Assessing salt-affected soils using remote sensing, solute modelling, and geophysics

Geoderma ◽  
2006 ◽  
Vol 130 (3-4) ◽  
pp. 191-206 ◽  
Author(s):  
J. Farifteh ◽  
A. Farshad ◽  
R.J. George
Keyword(s):  
Remote Sensing ◽  
Salt Affected Soils

Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing

2019 ◽  
pp. 233-256
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
R. K. Naitam
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Spatial Distribution ◽  
Soil Salinity ◽  
Regression Models ◽  
Landsat 8 ◽  
Gangetic Plain ◽  
Logistic Regression Models ◽  
Salt Affected Soils ◽  
Indo Gangetic Plain

A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of differentiating salt affected soils from other features were identified as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-affected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found affected with salinity/alkalinity. The spatial distribution of salt-affected soils in the district showed an association of shallow ground water depth with salinity.

Inventory of salt-affected soils and waterlogged areas: A remote sensing approach

1999 ◽  
Vol 20 (8) ◽  
pp. 1589-1599 ◽  
Author(s):  
R. S. Dwivedi ◽  
K. Sreenivas ◽  
K. V. Ramana
Keyword(s):  
Remote Sensing ◽  
Salt Affected Soils

Remote sensing technique for mapping salt affected soils

1994 ◽  
Vol 15 (9) ◽  
pp. 1901-1914 ◽  
Author(s):  
K. S. VERMA ◽  
R. K. SAXENA ◽  
A. K. BARTHWAL ◽  
S. N. DESHMUKH
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Technique ◽  
Salt Affected Soils

Remote Sensing and Geographic Information System for Appraisal of Salt-Affected Soils in India

2010 ◽  
Vol 39 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Gurbachan Singh ◽  
D. S. Bundela ◽  
Madhurama Sethi ◽  
Khajanchi Lal ◽  
S. K. Kamra
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
Salt Affected Soils

scholarly journals Using hyperspectral remote sensing to monitor the properties of salt-affected soils

Authorea ◽  
2020 ◽  
Author(s):  
Gopal Mahajan ◽  
Bappa Das ◽  
Bhaskar Gaikwad ◽  
Ashwini Desai ◽  
Shaiesh Morajkar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Salt Affected Soils

scholarly journals Characterization of soil salinity and its impact on wheat crop using space-borne hyperspectral data

2020 ◽  
Vol 26 (3) ◽  
pp. 271-285
Author(s):  
Mirzoolim Avliyakulov ◽  
Mamta Kumari ◽  
Nurmamat Rajabov ◽  
Normat Durdiev
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Narrow Band ◽  
Partial Least Square ◽  
Wheat Crop ◽  
Soil Parameters ◽  
Spectral Angle Mapper ◽  
Inventory Map ◽  
Salt Affected Soils ◽  
Spectral Angle

Facing the risk of soil salinization worldwide, there has been a growing interest in identifying rapid and inexpensive tools for soil salinity assessment. Remote sensing has shown great advantages in the field in recent decades. In present research, Hyperion Hyperspectral remote sensing data (EO-1) was used for characterization and mapping of salt-affected soils, to generate crop inventory map and to evaluate soil salinity impact on wheat crop growth in part of Mathura district of Uttar Pradesh representing Indo-Gangetic plain. Narrow bands can discriminate critical spectral differentials in detail and can assess the salinity hazard over crop. A detailed field survey was carried out in the study area in order to identify the salt-affected soils and to collect soil samples, groundwater table depth, chlorophyll content, LAI to characterize impact of soil salinity over crop. Various wheat crop spectra were collected for calculation of narrow band indices to discriminate various stress conditions. Spectral angle mapper (SAM) was used to generate crop inventory map with various types of crops. The same technique (SAM) was used to map various categories of salt affected soils represented by spectral endmembers of normal, slightly, moderately and highly salt-affected soils. The results showed that various severity classes of salt-affected soils could be reliably mapped using spectral angle mapper (SAM) analysis with an overall accuracy of 74.24 %. Empirical relationships developed between crop & soil parameters and vegetation indices using SMLR could show its possibility with an R2 of 0.52 and 0.41 to predict LAI and CCI, respectively. Validation results showed the RMSE of 0.8 and 5.2 to predict LAI and CCI. Partial least square regression (PLSR) statistical model (using spectroradiometer derived narrow band indices) was developed to assess different stress level with varying crop and soil parameters.

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