Comparing Landsat 8 for monitoring soil salinity in dry and wet seasons
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Abstract
The present paper was conducted to compare Landsat 8 OLI satellite data for monitoring soil salinity in dry and rainy seasons. To this end, three Analytical Algorithms had been selected: the Generalized Linear Model (GLM), the Random Forest model (RF), and the Support Vector Machine model (SVM). The model performance was assessed using Root Mean Square Error (RMSE) and Coefficient of determination (R2), along with various levels of soil salinity data obtained from Landsat 8 images. As a result, in case of the soil surface is at a constant depth of 0.30 m, it was observed that the Random Forest model of Landsat 8 satellite images presented the highest correlation compared to Root Mean Square Error (RMSE) which provided the lowest value at 8.218. In addition, a coefficient of determination (R2) calculated for this model is equal to 0.814. Hence, this study can be effectively used in detecting and mapping salinization by applying Landsat 8 with other analysis methods.
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References
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