# Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling

^{1}

^{2}

^{3}

^{4}

^{*}

^{†}

## Abstract

**:**

^{2}

_{V}= 0.79, Root Mean Square Errors (RMSE

_{V}) = 1.51 dS·m

^{−1}, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Field Measurement

^{®}. An average from five readings at each sample point was calculated as the representative value of the apparent electrical conductivity.

#### 2.3. Selection of Spectral Indices and Sensitive Bands

- (1)
- (2)
- Spectral radiometric calibration and atmospheric and Geometric corrections were performed on the WV-2 [21].
- (3)
- The FLAASH model was used to eliminate atmospheric and adjacency effects for images using the Environmental for Visualizing Images (ENVI 5.3, EXELIS VIS) software package
^{®}[22]. The WV-2 image was resampled into 2-m resolution, and the River channel with water bodies was clipped out due to the inaccessibility for sampling.

_{1}and SI

_{2}), which are calculated from narrowband reflectance factor spectra. We identified the wavelengths or spectral indices (showed in Table 2). The optimized spectral indices (OSI) were applied to identify optimal wavelengths or indices. The spectral indices are defined as follows:

#### 2.4. Model Generation and Data Analysis

#### 2.5. Partial Least-Squares Regression

#### 2.6. Model Evaluation

- (1)
- A high coefficient of determination (R
^{2}), indicating a strong linear relationship. - (2)
- Low Root Mean Square Errors (RMSE) of the model’s variables, indicating that the low error between measured and predicted data were calculated by the equation listed in Table 3.
- (3)
- Relative Percent Deviation (RPD), indicating the predictive ability of the model. Its computation process is the ratio between standard deviation (SD) and standard error of prediction (SEP). According to the predictive ability of the model, the RPD is divided into three categories: (1) The value of RPD exceeds 2.0, indicating a model with better predictive ability. (2) The RPD values ranging from 1.4 to 2.0 represent a model with general predictive ability. (3) The RPD value is less than 1.4, indicating that it has poor predictive ability.

## 3. Results and Analysis

#### 3.1. Statistical Characteristics of the Sampling Data

^{−1}and 4.54 dS·m

^{−1}, and the standard deviation (SD) was 1.99 dS·m

^{−1}and 2.20 dS·m

^{−1}, respectively. The mean value from all field sampling points was 4.60 dS·m

^{−1}, which is between the mean value both of calibration and validation sets.

#### 3.2. Analysis Correlation between EC and Bands of Worldview2-Images

#### 3.3. Analysis Correlation between EC and Optimized Spectral Index

#### 3.3.1. Two-Dimensional Correlation Analysis

_{1}) selected in the study site were calculated from remote sensing data and an index that calculates all possible combinations as a pair of bands. Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 summarized the correlation between optimized spectral indices and EC based on different mathematical methods. The X and Y axes represent R

_{λ1}and R

_{λ2}, respectively, within the spectral region of 400–1040 nm. The color bar on the right indicates the mapping of the correlation coefficient values to the colormap. The upper and lower limits of the color bar are the maximum positive correlation coefficient and the maximum negative correlation coefficient, respectively.

_{(B6,B2)}, RI

_{(B6,B3)}, and SI

_{1(B8,B8)}) reached −0.45**, 0.45**, and 0.50**, respectively. In Figure 6, the optimized spectral indices based on R-FD data indicated a better correlation with EC. The correlation coefficient (p = 0.01) of optimum band combination indies (NDI

_{(B7,B1)}, RI

_{(B8,B7)}, and SI

_{1(B5,B3)}) reached −0.43**, −0.41**, and −0.54**, respectively.

_{(B6,B2)}, RI

_{(B6,B2)}, and SI

_{1(B8,B7)}) reached −0.45**, 0.45**, and −0.48**, respectively. In Figure 9, the optimized spectral indices based on 1/R data illustrated that the NDI and RI indice of band (B2, B3, B6, B7, and B8) combinations had a significant correlation with EC. The correlation coefficient (p = 0.01) of optimal band combination indices (NDI

_{(B7,B2)}, RI

_{(B5,B2)}, and SI

_{1(B8,B8)}) reached 0.45**, −0.46**, and 0.41**, respectively. The 1/R-FD data (Figure 10) illustrated that the optimized spectral indices (NDI and RI) provided the better correlation with EC than SI

_{1}index and that the maximum of correlation coefficient reached 0.41** (NDI

_{(B8,B5)}), 0.42** (RI

_{(B8,B3)}), and −0.34** (SI

_{1 (B6,B4)}).

