. Assess
**2017**, 189, 177. [Google Scholar] [CrossRef] [PubMed] - Koganti, T.; Narjary, B.; Zare, E.; Pathan, A.L.; Huang, J.; Triantafilis, J. Quantitative mapping of soil salinity using the dualem-21s instrument and em inversion software. Land Degrad. Dev.
**2018**, 29, 1768–1781. [Google Scholar] [CrossRef] - Jiang, H.; Shu, H. Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis, Xinjiang, China. Earth Sci. Inform.
**2018**, 12, 43–56. [Google Scholar] [CrossRef] - Wang, D. Quantitative Inversion of Water and Salt in Coastal Saline Soil in the Yellow River Delta; Shandong Agricultural University: Tai’an, China, 2020. [Google Scholar]
- Azabdaftari, A.; Sunar, F. Soil salinity mapping using multitemporal Landsat data. ISPRS—Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2016**, 7, 3–9. [Google Scholar] [CrossRef] [Green Version] - Morgan, R.S.; Abd, E.-H.M.; Rahim, S. Soil salinity mapping utilizing sentinel-2 and neural networks. Indian J. Agric. Res.
**2018**, 52, 524–529. [Google Scholar] [CrossRef] - Weng, Y.L.; Gong, P. Soil salinity measurements on the Yellow River Delta. J. Nanjing Univ. Nat. Sci.
**2006**, 42, 602–610. [Google Scholar] - Ma, C. Retrieval of soil salt content based on Sentinel-1 dual-polarization radar image. Trans. Chin. Soc. Agric. Eng.
**2018**, 34, 153–158. [Google Scholar] - Hu, J.; Peng, J.; Zhou, Y.; Xu, D.; Shi, Z. Quantitative estimation of soil salinity using uav-borne hyperspectral and satellite multispectral images. Remote Sens.
**2019**, 11, 736. [Google Scholar] [CrossRef] [Green Version] - Ivushkin, K.; Bartholomeus, H.; Bregt, A.K.; Pulatov, A.; Franceschini, M.H.; Kramer, H.; van Loo, E.N.; Roman, V.J.; Finkers, R. Uav based soil salinity assessment of cropland. Geoderma
**2018**, 338, 502–512. [Google Scholar] [CrossRef] - Wang, D.; Chen, H.; Wang, G.; Cong, J.; Wang, X.; Wei, X. Salinity Inversion of Severe Saline Soil in the Yellow River Estuary Based on UAV Multi-Spectra. Chin. Agric. Sci.
**2019**, 52, 1698–1709. [Google Scholar] - Ma, Y.; Chen, H.Y.; Zhao, G.X.; Wang, Z.R.; Wang, D.Y. Spectral index fusion for salinized soil salinity inversion using Sentinel-2A and UAV images in a coastal area. IEEE Access.
**2020**, 8, 159595–159608. [Google Scholar] [CrossRef] - Aldabaa, A.; Weindorf, D.C.; Chakraborty, S.; Sharma, A.; Li, B. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma
**2015**, 239–240, 34–46. [Google Scholar] [CrossRef] [Green Version] - Rao, B.; Sharma, R.; Ravi, S.; Das, S.N.; Dwivedi, R.S.; Thammappa, S.S.; Venkataratnam, L. Spectral behaviour of salt-affected soils. Int. J. Remote Sens.
**1995**, 16, 2125–2136. [Google Scholar] [CrossRef] - Shrestha, R.P. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degrad. Dev.
**2006**, 17, 677–689. [Google Scholar] [CrossRef] - Ramos, T.; Castanheira, N.; Oliveira, A.; Paz, A.; Gonalves, M. Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal. Agric. Water Manag.
**2020**, 241, 106387. [Google Scholar] [CrossRef] - Masoud, A. Predicting salt abundance in slightly saline soils from Landsat ETM+ imagery using spectral mixture analysis and soil spectrometry. Geoderma
**2014**, 217–218, 45–56. [Google Scholar] [CrossRef] - Yao, Z.; Chen, J.; Zhang, Z.; Tan, C.; Wei, G.; Wang, X. Effect of plastic film mulching on the accuracy of soil salinity retrieval by UAV multispectral remote sensing. Trans. Chin. Soc. Agric. Eng.
**2019**, 35, 89–97. [Google Scholar] - Chen, H.; Ma, Y.; Zhu, A.; Wang, Z.; Zhao, G.; Wei, Y. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra. Int. J. Appl. Earth Obs. Geoinf.
**2021**, 101, 102360. [Google Scholar] [CrossRef] - Jiang, C.; Pan, S.; Chen, S. Recent morphological changes of the Yellow River submerged delta: Causes and environmental implications. Geomorphology
**2017**, 293, 93–107. [Google Scholar] [CrossRef] - Fan, X.; Pedroli, B.; Liu, G.; Liu, Q.; Liu, H.; Shu, L. Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Degrad. Dev.
**2011**, 23, 175–189. [Google Scholar] [CrossRef] - He, Y.; Desutter, T.; Prunty, L.; Hopkins, D.; Jia, X.; Wysocki, D.A. Evaluation of 1:5 soil to water extract electrical conductivity methods. Geoderma
**2012**, 185–186, 12–17. [Google Scholar] [CrossRef] - FAO. Standard Operating Procedure for Saturated Soil Paste Extract; Global Soil Laboratory Network (GLOSOLAN): Rome, Italy, 2021. [Google Scholar]
- Lu, R. Soil Agrochemical Analysis Method; China Agricultural Science and Technology Press: Beijing, China, 2002. [Google Scholar]
