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Research Article

Agricultural Reviews, volume 42 issue 3 (september 2021) : 257-266

Remote Sensing Applications in Mapping Salt Affected Soils

Nirmal Kumar1,*, S.K. Singh2, G.P. Obi Reddy1, V.N. Mishra3, R.K. Bajpai3
1Division of Remote Sensing Applications, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur-440 033, Maharashtra, India.
2ICAR - Central Coastal Agricultural Research Institute, Ela, Old Goa-403 402, Goa, India.
3Department of Soil Science and Agricultural Chemistry, College of Agriculture, Indira Gandhi Krishi Viswavidyalaya, Raipur-492 012, Chhattisgarh, India.
Cite article:- Kumar Nirmal, Singh S.K., Reddy Obi G.P., Mishra V.N., Bajpai R.K. (2021). Remote Sensing Applications in Mapping Salt Affected Soils . Agricultural Reviews. 42(3): 257-266. doi: 10.18805/ag.R-2008.

ABSTRACT

The aim of this review paper is to provide a comprehensive overview of remote sensing-based mapping of salt affected soils. By providing multispectral and multi-temporal low cost data at various resolutions, remote sensing plays an important role for identifying and mapping the distribution of salt affected soils. Different bands of the multispectral data and the indices and transforms derived from them have been found useful in delineating salt affected soils. The various approaches to map salt affected soils involving remote sensing data, from visual interpretation to supervised and unsupervised classifications have been discussed. Quantitative mapping of soil salinity with remote sensing and other environmental variables have also been discussed. 

INTRODUCTION

Soil salinization/ alkalization have been identified as a major cause of land degradation, after erosion, globally (Oldeman et al., 1990, Bai et al., 2008) including India (Maji et al., 2010; Reddy et al., 2018; Kumar, 2018; Kumar and Singh, 2018; Kumar et al., 2020). Global estimates reveal over 76 million hectare (M ha) area affected to various degrees of soil salinization (Bridges and Oldeman, 1999). In India the extent of salt affected soils (SAS) is reported to be 6.73 M ha (Mandal et al., 2009; Maji et al., 2010). SAS under irrigated agriculture have been estimated variably from 20% (Ghassemi et al., 1995) to up to 50% of all irrigated lands (Szabolcs, 1992; Flowers, 1999). In the future climate change scenario, rise in sea level will impact salinity in coastal areas. Further, the rise in temperature and subsequently increased evaporation will lead to increased salinization in the arid and semi-arid regions.
       

The 15th goal of the Sustainable Development Goals (SDGs) of the United Nations Development Programme (UNDP) aims “to combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods and strive to achieve a land degradation-neutral world” by 2030. This necessitates quantifying and monitoring the spatial distribution of soil salinity, but accurate data with a sufficient spatial resolution is often not available. Commonly used methods such as soil sampling are time consuming and yield only point values. The expert opinion based Global Assessment of Human-induced Soil Degradation (GLASOD) (Oldeman et al., 1991) methodology, which maps the status of soil degradation within loosely defined physiographic units (polygons) is qualitative, subjective and is not globally consistent and reproducible (Bai et al., 2008). Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The application of remote sensing for identifying and mapping SAS started decades ago with the interpretation of airborne photographs (Sharma et al., 1976; Rao and Venkatratnam, 1991; Singh et al., 2008) and satellite images (Sharma and Bhargawa, 1988; Mandal and Sharma, 2001, 2008, 2011; Sujatha et al., 2000).

       
Wide range of advancements in space borne sensors in terms of spatial, temporal, and spectral resolutions have been made in recent times (Sahu et al., 2014; Kumar et al., 2018). These advancements in the sensors and the advanced digital image analysis techniques have made the assessment, mapping and monitoring of SAS more accurate, rapid, and quantitative. In this review, a comprehensive array of routine and modern techniques to map SAS is presented. Largely, broadband sensors have been used for studying SAS. Lately, the use of spectroscopy for assessing SAS has been increasing but their use is not widespread in mapping SAS as limited hyper-spectral satellite data are available. This review includes different methods for mapping SAS with remote sensing.
 
Extent and distribution of salt affected soils

Global distribution of salt affected soils
 
Based on the FAO/UNESCO soil map of the world and many other maps and databases Szabolcs (1992) reported 932.2 M ha of SAS in the world which was further revised to 831 M ha (Martinez-Beltran and Manzur, 2005), extending over all the continents including Africa, Asia, Australasia and the Americas. Shahid, (2013) reported that no continent is free from salinity and more than 100 countries are affected by soil salinity/ sodicity. Altogether about 1 billion hectares of land have saline or sodic soils. However, human induced salinization affects a much smaller area than natural salinity but still affects approximately 76 M ha (Oldeman, 1990). Salt problems affect rich and poor countries alike, but Africa and Asia are disproportionately affected. Of the total 76 M ha, most area under salinization were reported in Asian (53 M ha) and African (13 M ha) continents. Out of the 76 million ha about 45 million ha comes under irrigated area accounting for 20% of total irrigated area of the world.
 
