Spatial distribution and genesis of salt on the saline playa at Qehan Lake, Inner Mongolia, China

Han, Lijing; Liu, Dongwei; Cheng, Guoshuai; Zhang, Guangchuang; Wang, Lixin

doi:10.1016/j.catena.2019.01.040

Cited by 42 publications

(14 citation statements)

References 51 publications

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“…(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.…”

Section: Correlation Between Soil Electrical Conductivity and Remote Sensing Indexmentioning

confidence: 99%

“…In order to obtain detailed information of soil moisture and salinity distribution, traditional measurements of soil properties require large numbers of experimental samples, long-term observation of soil characteristics and surrounding vegetation information as support, which requires a huge amount of manpower, financial resources and time [6] and is not suitable for long-term monitoring due to the influence of weather. For example, in the case of bad weather conditions (rainstorm, strong wind, etc.…”

Section: Introductionmentioning

confidence: 99%

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Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

Wang

et al. 2021

Water

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In arid and semi-arid regions, soil moisture and salinity are important elements to control regional ecology and climate, vegetation growth and land function. Soil moisture and salt content are more important in arid wetlands. The Ebinur Lake wetland is an important part of the ecological barrier of Junggar Basin in Xinjiang, China. The Ebinur Lake Basin is a representative area of the arid climate and ecological degradation in central Asia. It is of great significance to study the spatial distribution of soil moisture and salinity and its causes for land and wetland ecological restoration in the Ebinur Lake Basin. Based on the field measurement and Landsat 8 satellite data, a variety of remote sensing indexes related to soil moisture and salinity were tested and compared, and the prediction models of soil moisture and salinity were established, and the accuracy of the models was assessed. Among them, the salinity indexes D1 and D2 were the latest ones that we proposed according to the research area and data. The distribution maps of soil moisture and salinity in the Ebinur Lake Basin were retrieved from remote sensing data, and the correlation analysis between soil moisture and salinity was performed. Among several soil moisture and salinity prediction indexes, the normalized moisture index NDWI had the highest correlation with soil moisture, and the salinity index D2 had the highest correlation with soil salinity, reaching 0.600 and 0.637, respectively. The accuracy of the BP neural network model for estimating soil salinity was higher than the one of other models; R2 = 0.624, RMSE = 0.083 S/m. The effect of the cubic function prediction model for estimating soil moisture was also higher than that of the BP neural network, support vector machine and other models; R2 = 0.538, RMSE = 0.230. The regularity of soil moisture and salinity changes seemed to be consistent, the correlation degree was 0.817, and the synchronous change degree was higher. The soil salinity in the Ebinur Lake Basin was generally low in the surrounding area, high in the middle area, high in the lake area and low in the vegetation coverage area. The soil moisture in the Ebinur Lake Basin slightly decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. However, the spatial distribution of soil moisture had a higher mutation rate and stronger heterogeneity than that of soil salinity.

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Section: Correlation Between Soil Electrical Conductivity and Remote Sensing Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

Wang

et al. 2021

Water

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“…The higher the salinization, the higher the spectral reflectance of each band will increase accordingly 13 , therefore, it becomes possible to monitor salinization using raw bands or derived spectral indices of remote sensing. In previous studies on salinity monitoring, the choice of environmental variables varied, such as direct use of salinity indices for estimating soil salinity 63 , indirect estimation of soil salinity using vegetation indices 64 , or combining multiple environmental variables and grouping them to predict comparisons 58 .…”

Section: Discussionmentioning

confidence: 99%

Investigation of the spatial and temporal variation of soil salinity using Google Earth Engine: a case study at Werigan–Kuqa Oasis, West China

Xie

et al. 2023

Sci Rep

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Large-scale soil salinity surveys are time-costly and labor-intensive, and it is also more difficult to investigate historical salinity, while in arid and semi-arid regions, the investigation of the spatial and temporal characteristics of salinity can provide a scientific basis for the scientific prevention of salinity, With this objective, this study uses multi-source data combined with ensemble learning and Google Earth Engine to build a monitoring model to observe the evolution of salinization in the Werigan–Kuqa River Oasis from 1996 to 2021 and to analyze the driving factors. In this experiment, three ensemble learning models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were established using data collected in the field for different years and some environmental variables, After the accuracy validation of the model, XGBoost had the highest accuracy of salinity prediction in this study area, with RMSE of 17.62 dS m−1, R2 of 0.73 and RPIQ of 2.45 in the test set. In this experiment, after Spearman correlation analysis of soil Electrical Conductivity (EC) with environmental variables, we found that the near-infrared band in the original band, the DEM in the topographic factor, the vegetation index based on remote sensing, and the salinity index soil EC had a strong correlation. The spatial distribution of salinization is generally characterized by good in the west and north and severe in the east and south. Non-salinization, light salinization, and moderate salinization gradually expanded southward and eastward from the interior of the western oasis over 25 years. Severe and very severe salinization gradually shifted from the northern edge of the oasis to the eastern and southeastern desert areas during the 25 years. The saline soils with the highest salinity class were distributed in most of the desert areas in the eastern part of the Werigan–Kuqa Oasis study area as well as in smaller areas in the west in 1996, shrinking in size and characterized by a discontinuous distribution by 2021. In terms of area change, the non-salinized area increased from 198.25 in 1996 to 1682.47 km2 in 2021. The area of saline soil with the highest salinization level decreased from 5708.77 in 1996 to 2246.87 km2 in 2021. overall, the overall salinization of the Werigan–Kuqa Oasis improved.

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“…The Landsat-8 OLI image covering the study area acquired on 18 April 2021 was obtained from the Landsat-8 Level-1 product provided by the United States Geological Survey ( , accessed on 29 April 2021) [ 41 ]. To decrease the effects of atmospheric scattering on image quality, we performed radiation and atmospheric correction [ 45 ]. Then the study area with nearly 600,000 ha was clipped from the processed image, and we performed supervised classification using the ENVI v5.3 software.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Sun

Jiang

et al. 2022

Sensors

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Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.

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Spatial distribution and genesis of salt on the saline playa at Qehan Lake, Inner Mongolia, China

Cited by 42 publications

References 51 publications

Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

Investigation of the spatial and temporal variation of soil salinity using Google Earth Engine: a case study at Werigan–Kuqa Oasis, West China

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

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