1. Introduction
Soil salinization is a major type of land degradation in arid and semi-arid areas [
1,
2], one which causes soil consolidation and crop yield decline, and thus results in huge losses in agricultural production. In addition, its mutual induction with soil desertification will cause more significant damage to the ecological environment and even cause serious geological disasters [
3,
4,
5,
6]. Soil salinization lasts for a long time, and the land encounters difficulties when it attempts to repair itself, which makes for a continuous impact on the human living environment and economic development [
7,
8,
9]. More than 100 countries and 7% of land area on a global scale are affected by land salinization [
10,
11]. It has become a worldwide environmental issue of wide-ranging concern, thus leading many countries to pay high attention to the amelioration and development of saline-alkaline areas. China is one of the countries seriously affected by salinization [
12]. Therefore, it is important to strengthen the dynamic monitoring of saline-alkaline areas to curb the source of land degradation, and to make rational use of land to improve the ecological environment.
The methods of saline-alkaline area monitoring can be currently divided into two types: instrument-measured soil data [
13,
14] and large-scale monitoring with remote sensing. With the development of spatial information technology, remote sensing has become the most widely used method in large-scale saline-alkaline area monitoring [
5,
15,
16,
17]. The methods for monitoring saline-alkaline areas based on remote sensing technology have mainly changed from visual interpretation to methods using computers to process image data and extract features [
18].
The exploration of saline-alkaline area identification methods based on spectral features has been a subject of frequent scholarly discussion. In 1992, Dwivedi [
19] performed experimental research on the best remote sensing bands combination for saline-alkaline areas monitoring, and concluded that the combination of bands 1, 3, and 5 of Landsat TM remote sensing images contained the largest amount of information, while the accuracy of a saline-alkaline area being identified was not proportional to the amount of information in the remote sensing data. Farifteh [
20] found that soil reflectance had a good response to the salinity of a soil surface layer when using hyperspectral data for soil salinization classification, and concluded that there was a linear relationship between soil salinization and its spectral reflectance. By correlating the spectral parameters from MODIS images with salinization levels, Bouaziz et al. [
21] constructed a linear spectral unmixing (LSU) model to examine the status of soil salinization in semi-arid areas. Xiao Dong [
22] et al. obtained reflectance and salinity data by field sampling to construct an inversion model and a correction model. Yanhua Fu [
23] constructed a model indicating the relationship of spectral data and salt content, and of organic matter content and PH level.
Research efforts using indirect features are mainly used to verify the saline-alkaline soil distribution with the help of some other auxiliary information. For example, the growth condition of vegetation can be affected by salinity; thus, vegetation is a good indirect indicator of salinity [
24]. Some salt-tolerant vegetation can also be one of the salinization signs. On the ecological scale, soil salinity can adversely limit species diversity and species’ ecological niches [
25]. Salinity is especially associated with negative osmotic potential, which inhibits seed germination and debilitates cell turgidity [
26]. R. L. Dehaan et al. [
27] demonstrated that the growth and distribution of vegetation had a strong correlation with soil salinity. By developing the normalized difference vegetation index–salinity index (NDVI–SI) feature-space remote sensing model of soil salinization, Wang et al. [
28] successfully monitored the change of saline soil in the Tarim Basin, Xinjiang.
Although these two methods have attained some achievements, how to effectively identify saline-alkaline areas with high accuracy is still the focus of present research.
Minqin County in Gansu Province, China is located at the junction of the Tengger Desert and the Badain Jaran Desert [
29], where land degrades seriously. Since the middle of the 20th century, Qingtu Lake, which is located in the deepest part of the two deserts, has gradually dried up. In the 1970s, Minqin County started to use a large amount of groundwater, which caused soil salinization. If it continues this seriously, it will eventually lead to the merger of the Tengger Desert and the Badain Jaran Desert, which will directly affect the geomorphology, climate, and human environment of the northwest region and even threaten the survival of local peoples [
30].
Although scholars have attained some achievements of quantitative monitoring of land cover using remote sensing, there is little research on the application of remote sensing data to the identification and monitoring of saline-alkaline areas at present. Traditional saline-alkaline area identification methods only rely on the selection of a single feature parameter, which is difficult to adapt to the optimal classification effect. To solve the problem of low accuracy of saline-alkaline area identification based on the traditional spectral indices, taking Minqin County as the study area, we propose a high-precision method of saline-alkaline area identification using multi-source data. By analyzing the trends and reasons of changes in saline-alkaline areas in the Minqin oasis, the objective of this paper is to provide a reference for the timely monitoring of saline-alkaline areas and ecological environment construction globally in arid areas.
4. Conclusions
We use multi-source data for saline identification in Minqin County and draw the following conclusions:
(1) The proposed method is effective in saline identification.
Based on multi-source data, we use a decision tree classification method to extract saline-alkaline areas by constructing three features: spectral indexes, textures, elevations, and slopes. The results show that the accuracy of saline-alkaline area identification is 88.11%, which is 7.69% greater than the traditional salinity indices, indicating the effectiveness of the proposed method.
(2) The multi-source data can help to identify features and improve accuracy.
GF-6 data are beneficial to the improvement of the accuracy of saline-alkaline area identification. In which, band 3 and band 7 are important to the saline-alkaline area identification in the study area.
High spatial resolution of GF-2 data can provide rich texture information, thus reducing the mistakes of distinguishing or misclassifying between features due to “different features with the same spectrum” or “different features with the same spectrum”.
The height and slope from DEM can quantify the topography of the study area, which is also helpful for identifying features and improving the classification accuracy.
(3) Monitoring and prevention of unused land in the study area are necessary.
With 20.4% of the land considered to be within a saline-alkaline area, soil salinization in Minqin County is a serious concern, especially in the northwestern areas. Therefore, we should strengthen the monitoring and prevention of unused land to prevent further soil salinization.
In summary, based on the previous studies, we proposed a high-precision saline-alkaline area identification method based on multi-source data. The results demonstrate the effectiveness of the method, thus solving the current problem of low accuracy of saline-alkaline area identification, a solution which may be applied to large-scale saline-alkaline area monitoring in the future. Meanwhile, it should be noted that, although the decision tree classification method achieved better classification results in this study, the significance of selected feature variables and grading criteria need to be further studied and improved to make the discriminative rules and classification results more realistic. Therefore, the research on the identification and classification of soil salinization in arid zones needs to be further developed.