Soil salinity mapping of Mubuku Irrigation Scheme using Sentinel 1 imagery

Abstract

Soil degradation has adverse effects on the economic growth of countries and its divided into salinization, desertification, pollution and many others. Soil salinization is the process by which soluble salts accumulate in the root zones of soil negatively affecting agriculture. Due to its direct impact on agriculture, it needs to be managed. The initial step in managing soil salinization is providing accurate data on the areas affected by the salts and by what magnitude. The project was aimed at modelling soil salinity of Mubuku Irrigation scheme using Sentinel 1 GLCM texture features in conjunction with the electric conductivity values of twenty soil samples collected from Mubuku Irrigation scheme. Specifically, features that have the greatest significance to soil salinity modelling were determined and then relationship between the Sentinel 1 derived features and electric conductivity values also determined. Soil samples were collected from Mubuku Irrigation Scheme together with their coordinates and their electric conductivity values measured in the laboratory. Sentinel 1 imagery corresponding to the same date when the samples were collected was also obtained. Image was pre-processed through the following steps: application of the orbit file, speckle filtering, geometric and terrain correction and many other steps preparing image for processing. GLCM texture features were then generated from preprocessed image and then values corresponding to coordinates were extracted and exported into a workbook. Feature selection was carried out on the exported data to select the most significant features for model building in R. Regression was carried out on the selected features to create models for the selected features. Four models were generated with model1 having R2 0.701268, model2 having R2 0.75100, model3 having R2 of 0.80939 and model4 having R2 of 0.742446. The results of the models were used in band math to generate the final predictive maps for salinity of Mubuku Irrigation Scheme. Recommendations for future studies include using other selection algorithms like the genetic algorithm, use of more features for example Histogram textures and also use of other regression techniques that map input data onto a higher dimension feature space especially for nonlinear cases.