- Yeh, Y.L.; Chen, T.C. Application of grey correlation analysis for evaluating the artificial lake site in Pingtung plain, Taiwan. Can. J. Civ. Eng.
**2011**, 31, 56–64. [Google Scholar] [CrossRef] - Li, Y. Research on Soil Salinity in the Yellow River Delta Based on Remote Sensing; Chang’an University: Xi’an, China, 2018. [Google Scholar]
- Piao, S.; Fang, J.; Ji, W.; Guo, Q.; Ke, J.; Tao, S. Variation in a satellite-based vegetation index in relation to climate in china. J. Veg. Sci.
**2004**, 15, 219–226. [Google Scholar] [CrossRef] - Alhammadi, M.S.; Glenn, E.P. Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI. Int. J. Remote Sens.
**2008**, 29, 1745–1765. [Google Scholar] [CrossRef] - Abderrazak, B.; Ali, E.B.; Rachid, B.; Hassan, R. Sentinel-MSI vnir and swir bands sensitivity analysis for soil salinity discrimination in an arid landscape. Remote Sens.
**2018**, 10, 855. [Google Scholar] - Allbed, A.; Kumar, L.; Aldakheel, Y. Assessing soil salinity using soil salinity and vegetation indices derived from ikonos high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma
**2014**, 230–231, 1–8. [Google Scholar] [CrossRef] - Yahiaoui, I.; Douaoui, A.; Zhang, Q.; Ziane, A. Soil salinity prediction in the lower cheliff plain (algeria) based on remote sensing and topographic feature analysis. J. Arid. Land
**2015**, 7, 794–805. [Google Scholar] [CrossRef] - Khan, N.M.; Rastoskuev, V.V. Mapping salt-affected soils using remote sensing indicators—A simple approach with the use of GIS IDRISI. Ratio
**2001**, 11, 5–9. [Google Scholar] - Geladi, P.; Kowalski, B. Partial Least-Squares Regression: A Tutorial. Analytica Chimica Acta
**1986**, 185, 1–17. [Google Scholar] [CrossRef] - Zelterman, D. Multivariable Linear Regression; Springer International Publishing: Berlin, Germany, 2015. [Google Scholar]
- Hecht-Nielsen, R. Theory of the Backpropagation Neural Network. Neural Netw.
**1988**, 1, 593–605. [Google Scholar] [CrossRef] - Amari, S.; Wu, S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw.
**1999**, 12, 783–789. [Google Scholar] [CrossRef] - Breiman, L. Random forest. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] [Green Version] - Terhoeven-Urselmans, T.; Schmidt, H.; Joergensen, R.G.; Ludwig, B. Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment. Soil Biol. Biochem.
**2008**, 40, 1178–1188. [Google Scholar] [CrossRef] - An, L.S.; Zhao, Q.S.; Ye, S.Y.; Liu, G.Q.; Ding, X.G. Water-salt interactions factors and vegetation effects in the groundwater ecosystem in Yellow River Delta. Adv. Water Sci.
**2011**, 22, 689–695. [Google Scholar] - Goldshleger, N.; Ben-Dor, E.; Lugassi, R.; Eshel, G. Soil degradation monitoring by remote sensing: Examples with three degradation processes. Soil Sci. Soc. Am. J.
**2010**, 74, 1433–1445. [Google Scholar] [CrossRef] - Xu, W. Spectral Discriminant Analysis of Martian Simulated Minerals and Brines; Shandong University: Weihai, China, 2018. [Google Scholar]
- Fan, X.; Liu, Y.; Tao, J.; Weng, Y. Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression. Remote Sens.
**2015**, 7, 488–511. [Google Scholar] [CrossRef] [Green Version] - Scudiero, E.; Skaggs, T.; Anderson, R.; Corwin, D. Soil degradation in farmlands of California’s San Joaquin Valley resulting from drought-induced land-use changes. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 23–28 April 2016. EPSC2016-728. [Google Scholar]

