Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping between data resolution, spatial and temporal coverage, acquisition costs, and high accuracy expectations. This article provides an overview of research work related to soil salinization mapping and monitoring using remote sensing. By synthesizing recent research and highlighting areas where further investigation is needed, this review helps to steer future efforts, provides insight for decision-making on environmental sustainability and soil resource management, and promotes interdisciplinary collaboration.
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.
Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R
2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.
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