_{1}were relatively scattered and not concerned, but the correlation was significantly improved. The bands of Worldview-2 image identified as important features in the VIS-NIR region corresponded to B2, B3, B6, B7, and B8. These bands’ combination is relatively related to the conductive elements in the soil.

#### 3.3.2. Three-Dimensional Correlation Analysis

_{2}) selected in the study site were calculated from remote sensing data (spectral bands B1–B8) and an index that calculates based on the three-band combination in all possible combinations. The three-dimensional (3D) maps (showed in Figure 11) contained the horizontal slice map and vertical slice map, and the slice maps indicated the correlation between the EC and the spectral index (SI

_{2}).

_{λ1}R

_{λ2}, and R

_{λ3}), respectively, within the spectral region of 400–1040 nm. The color bar on the right indicates the mapping of the correlation coefficient values to the colormap. The upper and lower limits of the color bar are the maximum positive correlation coefficient and the maximum negative correlation coefficient, respectively.

_{2}based on different mathematical methods were picked out. As can be seen from the slice maps, the combinations of the three-band usually inferred the better correlation. Thereinto, the most effective spectral indices (SI

_{2(B8,B8,B8)}, SI

_{2(B5,B4,B4)}, SI

_{2(B1,B5,B1)}, SI

_{2(B8,B8,B8)}, SI

_{2(B8,B8,B8)}, and SI

_{2(B7,B7,B6)}) based on different mathematical methods (R-FD, R-SD, R-SQ, 1/R, and 1/R-FD) had the significant correlation with maximum correlation coefficients of −0.48, −0.54, −0.35, −0.47, 0.44, and −0.34, respectively. Among all three-band indices, the Sqrt ((B5)

^{2}+ (B4)

^{2}+ (B4)

^{2}) based on R-FD data has the best correlation coefficient with −0.54. Comparing to 2D indies, the three-band combination index enriched the data set and expanded the range of EC-related data.

#### 3.4. Estimation PLSR Models and Evaluation

_{1}), and optimized Three-Band Indices (SI

_{2})). We applied all the best correlation coefficients in each form of the transformation algorithms to build estimation models for salt-affected land estimation. The evaluation of the estimation models is illustrated (shown in Figure 13).

_{R}, Band5

_{R-FD}, Band1

_{R-SD}, Band8

_{R-SQ}, Band8

_{1/R}, and Band6

_{1/R-FD}) indicates the low RPD values of 1.52 and 1.65. The PLSR models (I-PLSR, II-PLSR, III-PLSR, IV-PLSR, V-PLSR, and VI-PLSR) and SI2-PLSR model were established in 2-dimensional and 3-dimensional data (2D and 3D), and the contrast of estimation models, indicating that predictive ability and stability of models in 2D and 3D, has been made better than in the Raw-PLSR model (shown in Table 4). The predictive ability of the β-PLSR performed the best result. The β-PLSR model has the highest R

^{2}

_{V}value with 0.79 and the lowest RMSE

_{V}value with 1.51 dS·m

^{−1}and RPD value with 2.01. With the classification rule of RPD, it is the most effective model in this study site and the best-implemented model that met all the model selection and validation criteria and that was used to predict and map the spatial variation in soil salinity.

#### 3.5. Soil Salinity Maps with EC Data

^{−1}(non-saline soil (0–2), slightly saline soil (2–4), moderately saline soil (4–8), and strongly saline soil (>8)). The salt-affected soil that is characterized by low or no-vegetation coverage demonstrated high EC values, and the higher the vegetation coverage, the lower the EC value (Figure 14). On the whole, the strongly EC value primarily occurs on both sides of the Keriya River. It also appears that the vegetation coverage played an imperative role in preventing soil salinization.

