Mapping the magnitude of sodicity in part of the Indo-Gangetic plains of Uttar Pradesh, Northern India using Landsat-TM data

Rao, B. R. M.; Dwivedi, R. S.; Venkataratnam, L.; Ravishankar, T.; Thammappa, S. S.; Bhargawa, G. P.; Singh, Amanpreet

doi:10.1080/01431169108929662

Cited by 54 publications

(19 citation statements)

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“…Spectra of each category of salt affected soils could be generated as a fact that increases in the salt concentration in soils result distinct and higher reflectance (Singh et al 1988;Rao et al 1991). Aerial and spatial extents of these categories of salt affected soils are needed to be mapped for their reclamation and management.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis

Gautam

Kumar

Saha

2011

J Indian Soc Remote Sens

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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.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis

Gautam

Kumar

Saha

2011

J Indian Soc Remote Sens

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“…Difference in surface reflectance of salt-affected soils had helped to identify and classify salt-affected soils from nonaffected ones. Several researchers used remote sensing data in mapping and monitoring of salt-affected soils with either visual or computer remote sensing techniques, or using combination of both methods (Rao et al 1991;Mougenot et al 1993;Douaoui et al 2006). Metternicht and Zinck (2003) reviewed various sensors and approaches for identification and mapping of salt affected areas.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: a case study of Indo-Gangetic plains of India

2014

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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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“…Mapping of these soils in some states of the Plains has been carried out recently using satellite data (NRSA 1980, Singh and Dwivedi 1989, Rao et al 1991. However, the extent of these soils does not remain constant over time.…”

Section: Introductionmentioning

confidence: 99%

Monitoring change in the extent of salt-affected soils in northern India

Singh

1994

International Journal of Remote Sensing

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Mapping the magnitude of sodicity in part of the Indo-Gangetic plains of Uttar Pradesh, Northern India using Landsat-TM data

Cited by 54 publications

References 1 publication

Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis

Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis

Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: a case study of Indo-Gangetic plains of India

Monitoring change in the extent of salt-affected soils in northern India

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