Development of salt-affected soils in the irrigated lands of arid and semi-arid region is major cause of land degradation. Hyperion hyperspectral remote sensing data (EO-1) was used in the present study for characterization and mapping of salt-affected soils in a part of irrigation command area of IndoGangetic alluvial plains. Linear spectral mixture analysis approach was used to map various categories of salt affected soils represented by spectral endmembers of slightly, moderately and highly salt-affected soils. These endmembers were related to surface expression of various categories of salt-affected soils in the area. The endmembers were selected by performing minimum noise fraction (MNF) transformation and pixel purity index (PPI) on Hyperion (EO-1) data with reference to high resolution LISS IV data and field data. The results showed that various severity classes of salt-affected soils could be reliably mapped using linear spectral unmixing analysis. A low RMSE value (0.0193) over the image was obtained that revealed a good fit of the model in identification and classification of endmembers of various severities of salt affected soils. The overall classification accuracies for slight, moderate and highly salt-affected soils were estimated of 78.57, 79.81 and 84.43% respectively.
Hyperspectral remote sensing (Hyperion EO-1) data has emerged as most promising tool in quantifying severity of salt-affected soils. The study deals with identifying sensitive spectral bands (wavelength regions) for salinity parameters and thereafter used to compute spectral indices viz. Salinity index (SI), Brightness index (BI), Normalized Differential Salinity Index (NDSI), Combined Spectral Response Index (COSRI) and Coloration index (CI). Six sensitive hyperspectral bands (Band 9, 20, 22, 28, 29 and 46) of Hyperion-1 satellite data were identified to generate the spectral indices. The relationship between these spectral indices and salinity parameters of electrical conductivity (EC), sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) were established to generate maps showing severity of salt-affected soils of the area. The severity maps were categorized into classes of normal, slight, moderate and highly showing the spatial distribution of severity of salt affected soils. Among these spectral indices, SI shown highest correlation coefficient (r 2 ) with the parameters of ECe (r 2 = 0.777), SAR (r 2 = 0.801) and ESP (r 2 = 0.804) followed by BI, COSRI and CI. The Hyperion data has shown the potential to assess severity of salt-affected soils for large area which may very useful for identifying the area for carring out reclamation measures and management planning.
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