Distribution of salt affected soils in India
 
The area estimates of SAS in India vary from 0.68 to 26.1 M ha depending on the methodology and definition applied (Table 1) across various organizations all over the country. These variations reflect the differences in adopted methodologies and the classification systems of SAS.
 

Table 1: Nationwide area under SAS (M ha) estimated by different researchers/ organizations.


       
First nationwide estimate of SAS was reported by Raychaudhuri (1966). A conventional approach was applied and the area under SAS was reported to be 6.0 M ha. Based on soil map of 1:250 K the ICAR-NBSS and LUP projected an area of 10.1 Mha as SAS in India (Sehgal and Abrol, 1994) which was revised to 5.89 Mha in 2004 (NBSS and LUP, 2004). The National Remote Sensing Agency (NRSA, 1997), Hyderabad prepared state wise SAS maps of India on a scale of 1:250 K jointly with the ICAR and other agencies integrating Landsat data from 1986/1987 and ground truth survey data. Mandal et al., (2009), developed GIS database on salt affected areas of the country. They estimated 6.73 million ha of SAS in India with extensive areas in the Gangetic plain of Uttar Pradesh; the arid and semiarid regions of Gujarat and the peninsular plains of Maharashtra state. A significant area of SAS, 1.237 M ha out of total 6.73 M ha, was also reported in the coastal region covering eleven states and union territories. The salt affected soils are primarily saline in deltaic, coastal and mud flats/mangrove swamps and sodic in alluvial, aeofluvial /aeolian/arid and peninsular plains. Kumar and Singh (2018) found about 10% of the total geographical area in 23 district of Uttar Pradesh, in a stretch from Gazipur to Aligarh, to be affected by soil salinity.
 
Remote sensing for mapping SAS
 
The field method of mapping SAS requires extensive soil sampling through a sampling design and analyzing it for soil properties such as: (a) pH values (b) salt contents measured as the Electric Conductivity (EC) in a saturated soil paste/ extract or in aqueous extracts/ suspension with different soil/water ratios (c) exchangeable sodium and cation exchange capacity from which Exchangeable Sodium Percentage (ESP) is calculated and (d) the ratio of the concentration of sodium (Na+) to other cations (K+, Ca2+ and Mg2+) in the soil solution or in water extracts, also known as Sodium Absorption Ratio (SAR) (Kertesz and Toth, 1994; Kumar et al., 2018). This makes the approach time consuming and expensive. The other approach is to map the SAS based on field survey and experts’ opinion such as GLASOD (Oldeman et al., 1991). It is qualitative, subjective and is not globally consistent and reproducible (Bai et al., 2008).
       
The identification and mapping of salt affected lands can be efficiently approached by using satellite data mainly because they provide a wide spatial coverage and frequent data sets in different spatial resolutions. The remote sensing data have been used to identify SAS in three manners: visual interpretation, unsupervised and supervised classification of moderate resolution data such as Landsat (Singh and Dwivedi, 1989; Gao, 2008) and prediction of soil salinity indicator properties (EC, ESP, SAR, or pH) with remote sensing and other environmental variables. Most attempts to map SAS in India is based on visual interpretation of aerial photographs or False Colour Composites (FCC) of moderate resolution satellite data (NRSC, 2005; 2011; 2012; Ajai et al., 2009; SAC, 2007). This method is constrained by being subjective and time, labor and cost consuming.
       
An alternative for visual interpretation techniques is the automatic extraction of SAS from satellite imagery based on their spectral response. This approach applies supervised (Saha et al., 1990; Dwivedi and Sreenivas, 1998a,b; Chen and Rao, 2008; Abbas et al., 2013) or unsupervised (Khan et al., 2005; Mitchell, 2014) classification on moderate resolution data such as Landsat, Linear Imaging Self-scanning System (LISS)-III, etc. The approach is objective and provides rapid analysis of data. Many methods of classification such as Maximum Likelihood (MLH), Support Vector Machine (SVM), Decision Tree (DT) and Logistic Regression (LR) based methods have been used successfully to identify SAS based on the surface features.
       