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

**a**) Location of the Kenli District in China; (

**b**) test area in the Kenli district; (

**c**) UAV image covering the test area.

**Figure 2.**Scatter plot of the optimal retrieval model (SSRI-based RF method) of soil salinity based on UAV imagery.

ID | Band | Abbreviation | Center Wavelength (nm) | Bandwidth (nm) |
---|---|---|---|---|

1 | Green | G | 550 | 40 |

2 | Red | R | 660 | 40 |

3 | Red edge | REG | 735 | 10 |

4 | Near-infrared | NIR | 790 | 40 |

**Table 2.**Spectral indexes and equations. G represents the reflectance of the green band, R denotes the reflectance of the red band, REG is the reflectance of the red edge band, and NIR is the reflectance of the near-infrared band.

Index Type | Spectral Index | Equation | Reference |
---|---|---|---|

VI | Normalized Difference Vegetation Index (NDVI) | $\frac{\mathrm{NIR}-\mathrm{R}}{\mathrm{NIR}+\mathrm{R}}$ | [19] |

Difference Vegetation Index (DVI) | $\mathrm{NIR}-\mathrm{R}$ | [19] | |

Soil Adjusted Vegetation Index (SAVI) | $\frac{\left(1+\mathrm{L}\right)\times \left(\mathrm{NIR}-\mathrm{R}\right)}{\mathrm{NIR}+\mathrm{R}+\mathrm{L}},\mathrm{L}=0.5$ | [32] | |

Ratio Vegetation Index (RVI) | $\frac{\mathrm{NIR}}{\mathrm{R}}$ | [32] | |

Green Normalized Difference Vegetation Index (GNDVI) | $\frac{\mathrm{NIR}-\mathrm{G}}{\mathrm{NIR}+\mathrm{G}}$ | [33] | |

Red Normalized Vegetation Difference Index (NDVI_{REG}) | $\frac{\mathrm{NIR}-\mathrm{REG}}{\mathrm{NIR}-\mathrm{REG}}$ | [33] | |

SI | Salinity Index (SI-T) | $\frac{\mathrm{R}}{\mathrm{NIR}}\times 100$ | [34] |

Salinity Index 1 (SI1) | $\sqrt{\begin{array}{c}\mathrm{G}\times \mathrm{R}\end{array}}$ | [34] | |

Salinity Index 2 (SI2) | $\sqrt{\begin{array}{c}{\mathrm{G}}^{2}{+\mathrm{R}}^{2}{+\mathrm{NIR}}^{2}\end{array}}$ | [35] | |

Salinity Index 3 (SI3) | $\sqrt{\begin{array}{c}{\mathrm{G}}^{2}{+\mathrm{R}}^{2}\end{array}}$ | [35] | |

Normalized Difference Salinity Index (NDSI) | $\frac{\mathrm{R}-\mathrm{NIR}}{\mathrm{R}+\mathrm{NIR}}$ | [36] | |

Soil Remote Sensing Index (SRSI) | $\sqrt{{\left(\mathrm{NDVI}-1\right)}^{2}{+\mathrm{SI}1}^{2}}$ | [32] | |

BI | Brightness index (BI) | $\sqrt{{\mathrm{R}}^{2}{+\mathrm{NIR}}^{2}}$ | [36] |

Sample Set | Minimum (g/kg) | Maximum (g/kg) | Average (g/kg) | SD (g/kg) | Sample Size |
---|---|---|---|---|---|

All | 0.264 | 20.651 | 7.583 | 5.766 | 120 |

Modeling set | 0.277 | 20.675 | 7.575 | 5.735 | 90 |

Validation set | 0.258 | 20.250 | 7.627 | 5.864 | 30 |

Reflectance | Grey Correlation Coefficient | Pearson Correlation Coefficient |
---|---|---|

