Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain

Abstract

Salt's deposition in the subsoil is known as salinization. It is caused by natural processes such as mineral weathering or human-made activities such as irrigation with saline water. This environmental issue has grown more critical and is frequently occurring in the Hungarian Great Plain, adversely influencing agricultural productivity. This study aims to predict soil salinity in the Great Hungarian Plain, located in the east of Hungary, using Landsat 8 OLI data combined with four state-of-the-art regression models, i.e., Multiple Linear Regression, Partial Least Squares Regression, Ridge Regression, and Feedforward Artificial Neural Network. For this purpose, seventy-six soil samples were collected during a field survey conducted by the Research Institute for Soil Sciences and Agricultural Chemistry between the 15 of September and the 15 of October, 2016. We used the min–max accuracy, the root-mean-square error (RMSE), and the mean squared error (MSE) to evaluate and compare the four models' performance. The results showed that the ridge regression model performed the best in terms of prediction (MSEtraining = 0.006, MSEtest = 0.0007, RMSE = 0.081), with a min–max accuracy equal to 0.75. Hence, the application of regression modeling on spectral indices, principal component analysis, and land surface temperature derived from multispectral data is an efficient method for soil salinity assessment at local scales. The resulting map can provide an overview of salinity levels and evaluate the efficiency of land management strategies in irrigated areas. An increase in sampling density will be recommended to validate this approach on the regional scale.

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

“…For example, Nie et al ( 2021 ) evaluated the effectiveness of terrain factors such as DEM, slope, and TWI using kriging regression techniques in order to predict the extent of secondary salinization, in a setting of northeast China. Later, Sahbeni ( 2021 ) used field data and spectral salinity and vegetation indices derived from Landsat 8 OLI for developing soil salinity prediction maps in the Great Hungarian Plain using regression modeling. This study demonstrated the efficiency of the elevation factor which was statistically significant for soil salinity prediction, with a p value equal to 0.002, as in the case of this study.…”

Overexpression of McHB7 Transcription Factor from Mesembryanthemum crystallinum Improves Plant Salt Tolerance

“…Since not all the CAM species are salt tolerant [ 35 ], the ice plant, as a halophyte, is a useful model for studying plant stress tolerance and enhanced water use efficiency. Considering that soil salinization has become worse and worse due to mineral weathering and irrational use of irrigation and fertilizers [ 36 ], the goal of this study was to uncover the molecular mechanisms underlying plant salt stress response and tolerance in the ice plant. The acquired knowledge may inform engineering and breeding efforts for stress-tolerant crops.…”

Drainage models: An evaluation of their applicability for the design of drainage systems in arid regions

“…Therefore, the potential of using remote sensing images such as Landsat coupled with ANNs to develop a model that can predict soil salinity might be a solution in data-poor areas (e.g. Libya) (Sahbeni, 2021).…”