Abstract
Abstract 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.
Highlights
Soil salinization is a severe land degradation form in arid and semi-arid areas where evaporation exceeds precipitation [1]
The findings demonstrated the significance of satellite-derived spectral indices and climatic, geomorphometric, and legacy data in modeling soil salinity with acceptable accuracy
The remarkable difference between a minimum of 0 g/kg of soil and a maximum of 5.6 g/kg indicates a spatial variability of salinity levels
Summary
Soil salinization is a severe land degradation form in arid and semi-arid areas where evaporation exceeds precipitation [1]. The magnitude of such an environmental process makes it challenging to model and analyze on a regional scale [5]. While using conventional approaches to monitor soil salinity on local or regional scales is time consuming and requires enormous resources, geographic information systems (GIS) and remote sensing tools have become suitable alternatives, creating easier, less time consuming, and more affordable methods to assess and control environmental threats. Geostatistical, and machine learning methods are used to model and measure the uncertainty of DSM outputs, where the soil parameter of interest is considered a realization of a random variable at a single location [10]. Spectral enhancement techniques, e.g., principal component analysis (PCA) [11,12], Tasseled Cap transformation [13], and spectral indices [14], have given relatively acceptable results with regards to mapping soil parameters
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