Evaluating different spectral indices in identification and preparation of soil salinity mapping of arid region of Iran مقاله

Soil salinity undergoes significant spatial and temporal variations; therefore, salinity mapping is difficult, expensive, and time consuming. However, researchers have mainly focused on arid soils (bare) and less attention has been paid to halophyte plants and their role as salinity indicators. Accordingly, this paper aimed to investigate the relationship between soil properties, such as electrical conductivity of the saturation extract (ECe) and the spectral reflectance of vegetation species and bare soil, to offer a method for providing salinity map using remote sensing. Various vegetation species and bare soil reflectance were measured. Spectral Response Index (SRI) for bare soil and soil with vegetation was measured via the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and salinity indexes. The electrical conductivity of the saturated extract, texture, and organic matter of soil samples were determined. The correlation coefficient of soil salinity with SRI, SAVI, and salinity indexes were obtained, and a model was presented for soil salinity prediction. EC map was estimated using the proposed model. The correlation between SRI and EC was higher than other models (0.97). The results showed that the salinity map obtained from the model had the highest compliance (0.96) with field findings. In general, in this area and similar areas, the SRI index is an acceptable indicator of salinity and soil salinity mapping.

Accordingly, this paper aimed to investigate the relationship between soil properties, such as electrical conductivity of the saturation extract (ECe) and the spectral reflectance of vegetation species and bare soil, to offer a method for providing salinity map using remote sensing. For example, McGowen and Mallyon (1996), using TM data and maximum likelihood classification algorithm, provide more saline areas estimation more than expected, which was due to the land management, vegetation conditions, and soil type of the studied area. On the contrary, remote sensing can minimize the time and cost for broad sampling and salinity mapping because salinity levels are assessed by various band reflectances and satellite imagery ratios (Tamلs and Lénلrt, 2006; Eldeiry and Garcia, 2008). El Hafyani (2019) modeled and mapped soil salinity in Tafilalet plain, Morocco, based on Landsat 8 OLI satellite data in combination with ground field data. Most parameters (blue, green, red and NIR and the vegetation index) indicateda good perception of the remote sensing data in the spatial mapping of soil salinity. Study of soil salinity in the Ardakan area, IRAN, based field observation and remote sensing, procceding of 8th EARSEL symposium NETHERLAND, May 1988 Allbed, A. Generating large scale soil salinity maps with geophysics and remote sensing. J. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing, 72; 201–211. The use of remote sensing data to extract information from agricultural land with emphasis on soil salinity.