UTILIZING HYPERSPECTRAL REMOTE SENSING FOR SALINE SOIL ASSESSMENT IN THE EMIRATE OF ABU DHABI, UNITED ARAB EMIRATES

This study presents the application of hyperspectral remote sensing techniques to measure soil salinity. Soil salinity is a major concern in many arid and semi-arid regions, affecting crop productivity and ecosystem health. In most arid and semi-arid areas various levels of salinity can be encountered including sabkhas. Sabkha (salt flat) is a geological feature composed of saline and salt marshes that typically occur in shallow or coastal environments within arid and semi-arid climates. Traditional methods of measuring soil salinity are time-consuming and labor-intensive, making remote sensing techniques an attractive alternative. This study employed a hyperspectral sensor specifically the SVC-XHR-1024i to collect reflectance data over the western region of Abu Dhabi Emirate, a semi-arid area distinguished by scattered sabkhas. Most previous studies have used broad-band multi-spectral data while this study focuses on the use of hyperspectral remote sensing data which is believed to produce better accuracy results. The collected data were then processed to derive spectral indices that are sensitive to soil salinity. Ground truthing was conducted through field sampling and laboratory analysis of soil samples. Laboratory measurements of Electrical Conductivity (EC) of soil water extracts were carried out. The study established a statistical relationship between the EC salinity measurements of the field soil samples and their corresponding spectral reflectance. The study used published spectral salinity indices such as the Near-Shortwave Infrared (NSI), Shortwave Infrared (SWIR), Near-Infrared (NIR), and Visible (VIS) to model salinity levels. The study findings indicate that the NSI-based model has superior predictive capacity for saline soils compared to the SWIR model, with the NIR-SWIR spectral range being optimal for accurate salinity mapping. Specifically, the NSI model demonstrated 71% accuracy as a result of this research. The study highlights the potential of hyperspectral remote sensing as a cost-effective and efficient tool for monitoring soil salinity and identifying areas at risk of salinization, which can inform land management strategies for sustainable agriculture and future land development.