چکیده:
Saline lakes can increase the soil and water salinity of the coastal areas. The main aim of this study is to distinguish the characteristics of the spectral reflectance of saline soil, analyze the statistical relationship between soil EC and characteristics of the spectral reflectance of saline soil, and to map soil salinity east of the Maharloo Lake. The correlation between field measurements of electrical conductivity and remote sensing spectral indices was evaluated using multiple regression analysis. In this study, Kriging, CoKriging, and multiple regressions were applied for soil salinity mapping and classification using 100 soil samples. After radiometric, geometric, and atmospheric corrections of Landsat OLI images, the statistical correlation between the electrical conductivity of field measurements and spectral reflectance was investigated. According to obtained results, the modified salinity index (MSI) with the highest correlation (R2=0.78) was used as an auxiliary variable for the coKriging method. Kriging with a spherical model was selected for soil salinity mapping (RMSE = 50.5 and R2 = 0.18). The RMSE and R2 values for CoKriging were (43.2 and 0.42), respectively. Because of their acceptable R2 (=0.65) and low standard deviation (33.8) for salinity analysis, MSI and difference vegetation index (DVI) were used to estimate and zonate soil salinity in the study area. The results showed that soil salinity could be estimated via spectral indices with acceptable accuracy, R2 and RMSE. Overall, this method leads to a decrease in the costs involved in the soil mapping of saline soil areas.
خلاصه ماشینی:
Mapping spatial variability of soil salinity in a coastal area located in an arid environment using geostatistical and correlation methods based on the satellite data M.
The study of soil salinity using remote sensing techniques was proposed to reduce fieldwork and data generation costs (Allbed and Kumar, 2013).
Reviewing the previous studies shows that the best indices and original bands for soil salinity mapping are different based on the imagery types and the extent of the salinity.
In this study, a combination of image processing and geostatistics were applied to: (1) investigate the spectral reflectance behavior of saline soils in Maharlo Lake, (2) analyze the statistical correlation between the electrical conductivity of field measurements and remotely sensed spectral indices, and (3) select the best method for generating the soil salinity map of the study area using image processing and geostatistics.
Location of the study area in Fars province and Iran Salt-affected soils have high spectral values in red and near-infrared bands (131.
4. Classification of soil salinity map There are some restrictions on the use of remote sensing data for mapping salt-affected areas related to the spectral behavior of salt types, the spatial distribution of salts on the ground surface, temporal changes on salinity, vegetation diversity, and spectral confusions with other ground surfaces (Metternicht and zink, 2003).
The results of this study show that soil salinity could be estimated via spectral indices with an acceptable accuracy.
Mapping soil salinity in irrigated land using optical remote sensing data, Eurasian Journal of Soil Science 3, 82 – 88.