*3.1. Quantitative Analysis Results of Soil Electrical Conductivity and Remote Sensing Index* 3.1.1. Correlation between Soil Electrical Conductivity and Remote Sensing Index

The correlation between remote sensing index and soil conductivity is given in Table 2. From Table 2, it can be deduced that: (1) The correlation between B1, B2, B3, B4 in Landsat 8 satellite data and soil salinity was greater than 0.4, so we chose these three bands to build the new salinity indices D1 and D2. (2) The salinity index D2 established for this study had the highest correlation with soil conductivity, followed by OLI\_SI. The indexes with correlation coefficients greater than 0.6 were also D1 and Landsat 8 band 1, and their significance was less than 0.001. (3) In Landsat 8 OLI bands, bands 1 and 2 had the highest correlation with soil conductivity, followed by bands 3, 6 and 7, which had almost no correlation. (4) The correlation between vegetation index and soil conductivity was less than 0.4, and the correlation was poor. (5) The correlation between intensity indexes Int1, Int2 and soil conductivity was 0.467 and 0.414, respectively, with good significance, but the correlation of brightness index was poor. (6) The correlation between salinity index and soil conductivity was uneven. The correlation between OLI\_SI, S4, S5, D1, D2 and soil conductivity was higher than 0.5, with good significance, while the correlation between S6 was very low and not significant.

**Table 2.** Correlation coefficients between soil electrical conductivity and remote sensing indexes.


Note: B1, B2, B3, B4, B5, B6 and B7 are Landsat 8 coastal band, blue band, green band, red band, near infrared band, short wave infrared 1 and shortwave infrared 2, respectively; \* *p* < 0.05; \*\* *p* < 0.01.

#### 3.1.2. Regression Model of Soil Electrical Conductivity

The salinity index D2 was selected as the remote sensing index with the highest correlation and significant correlation with soil electrical conductivity. The regression model with soil conductivity was established. The specific parameters of regression analysis are shown in Table 3, including model formula, fitting accuracy and verification error.

In Table 3, it can be observed that: (1) The regression model of salinity index D2 and soil conductivity had good fitting accuracy. Except for the exponential model, the fitting R<sup>2</sup> of other models was greater than 0.5, and the validation error RMSE was less than 0.1 S/m. (2) The BP neural network model had the best fitting accuracy—R<sup>2</sup> was 0.624, RMSE was 0.0830 S/m (Figure 2a, verification figure)—followed by cubic function model, where R<sup>2</sup> was 0.620, and RMSE was 0.0834 S/m. (3) The fitting accuracy of linear function, quadratic function and SVR model was also greater than 0.6, and RMSE was less than 0.09. (4) In addition, it can be found that although the fitting accuracy of the cubic function model and the quadratic function model was high, the regression coefficients showed the over-fitting phenomenon, and the significance was not high. Therefore, in the final model selection, these two models were excluded. We selected the BP neural network model.



Note: Sig is the significance of the regression coefficient. If its value is 0.01 < Sig < 0.05, the difference is significant, and if Sig < 0.01, the difference is extremely significant. Sig1, Sig2, Sig3, Sig4 are the significances of the first, second, third and fourth coefficient, respectively.

**Figure 2.** (**a**) Comparison of measured and predicted soil conductivity; (**b**) comparison of measured and predicted soil moisture.

*3.2. Quantitative Analysis Results of Soil Volumetric Moisture and Remote Sensing Index*

#### 3.2.1. Correlation between Soil Volumetric Moisture and Remote Sensing Index

The correlation between remote sensing index and soil volumetric moisture is presented in Table 4. From Table 4, it can be inferred that: (1) NDWI had the highest and significant correlation with soil volumetric moisture, with R of 0.600, followed by the humidity component of the Spike cap transformation, with R of 0.572. (2) The correlation between Landsat 8 band and soil volumetric moisture was on a general level, B1 and B2 had better correlation, bands 10–11 had almost no correlation, B1–4 had positive correlation, and B5–B7 had negative correlation. (3) Vegetation indices NDVI, EVI, SAVI and VCI negatively correlated with soil volumetric moisture, with good correlations of −0.476, −0.442, −0.498 and −0.476, respectively. (4) LSWI had a low correlation with soil volumetric moisture, while TVDI, VSDI and ATI had poor and insignificant correlation. LST had almost no correlation with surface temperature.


**Table 4.** Coefficient of correlation between soil volumetric moisture and remote sensing indexes.

Note: \* *p* < 0.05, \*\* *p* < 0.01.

#### 3.2.2. Regression Model of Soil Volumetric Moisture

Among the remote sensing indexes selected by this research institute, NDWI had the highest correlation with soil volumetric moisture. Therefore, the regression model of soil volumetric moisture was established using NDWI. The exact parameters and accuracy of the regression model are shown in Table 5. In Table 5, we can see that: (1) The fitting accuracy of the cubic function model was the best—R2 was 0.538 and RMSE was 0.230 followed by the SVR model and the quadratic function model. (2) The SVR model was not suitable as an inversion model because the fitting R<sup>2</sup> was high, but the verification error RMSE was the largest. (3) The fitting accuracy of exponential function was the lowest, and the fitting effect of the primary function was not good. (4) The fitting R<sup>2</sup> of the quadratic function was close to that of the cubic function, but the RMSE of the cubic function was smaller (Figure 2b for verification). The significance of the regression parameters was not very different. Therefore, the cubic function was chosen as the remote sensing inversion model of soil volumetric moisture.



