SOIL SALINITY FORCASTING USIG SPECTRUM REFLECTIVITY DATA - & et al., Al-TAMIMI (IRAQI JOURNAL OF AGRICULTURAL SCIENCES, 2018)
This study was conducted to assess soil salinity forecasting using spectral soil reflectivity. Artificial salinization was carried out on silty clay loam soil. Collected soil sample was handy crushed, sieved through a 4 mm sieve and backed in plastic columns. The columns were closed from the bottom with a perforated plastic lids with the presence of sand-gravel filter. Columns placed vertically at plastic basins contain saline ground water and left for salinization by capillary rise. At the desired salinity level, soil reflectivity was measured using spectroradiometer and wave length between 350-2500 nm, and band width 1 nm. Soil salinity and moisture were determined soon after spectral measurements. Data processed and converted to digital data using ViewSpecPro software. MS Excel 2010 was used to calculate reflectivity data for bands equivalent to those used with the sensor OLI used at LandSat-8. SPSS V.23 statistic program was used to formulate mathematics models (Multiple linear, Quadratic and Cubic) that describe the relationship between soil salinity and spectral reflectivity at three soil moisture levels i.e. 8, 18 and 24%. Results confirmed the efficiency of the three models to forecast soil salinity at 19 dS m-1 or higher and at soil moisture of 24%. The quadratic and cubic models also gave good results at soil salinity of 9 dS m-1 or more and at 8% soil moisture level. At soil moisture of 18%, the Quadratic and Cubic models showed behavior similar to their behavior at the lower moisture level, while the linear model was efficient at salinity level of 40 dS m -1 and higher.