Other method involves an integrated use of remote sensing and other environmental variables for developing soil salinity prediction models. Different statistical models have been used to correlate field-measured EC with satellite data variables (bands, transforms, or indices) as well as, geology, soil, terrain and hydrological variables. The best fit model is applied to the rasters of variables to get the salinity map of the area. These maps directly give the salinity level at any point in the image. The use of multispectral satellite images for salinity modelling is constrained by limited number of bands. The development of airborne and satellite-based hyperspectral sensors has overcome the spectral limitations of multispectral satellite imagery.
 
Bands, transforms and indices for identifying SAS
 
Different bands of multispectral satellite data have been used individually or in combinations to identify SAS or to differentiate it from other surface features. A FCC is preferred over a single band. The standard FCC has been used in visual interpretation (Sharma and Bhargava, 1988; Singh and Dwivedi, 1989; Dwivedi, 1992, 1994; Sujatha et al., 2000; Sethi et al., 2006) and digital analyses to predict SAS (Dwivedi et al., 2001; Abbas et al., 2013). Apart from the standard FCC, Landsat- Thematic Mapper (TM) band combination 1, 3 and 5 was identified as the best combination on the basis of information content for characterizing SAS in the Indo-Gangetic Plains (IGP) (Dwivedi and Rao, 1992; Csillag et al., 1993). Saha et al., (1990) found TM bands 2, 4, 5 and 7 suitable for identification of SAS and waterlogged areas based on spectral responses and statistics of wasteland categories. Landsat Multispectral Scanner (MSS) bands 3, 4 and 5 are recommended for salt detection in addition to TM bands 3, 4, 5 and 7 (Naseri, 1998). Metternicht and Zinck (1996) found that a combination of six TM bands (1, 2, 4, 5, 6 and 7) provides the highest separability among salt- and sodium-affected soil classes. The incorporation of the TM thermal band 6 improves the separability of alkaline areas neighbouring saline–alkaline, saline and non-affected areas. Dogan and Kýlýç (2013) found all bands of Landsat Enhanced Thematic Mapper Plus (ETM+) except band 4 and 6 suitable for salinity assessment based on correlation of digital number (DN) values with EC and pH. The red-edge bands of Sentinel -2A Multispectral Instrument (MSI) data were reported more sensitive towards soil salinity (Wang et al., 2019).
       
Alternatively, many image transforms such as, principal components (PC) (Ding et al., 2011; Pattanaaik et al., 2008) and Tasseled Cap Transformation (TCT) (Gutierrez, 2002; Elnaggar and Noller, 2010; Rao et al., 2006; Chen and Rao, 2008) have been found effective in assessment of SAS. Ding et al., (2011) demonstrated that the PC3 of ETM+ was the best band to identify areas of severely salinized soil. Dwivedi and Sreenivas (1998) found PC3 of TM data of two periods effective to identify changes in salt affected areas with 84.33% accuracy. The TCT is generally understood to identify brightness, greenness and wetness (or yellowness) of pixels in an image (Kauth and Thomas, 1976). The brightness band can be used to identify saline soils from their highly reflective characteristics (Peng, 1998). The brightness and wetness bands of the TC found to accentuate the boundary between SAS and land-water (Mitchell, 2014). Relative to the image classification process, Masoud and Koike (2006) found the TCT to enhance the detection and classification of saline features. TCT has been found to perform better than PC in classification and assessment for degraded lands with higher overall classification accuracies (Patterson and Stephen 1998, Phua and Saito 2003, Toomey and Vierling 2005). Based on transformed divergence values, Gutierrez (2002) found better separability among SAS categories using TC transformation composite than using different band combinations.
       
In addition, vegetation indices (VIs) and several salinity indices (SIs) have been used to get better accuracies in identification of SAS (Table 2). In general, soil salinity is negatively related with VIs. However, soil salinity (EC values dS/m) was found to be poorly correlated with VIs such as Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) (Douaoui et al., 2006; Aldakheel, 2011), while SIs were well correlated with the EC values (Allbed et al., 2014; Aswaf et al., 2016; Elhag, 2016).These indices make the required salinity information more prominent while suppressing the effects of other land use/land cover features.
 

Table 2: Indices for soil salinity assessment.

 
 
Recently, Wang et al., (2019) developed 36 intensity and salinity indices from the Sentinel-2A MSI data with two and three bands combinations. The red-edge indices were found more sensitive towards salinity than the existing SIs. This might be attributed to the novel spectral information and higher signal-to-noise ratio in the red-edge region.
 
Visual interpretation
 
Visual interpretation involves identification and delineation of salt affected lands on satellite data (panchromatic or multispectral) or on aerial photographs manifested by their conspicuous tone/colour, size, shape, texture, pattern, association etc. (Goosen, 1967). Sharma et al., (1976) mapped SAS of Sangrur district, Punjab on black and white aerial photographs.
       
