Estimating the potassium grade of saline underground water using Sentinel satellite images and random forest algorithm(case study of Khoor and Biabank playa, Isfahan province)

Articles in Press

Document Type : Research Paper

Authors

1 Department of Soil Science and Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

2 Department of Desert Management, Faculty of Pasture and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

3 Department of Archaeology, Faculty of Humanities, Higher Education Institute of Architecture and Arts, Tehran, Iran.

Abstract

One of the widely used elements that plays an important role in sustainable agricultural production is potassium.The potassium in the surface soil of the playa originates from the potassium present in the underground water.As a result, there is a correlation between the surface soil potassium and the potassium grade of the groundwater.The aim of this research is to utilize a combination of the (RF) algorithm and satellite imagery to establish the relationship between soil surface potassium and remote sensing indicators.This will enable the prediction of the potassium grade of the underground in Khoor and Biabank playa in Isfahan province.60soil samples were taken from the0-5cm layer to measure potassium in the surface layer (dependent variable).determine the sampling coordinates, the Latin supercube method was used. Twelve boreholes were drilled to extract and measure the potassium grade of underground saline water.The12bands of the Sentinel-2satellite and four main mathematical operations were used to define the index(independent variables)to model the potassium content of the surface soil layer and ultimately estimate the rate of potassium grade in the underground saline water.The data were categorized into two groups:70% for calibration (training) and30% for validation(testing.The data were modeled using the RF algorithm in the Google Colab environment and implemented with the Python programming language. The results of this algorithm were obtained with R2, MSE, RMSE and MAE statistical indices of 0.51, 0.0179, 0.1338 and0.1130 respectively.The results of this research confirm the effectiveness of remote sensing data and machine learning algorithms in predicting the potassium grade of saline groundwater.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 26 May 2024

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