## 4. Discussion

#### 4.1. Application of Multidimensional Modeling with Different Algorithm

^{2}= 0.43, R

^{2}= 0.39). Combined with indices (SI, OLI-SI, and NDVI) and images (Landsat TM and ASTER), and Allbed A [13] indicated the possibility of applying IKONOS image and spectral indices (SAVI, NDSI, and SI-T) in the prediction of soil salinization using a stepwise regression method, and the results yielded R

^{2}= 0.65 and RMSE = 3.9 dS·m

^{−1}.

^{2}

_{V}= 0.79, RMSE

_{V}= 1.51 dS·m

^{−1}, and RPD = 2.01, which could monitor and map the salt-affected land successfully in Keriya River regions.

#### 4.2. Estimation of Salt-Affected Land in Arid and Semiarid Regions

## 5. Conclusions

_{1}and SI

_{2}). Among these indices, the SI

_{1}(Sqrt(B3

^{2}× B5

^{2})) based on R-FD data has a higher correlation coefficient of −0.54. For the all three-band indices, the combinations of three bands usually revealed better correlation coefficients, and the SI

_{2}(Sqrt(B5

^{2}+ B4

^{2}+ B4

^{2})) based on R-FD data has the best correlation coefficient of −0.54, indicating that leading into the red edge could enhance the sensitivity of the OSI to EC.

^{2}

_{V}= 0.79, RMSE

_{V}= 1.51 dS·m

^{−1}, and RPD = 2.01, which show the best state among the ten PLSR models. The field-tested results of soil salinity used to detect soil salinity based on optimal spectral index results in are good correlation with each other. Then, the finding of this study should help to control soil salinization and are further effective for soil restoration and land reclamation of this region and other similar arid regions.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**Correlation between EC and bands selected from Worldview-2 image: (

**a**–

**c**) the correlation results based on original data, first derivative (R-FD) data, and second derivative (R-SD) data, respectively, and (

**d**–

**f**) the correlation results based on reciprocal inverse (1/R) data, square data (R-SQ) data, and reciprocal first derivative (1/R-FD) data, respectively. Note: * a means pass the significant test (0.05), ** a means pass the significant test (0.01).

**Figure 5.**The correlation analysis between EC and optimized spectral indices based on original data (R). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 6.**The correlation analysis between EC and optimized spectral indices based on first derivative (R-FD). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 7.**The correlation analysis between EC and optimized spectral indices based on second derivative (R-SD). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 8.**The correlation analysis between EC and optimized spectral indices based on square data (R-SQ). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 9.**The correlation analysis between EC and optimized spectral indices based on reciprocal inverse (1/R). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 10.**The correlation analysis between EC and optimized spectral indices based on reciprocal first derivative (1/R-FD). (

**a**–

**c**) represent the correlation EC with NDI, RI, and SI, respectively.

**Figure 11.**The correlation analysis between EC and optimized spectral indices based on different mathematical algorithms. (

**a**–

**r**) represent the horizontal slice map and vertical slice map, and the slice maps indicated the correlation between the EC and the spectral index (SI

_{2}), respectively.

**Figure 12.**The correlation analysis between EC and optimized spectral indices based on reciprocal first derivative. (

**a**–

**f**) represent the correlation EC with SI2, respectively.

**Figure 13.**The scatter plot of measured versus predicted EC. (

**a**) the result of Raw-I-PLSR model, (

**b**) the result of Raw-II-PLSR model, (

**c**–

**h**) the results of (I-VI) PLSR models, (

**i**) the result of α-PLSR model, and (

**j**) the result of β-PLSR model.

**Figure 14.**The soil salinity maps with EC data: (

**a**) sampling area and EC inversion based on the optimal PLSR model and (

**b**) sampling area based on Normalized Difference Vegetation Index (NDVI) inversion.

Bands | Wavelength (nm) | Resolution |
---|---|---|

Coastal | 400–450 | Multispectral: 1.85 m GSD at nadir, 2.07 m GSD at 20° off-nadir. |

