Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing, and a modified deep learning MU-NET convolutional neural network

Abstract

A study revealed that the Siwa Oasis faces high soil salinity, which negatively impacts agricultural areas and crop productivity, despite its significant economic and agricultural importance. The current research proposes an approach to detect and segment salinity and vegetation areas at Siwa Oasis, Egypt, by combining remote sensing and building a deep learning neural network model-based U-NET algorithm to detect salinity change areas, anticipate further degradation, and predict soil quality indicators. To locate changes among the available images, standard image improvement, classification, and change detection methods have been used. We applied a deep learning modified U-Net (MU-NET) algorithm to segment and produce salinity maps. The MU-NET architecture is a two-level nested U-structure merged with a residual U-block (RUb), which consists of an encoder and a decoder. We applied RUb, which consists of several layers and skip connections. Different combinations of the salinity and vegetation indices were added to the original image to improve segmentation precision. The model was validated and trained using actual data samples collected over a 10-year period from the Landsat 8 satellite, which can monitor and analyse present land cover changes. The dataset consisted of 91 OLI and TIRS spectral images. Each one consists of eleven bands with a spatial resolution of 30 m for bands 1 to 7 and 9. A field survey was used as the main source of data for comparing the proposed model's outputs to assess the error rate. The study region is experiencing an increase in soil salinity in all directions, particularly with regard to the spatial distribution of saline soils, not just the quantitative increase in salt-affected soils. These findings supported the acceleration of soil salinization and vegetation death. The proposed model achieved the highest performance results among the other models and literature and was based on applying method 12 using 13 image layers, with the highest accuracies of 91.27% and 90.83% for salinity and vegetation, respectively. • Remote sensing and deep learning integration is crucial for salt-affected soil segmentation. • Incorporating diverse indices improves accuracy in salinity change detection. • Model validation and training utilize 10 years of Landsat 8 data. • Model outputs compared with field survey data for error assessment • Increasing soil salinity threatens agricultural productivity in Siwa Oasis.