## *3.3. Correlation Analysis of Soil Moisture and Salinity*

The correlation analysis between soil electrical conductivity and volumetric moisture is shown in Figure 3. From Figure 3, it can be learned that the rules of variation of soil electrical conductivity and soil volumetric moisture tended to be the same, and the correlation degree was 0.817, which meat the synchronous change degree was higher. In other words, the correlation degree between them was higher. In addition, the change rate of soil volumetric moisture at most observation points was higher than that of soil electrical conductivity. The spatial variation rate of soil volumetric moisture was high, and its heterogeneity was strong.

**Figure 3.** Simultaneous change of soil electrical conductivity and volume of soil water.

#### **4. Discussion**

#### *4.1. Different Rules between Remote Sensing Index and Soil Moisture and Salinity*

The correlation between soil electrical conductivity and Landsat 8 bands gradually decreased from B1 to B7, and there was almost no correlation between soil electrical conductivity and B6 and B7. The spectral reflectance of soil electrical conductivity increased with the increase in soil salinity. The 325–600 nm reflectance curve rose sharply with a large amount of information; the 600–1015 nm reflectance curve was flat with no obvious absorption and less information content [52]. Therefore, the correlation of Landsat 8 B1–B4 was significant, and the correlation of the B1 was the greatest. Vegetation index was negatively correlated with soil conductivity; that is, vegetation index was negatively correlated with soil salinity. Vegetation growth is susceptible to soil salinity stress, resulting in impaired photosynthesis and respiration [53]. There are only few salt-tolerant plants in the Ebinur Lake Basin, so the vegetation coverage in the areas with high soil salinity is very low. The purpose of salinity index is to highlight the information of surface salinity, so there is a high correlation between vegetation index with soil conductivity. The correlation between OLI\_SI, D1, D2 and soil conductivity was greater than that of other salinity indices. These three indices were proposed for Landsat 8. The expression contained band 1 of Landsat 8, and the correlation between band 1 and soil conductivity was also higher than that of the other bands. Therefore, band 1 should be included in the subsequent study of soil salinity index for Landsat 8.

Soil volumetric moisture was positively correlated with B1–B4 of Landsat 8, and negatively correlated with B5–B7. The reason is that the reflectance spectrum of soil moisture rises rapidly at 300–750 nm with a large amount of information and tends to increase gently at 800–1350 nm with a small amount of information. There are also two steep upward slopes at 1500–1800 nm and 2100–2400 nm [38]. Water index can highlight surface moisture, with a reliable indicator effect on soil moisture. It has a significant positive correlation with soil volume moisture, especially the NDWI and humidity components of the Spike-cap transformation. In theory, the drought index and surface temperature are negatively correlated with soil moisture [12,13], but this study found that drought index and surface temperature were not correlated with soil moisture, so it is not feasible to use drought index to reflect soil moisture laterally. Drought index and surface temperature may affect soil moisture in time resolution. Generally, vegetation index is positively correlated with soil moisture [10], but in this study, vegetation index was negatively correlated with soil volumetric moisture. The reason is that the Ebinur Lake Basin is a high salinity area, where the soil contains too much salt, so that it is difficult for the vegetation to absorb water, and the growth is limited. In addition, the correction analysis of soil water and salt also validated the analysis. The change of soil water and salt was consistent, and soil salinity seriously affected the change of soil water.

#### *4.2. Spatial Distribution of Soil Salt in Ebinur Lake Basin*

The spatial distribution map of soil salinity in the Ebinur Lake Basin was obtained by model inversion, as shown in Figure 4a. From the figure, it can be seen that the soil salinity in the Ebinur Lake Basin was low around, high in the center, high in the lake area and low in the vegetation coverage area. The soil salinity in the lake area decreased gradually outward. In addition, the salt content along the coast of the Ebinur Lake, Boltala River, Jinghe River, Kuitun River and Akeqisu River is higher than that of other areas. Among them, it was the most obvious in the case of the Ebinur Lake and the Akezisu River. According to the correction analysis of soil water and salt in the Ebinur Lake Basin, the change trend of soil salinity and soil moisture tends to be the same, so in the vicinity of the water area, the soil moisture content is higher than in other areas. There are two distinct white areas in the southeastern part of the Ebinur Lake; namely, the soil electrical conductivity there is greater than 0.4 S/m. These two areas are the Jinghe Salt Field and the Jinghe Old Salt Field respectively, which further confirms the accuracy of the model inversion. The Ebinur Lake is a saltwater lake. The area of the lake is decreasing year by year. The water around the lake is gradually evaporating, and the soil salinity is gradually increasing.

**Figure 4.** (**a**) Spatial distribution of soil electrical conductivity (S/m); (**b**) spatial distribution of soil volumetric moisture (m<sup>3</sup> m−<sup>3</sup> ).

