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

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 validation 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.

[1]  A. Bannari,et al.  A theoretical review of different mathematical models of geometric corrections applied to remote sensing images , 1995 .

[2]  A. Bannari,et al.  Mapping Slight and Moderate Saline Soils in Irrigated Agricultural Land Using Advanced Land Imager Sensor (EO-1) Data and Semi-Empirical Models , 2016 .

[3]  Graciela Metternicht,et al.  Remote sensing of soil salinity: potentials and constraints , 2003 .

[4]  Rob Garner Landsat 8 Instruments , 2016 .

[5]  Priyakant Sinha,et al.  Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques , 2014, Remote. Sens..

[6]  P. M. Teillet,et al.  Nécessità de l'étalonnage radiométrique et standardisation des images numériques de télédétection , 1999 .

[7]  David P. Roy,et al.  Continuity of Landsat observations: Short term considerations , 2011 .

[8]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[9]  G. F. Epema,et al.  Remote sensing of salt affected soils , 1993 .

[10]  M. Alhammadi,et al.  Impact of Salinity Stress on Date Palm (Phoenix dactylifera L) – A Review , 2012 .

[11]  Yohei Sato,et al.  Mapping Salt-affected Soils Using Remote Sensing Indicators-A Simple Approach With the Use of GIS IDRISI - , 2001 .

[12]  J. C. Doornkamp,et al.  Geology, geomorphology and pedology of Bahrain , 1982 .

[13]  Nadir Ahmed Elagib,et al.  Climate variability and aridity in Bahrain , 1997 .

[14]  L. Kumar,et al.  Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review , 2013 .

[15]  Graciela Metternicht,et al.  Remote Sensing of Soil Salinization : Impact on Land Management , 2008 .

[16]  A. Bannari,et al.  Characterization of Slightly and Moderately Saline and Sodic Soils in Irrigated Agricultural Land using Simulated Data of Advanced Land Imaging (EO‐1) Sensor , 2008 .

[17]  S. Manson Review of Principles of Geographic Information Systems: Spatial Information Systems and Geostatistics , 1999 .

[18]  Fouad Al-Khaier,et al.  Soil Salinity Detection Using Satellite Remote Sensing , 2003 .

[19]  Marc Van Meirvenne,et al.  14 Stochastic Approaches for Space-Time Modeling and Interpolation of Soil Salinity , 2008 .