Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

Abstract

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R 2 = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on OPEN ACCESSRemote Sens. 2014, 6 1138 regression analysis and remote sensing data is a promising approach, as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken in the early stages to prevent soil salinity from becoming prevalent, sustaining agricultural lands and natural ecosystems.

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

“…The SI was utilized in this study because: (1) the identification of the spectral response model of saline soils is effective in the separation of saline soil types [7]; and (2) the red waveband (620~680 nm) of ETM+ image is sensitive to soil salinity [9]. Studies [7,40] found that the blue and red wavebands of Landsat data performed high spectral reflectance for salt-affected soils at low moisture content.…”

“…Salt-affected soils can be discriminated using the visible and infrared portions of remote sensing spectra [2,[6][7][8][9]. A growing body of studies, aided by statistical analyses of field spectroscopy data and satellite remote sensing observations demonstrates that both multispectral [10][11][12][13][14][15] and hyperspectral passive reflectance data can be used to map soil salinization at landscape scales [16].…”

Modeling the contributing factors of desertification and evaluating their relationships to the soil degradation process through geomatic techniques

“…Dry areas are naturally prone to soil salinization due to a lower rate of rainfall and high evaporation which limits the leaching of salts, and this effect is expected to be magnified when it is combined with humans' negative intrusion like the over-fertilization of farmland (Metternicht and Zinck, 2009). Allbed et al (2014) found that the Salinity Index (SI) and red band (band 3) have significant correlation with electrical conductivity (EC). Though much research has been carried out in the field of soil salinity identification so far, through various models like SI, Normalized Differential Salinity Index (NDSI) etc., an extraction of soil salinity still needs more accuracy because of the confusion created from the same spectral signature values of settlement roofs and saline-affected zones in the study region.…”

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

“…The satellite images were processed with specific mathematical algorithms in all remotesensing techniques with the goal of the generation of useful information. The information mentioned can be integrated with other information and layers for the evaluation and interpretation of exploratory results (Li et al, 2015, Abedi et al, 2012Bonham-Carter and Agterberg, 1990;Carranza, 2009;Carranza and Sadeghi, 2010;Ford and Blenkinsop, 2008;Lindsay et al, 2014;Lisitsin et al, 2013;Pan and Harris, 2000;Porwal et al, 2010;Feizi and Mansouri, 2013a).…”

“…Based on previous work such as Allbed et al (2014), modelling and mapping of mineral potentials based on satellite image data and processing it based on remote-sensing and regression analysis is a promising approach as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken as the first step in the mineral prospectivity mapping (MPM) field.…”