Blue | 450–510 | |

Green | 510–580 | |

Yellow | 585–625 | |

Red | 630–690 | |

Red Edge | 705–745 | Panchromatic: 0.46 m GSD at nadir, 0.52 m GSD at 20° off-nadir. |

Near-IR1 | 770–895 | |

Near-IR2 | 860–1040 |

Optimized Spectral Index | Abbreviation | Equation | Reference |
---|---|---|---|

Ratio index | RI | R_{λ1}/R_{λ2} | [23] |

Normalized difference index | NDI | (R_{λ1} − R_{λ2})/(R_{λ1} − R_{λ2}) | |

Soil salinization index1 | SI_{1} | Sqrt (R_{λ1}^{2} × R_{λ2}^{2}) | |

Soil salinization index2 | SI_{2} | Sqrt (R_{λ1}^{2} +R_{λ2}^{2} + R_{λ3}^{2}) | [11] |

_{1}, λ

_{2}, and λ

_{3}are wavelengths in nanometers (nm).

Index | Equation |
---|---|

Coefficient of Determination | R^{2} = ${\left[\frac{{{\displaystyle \sum}}_{i=1}^{N}\left({x}_{i}-\overline{x}\right)\left({y}_{i}-\overline{y}\right)}{\sqrt{{{\displaystyle \sum}}_{i=1}^{N}{\left({x}_{i}-\overline{x}\right)}^{2}+{{\displaystyle \sum}}_{i=1}^{N}{\left({y}_{i}-\overline{y}\right)}^{2}}}\right]}^{2}$ |

Root Mean Square Error | RMSE = $\sqrt{\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathsf{\gamma}}_{\mathrm{i}}-{\mathsf{\beta}}_{\mathrm{i}}\right)}^{2}}{\mathrm{n}}}$ |

Relative Percent Deviation | RPD = SD/SEP |

Type | Acronym | Parameters | R^{2}_{c} | RMSE_{c} | R^{2}_{v} | RMSE_{v} | RPD |
---|---|---|---|---|---|---|---|

OD | Raw-I-PLSR | 4 | 0.45 | 1.81 | 0.42 | 1.93 | 1.52 |

Raw-II-PLSR | 3 | 0.49 | 1.84 | 0.49 | 1.96 | 1.65 | |

2D | I-PLSR | 3 | 0.58 | 1.74 | 0.56 | 1.83 | 1.76 |

II-PLSR | 0.76 | 1.76 | 0.72 | 1.79 | 1.96 | ||

III-PLSR | 0.42 | 1.82 | 0.40 | 1.98 | 1.51 | ||

IV-PLSR | 0.58 | 1.82 | 0.55 | 1.91 | 1.78 | ||

V-PLSR | 0.63 | 1.75 | 0.61 | 1.78 | 1.87 | ||

VI-PLSR | 0.43 | 1.97 | 0.39 | 2.05 | 1.31 | ||

3D | α-PLSR | 4 | 0.69 | 1.73 | 0.65 | 1.74 | 1.89 |

β-PLSR | 3 | 0.80 | 1.40 | 0.79 | 1.51 | 2.01 |

_{1}) are optimized based on raw data and its mathematical transformation algorism (R-FD, R-SD, 1/R, R-SQ, and 1/R-FD). C represents calculation, V represents validation, and PLSR represents Partial least squares regression.

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## Share and Cite

**MDPI and ACS Style**

Kasim, N.; Maihemuti, B.; Sawut, R.; Abliz, A.; Dong, C.; Abdumutallip, M. Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling. *Water* **2020**, *12*, 880.
https://doi.org/10.3390/w12030880

**AMA Style**

Kasim N, Maihemuti B, Sawut R, Abliz A, Dong C, Abdumutallip M. Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling. *Water*. 2020; 12(3):880.
https://doi.org/10.3390/w12030880

**Chicago/Turabian Style**

Kasim, Nijat, Balati Maihemuti, Rukeya Sawut, Abdugheni Abliz, Cui Dong, and Munira Abdumutallip. 2020. "Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling" *Water* 12, no. 3: 880.
https://doi.org/10.3390/w12030880