ESTIMATING SALINITY USING REMOTE SENSING DATA

Abstract

Remote sensing data has proved to be an effective method of estimating soil salinity in land cover. Remote sensing data is used in soil salinity studies as it is quick and useful for making soil changes predictions. Soil salinity causes many problems for agriculture as it has bad effects on water absorption of the plants, which results in yield reduction. This study was conducted in El-Sheikh in Giza governorate in Egypt, to understand the correlation between field truth data of soil salinity in terms of TDS (Total Dissolved Salts) and soil salinization indices. In this study, a Landsat-7 image taken in October 2009 was used after radiometric and atmospheric corrections. SLR (Simple Linear Regression) was applied between truth data and salinization indices. The best representative index for the study area was SI6, which achieved a correlation 0.78 and the minimum value of RMSE (Root Mean Square Error). This approach enables precise monitoring of the spatial distribution of soil salinity, especially in the reclaimed areas, by the combination of remotely sensed data, GIS, and field truth data.