From the above analysis, we can see that the soil salt content is negatively correlated with vegetation coverage. It can be learned that the soil salt content in vegetation coverage area is lower than that in bare land or wetland. In addition, soil salinity of the northern mountain forest is lower than that of the southwestern farmland. In addition to the cause of farmland fertilizer application, the high soil salinity will inhibit the growth of vegetation, so it is also suitable for planting trees and grasslands under the extremely low salinity soil. The area with soil electrical conductivity lower than 0.05 S/m in the eastern part of the country is the Arxi Sea grassland, which further validates the above discussion.

Salinization is part of the main causes of soil degradation in arid and semi-arid regions of the world. It inhibits plant growth and agricultural production and aggravates soil erosion. Nearly 20% of land in China is influenced by salinization, which is increasing with human activities, especially in arid and semi-arid areas [54]. As a typical arid and semi-arid region and an important geographic unit in central Asia, monitoring and mapping of the Ebinur Lake Basin soil salinity over a long period of time and wide space are of great significance for curbing soil degradation and sustainable agricultural production.

#### *4.3. Spatial Distribution of Soil Moisture in Ebinur Lake Basin*

The spatial distribution of soil moisture in the Ebinur Lake Basin was retrieved from the cubic regression model, as shown in Figure 4b. As can be seen on the map, soil moisture

in the Ebinur Lake Basin gradually decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. There were many white spots (i.e., soil volumetric moisture > 2 m<sup>3</sup> m−<sup>3</sup> ). In the southwest of the basin, the bright spot was the Panqiao fishpond and in the northwest, paddy fields and reservoirs. This area was the same as the lake, so the soil moisture in this area was very high. Soil volumetric moisture along the coast of the Ebinur Lake, recharged by lake water, was 1–2 m<sup>3</sup> m−<sup>3</sup> . The soil moisture in this kind of area is also higher than that in other areas. Because of poor water storage capacity of inland saline soil, the soil moisture of inland saline soil is lower than that of other regions. The soil moisture along the four rivers is 1–2 m<sup>3</sup> m−<sup>3</sup> , which conforms to the basic natural law.

Soil moisture content of the farmland in the west is low, and the type of soil is inland saline soil, which is not conducive to the growth of crops, but affected by topography, it developed into farmland. Under the influence of water stress, the yield of crops is low. Soil water loss is a major restrictive factor for land degradation in arid and semi-arid areas [55]. Vegetation is very vulnerable to water stress, which has a huge impact on agricultural production [56]. Therefore, timely and accurate dynamic grasp of soil moisture changes in arid and semi-arid areas is of great significance for ecological development.

#### *4.4. Relevant Rules of Soil Water and Salt*

According to the correlation analysis of soil water and salt in the Ebinur Lake Basin, the change trend of the two tended to be consistent. Depending on the physical characteristics of soil, soil water is the carrier of soil salt transport. As can be seen in Figure 4, the soil salinity was extremely high and the soil moisture was also large around the Ebinur Lake, and the two changes were related. In the eastern, southern and northwestern parts of the basin, the conductivity of soil volumetric was less than 0.05 S/m, the salinity was very low, but the soil moisture content was high. Soil moisture content was high, which has a definite dilution effect on soil salinity. Soil water and salt are related and interrelated. Especially in arid and semi-arid areas, the change of soil water and salt is one of the controlling factors in the formation of saline land [56]. It is of great significance to study the correlation and linkage effects of soil water and salt for soil restoration and inhibition of land desertification and degradation in arid areas.

#### *4.5. Data Accuracy Discussion*

The field experiment for this study was carried out in the Ebinur Lake in May, and the measurement were performed for only one year. Although multiple measurements were taken for each measurement point and their average value was taken into consideration, the inversion model of this study is not universal due to the lack of long-term continuous observation data. In arid areas, soil moisture and salt content change with the year and season. As a result, the spectral characteristics of the ground surface will change, the inversion index will also change, and the final inversion model and results will also be different. Therefore, the important direction for the future research is to study the influence of different measurement periods on the selection of soil water content and salinity inversion index, and to explore whether there is an inversion model that is more universal for all time periods.

#### **5. Conclusions**

In this study, based on a series of field experiment data of soil salinity, soil moisture and remote sensing data (from Landsat 8 OLI), the remote sensing index for estimating soil water and salt content in the Ebinur Lake Basin were tested and compared, and two new salinity indices for Landsat 8 were developed. Good models for inversion of soil moisture and salinity in the Ebinur Lake Basin were tested and obtained. The spatial distribution of soil water and salt in the Ebinur Lake Basin was predicted using the remote sensing data. The research results of this paper have certain guiding significance for the future geophysical process modeling of water and salt transport in arid saline lake basins. The basic conclusions of this study are as follows:


**Author Contributions:** This paper was written by J.W., D.L. reviewed and improved the manuscript with comments; the data compilation and statistical analyses were completed by J.W., W.W., Y.H., S.T., D.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project is supported by Inner Mongolia major science and technology project (ZDZX2018054).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