G | 0.567 ** | 0.532 ** |

R | 0.569 ** | 0.522 ** |

REG | 0.550 * | S0.509 * |

NIR | 0.612 ** | 0.557 ** |

G | R | REG | NIR | |
---|---|---|---|---|

R_{i} | 0.567 ** | 0.569 ** | 0.550 * | 0.612 ** |

σ_{i} | 0.791 | 0.761 | 0.470 | 0.732 |

P_{i} | 0.472 | 0.456 | 0.273 | 0.435 |

ID | Algebra Operation |
---|---|

1 | R+G+NIR |

2 | R-G-NIR, G-R-NIR, NIR-R-G |

3 | $\sqrt[3]{\mathrm{R}*\mathrm{G}*\mathrm{NIR}}$ |

4 | $\frac{\mathrm{R}}{\sqrt{\mathrm{G}*\mathrm{NIR}}},\frac{\mathrm{G}}{\sqrt{\mathrm{R}*\mathrm{NIR}}},\frac{\mathrm{NIR}}{\sqrt{\mathrm{R}*\mathrm{G}}}$ |

Spectral Index | Grey Correlation Coefficient | Pearson Correlation Coefficient |
---|---|---|

SSRI | 0.689 ** | 0.632 ** |

NDVI | 0.619 ** | 0.602 ** |

DVI | 0.601 ** | 0.557 ** |

SRVI | 0.512 * | 0.476 * |

RVI | 0.517 * | 0.458 * |

GNDVI | 0.557 ** | 0.514 * |

NDVI_{REG} | 0.507 * | 0.454 |

Salinity Index (SI-T) | 0.607 ** | 0.559 ** |

Salinity Index 1 (SI1) | 0.556 ** | 0.514 * |

Salinity Index 2 (SI2) | −0.390 | −0.200 |

Salinity Index 3 (SI3) | 0.637 ** | 0.601** |

NDSI | 0.535 * | 0.474* |

SRSI | 0.677 ** | 0.615** |

Brightness Index (BI) | 0.235 | 0.229 |

Modeling Method | Modeling Accuracy | Validation Accuracy | |||
---|---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | RPD | |

RF | 0.625 | 2.977 | 0.633 | 2.789 | 1.425 |

BPNN | 0.601 | 3.375 | 0.610 | 3.090 | 1.397 |

SVM | 0.584 | 3.547 | 0.591 | 3.274 | 1.363 |

PLSR | 0.557 | 3.645 | 0.566 | 3.455 | 1.321 |

MLR | 0.492 | 3.988 | 0.488 | 4.714 | 0.670 |

Modeling Method | Modeling Accuracy | Validation Accuracy | |||
---|---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | RPD | |

RF | 0.667 | 2.554 | 0.679 | 2.443 | 1.878 |

BPNN | 0.641 | 2.631 | 0.653 | 2.781 | 1.750 |

SVM | 0.619 | 3.205 | 0.621 | 3.029 | 1.549 |

PLSR | 0.633 | 2.980 | 0.639 | 2.991 | 1.583 |

MLR | 0.537 | 3.652 | 0.526 | 3.631 | 0.998 |

Modeling Method | Modeling Accuracy | Validation Accuracy | |||
---|---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | RPD | |

RF | 0.724 | 1.764 | 0.745 | 1.879 | 2.211 |

BPNN | 0.699 | 1.989 | 0.682 | 2.376 | 2.043 |

SVM | 0.665 | 2.554 | 0.658 | 3.002 | 1.675 |

PLSR | 0.671 | 2.275 | 0.689 | 2.897 | 1.748 |

MLR | 0.639 | 3.091 | 0.622 | 2.994 | 1.464 |

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Yu, X.; Chang, C.; Song, J.; Zhuge, Y.; Wang, A.
Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index. *Sensors* **2022**, *22*, 546.
https://doi.org/10.3390/s22020546

**AMA Style**

Yu X, Chang C, Song J, Zhuge Y, Wang A.
Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index. *Sensors*. 2022; 22(2):546.
https://doi.org/10.3390/s22020546

**Chicago/Turabian Style**

Yu, Xinyang, Chunyan Chang, Jiaxuan Song, Yuping Zhuge, and Ailing Wang.
2022. "Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index" *Sensors* 22, no. 2: 546.
https://doi.org/10.3390/s22020546