Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Abstract

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity 24validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape

“…However, the reflectance changed proportionally as the salt concentrations increased in the soil samples from 26 to 507 dS/m (Table 3). This observation is consistent with many other studies indicating the importance of the spectral domain between 1200 and 2500 nm for soil salinity characterization [18,[52][53][54]. Table 3.…”

“…In addition, the spatiotemporal dynamic nature of soil salinity makes it very difficult to use conventional methods for comparisons over large territory [5]. Several authors have examined the advantages of remote sensing methods and sensors for the assessment of soil degradation due to salinity [1, [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Remote sensing methods are relatively easy to apply and reliable in certain conditions.…”

Mapping Soil Salinity in Western United Arab Emirates (UAE) Using Satellite Data

“…The NDSI based model performed accurately in predicting and mapping medium salinity levels (100%) and low saline soils (33%)). The SSSI-1 and SSSI-2 based models in this study demonstrated conflicting results to those shown in [49].…”

“…Statistical and mathematical models have been developed to model vegetation canopies [47], soil moisture [48] and soil salinity [46], [49]. A widely used technique in the estimation of soil salinity in arid and semi-arid regions is the application of empirical and semi-empirical soil salinity models [46], [49].…”

Quantitative Research on the Relationship between Salinity and Crack Length of Soda Saline-Alkali Soil

“…The remote-sensing method is also known as an effective approach for determining the spatial distributions of soil salinity, classifying the soil salinity levels due to the diagnostic spectral characteristics of different salt minerals [11][12][13][14]. However, it is often used only in a qualitative way because of the mixed pixel problems and the low spectral resolution of most remote-sensing data [15][16][17]. Therefore, it is urgent and important to develop a non-destructive and rapid method to measure the salt content based on the chemical and physical properties of the saline soils.…”