These were identified as white to whitish-gray colour patches with fine to medium texture. Rao and Venkatratnam (1991) identified three classes of SAS (slight, moderate and strong) on black and white aerial photograph in a black soil region of Gujarat. However, there was a mismatch of area under different categories when compared to SAS classes delineated on TM FCC. Singh et al., (2008) prepared a plot level map of sodic lands under canal and non-canal command area in a part of Etah district, Uttar Pradesh using aerial photographs. The major limitations of working with aerial photos are that they are generally collected for smaller regions of interest, are not frequently acquired and have no multi spectral information. Further, aerial photographs can only be visually interpreted. Satellite images provide a synoptic view of a large area in different spectral bands periodically.
       
Different multispectral satellite data have been interpreted visually for mapping SAS (Sharma and Bhargawa, 1988; Singh and Dwivedi, 1989; Narayan et al., 1989; Verma et al., 1994; Dwivedi, 1994; Singh, 1994; Mandal and Sharma, 2001, 2008, 2011; Sharma et al., 2011) and their categories (Rao and Venkatratnam, 1991; Kalra and Joshi, 1996; Dwivedi et al., 1999; Sharma et al., 2000; Sujatha et al., 2000) with limited field checks. On a standard FCC, salt affected areas are generally identified as scattered and non-contiguous patches of irregular shapes with fine to mottled texture and bright to dull white colour (subjected to moisture content) (Sharma and Bhargawa, 1988; Narayan et al., 1989; Verma et al., 1994). However, surface features appearing in similar tones on FCC may reduce the accuracy. The other elements of visual interpretation like association and shape, different band combinations and the season of the data interpreted can help in differentiating the features showing similar colour/tone. Sharma and Bhargawa (1988) differentiated saline soils and barren sandy soils, occurring in sand bars and river beds, both with a bright white color, using their geographic settings. The SAS is located in the river plains/ valleys, coastal lowlands and deserts and are associated with irrigated agriculture lands amid crop areas and around tidal marshes in the case of coastal saline soils (Verma et al., 1994). Dwivedi et al., (1999) differentiated SAS from the rock-outcrops of shales and their quarries, both showing similar colour on the standard FCC, by analyzing latter’s association with shale country. Verma et al., (1994) were able to differentiate SAS from the fallow land with dry stubble at the surface by choosing the image of March or first week of April which offers the maximum contrast between SAS and crop land instead of the image of May and June when the salt appearance on the surface is highest. Rao and Venkatratnam (1991) also found the image of April to be better in identifying SAS than the image of December when soils are moist and the salt encrustation cannot be seen easily on the surface especially in the black soils. Verma et al., (1994) differentiated SAS and sandy soils with the help of thermal band which shows a dark gray tone with smooth texture and very light gray tone with rough texture, respectively. Although the standard FCC (TM band combination 2, 3 and 4) is the most used band combination in the interpretation of SAS, Dwivedi et al., (1992) found the band combination 1, 3 and 5 the best suitable. Dwivedi (1996) found first principal component (PC1) of MSS to identify SAS with an accuracy of 98.1%.
       
Visual interpretation of remote sensing data is time consuming, subjective and hard to reproduce. An alternative for visual interpretation techniques is the automatic extraction of the SAS from satellite imagery different classification techniques based on their spectral responses. This provides a quick and objective tool for identifying and mapping SAS.
 
Digital image classification
 
Several unsupervised and supervised classification methods including machine learning techniques have been found successful in identifying and mapping SAS using multispectral bands and/or different indices and PCs. Limited use of unsupervised classifiers in identification of SAS have been reported. Khan et al., (2005) found reliable results in identification of SAS by applying Iterative Self Organizing Data Analysis Technique (ISODATA) classification on different indices and PCs derived from IRS-1B LISS-II data. Mitchell (2014) monitored SAS, every 8 years, over a span of 1990 to 2013 using ISODATA on a composite of Landsat 5 TM/ 8 OLI bands blue, NIR, and TIR along with TC brightness and wetness for three years to identify SAS with an accuracy of 97% to 99.33%.
       
Saha et al., (1990) applied MLH on TM data in mapping salt affected and surface waterlogged lands in India and found that these salt-affected and water logging areas could be effectively delineated, mapped and digitally classified with an accuracy of about 96 percent using bands 2, 4, 5 and 7. Dwivedi and Sreenivas (1998a) applied MLH on MSS FCC for years 1975 and 1992 to delineate SAS with accuracies of 98.85 and 98.5%, respectively and found shrinkage of 14.55% in area under SAS during the period. Dwivedi and Sreenivas (1998b) applied MLH on 23.5m spatial resolution IRS-1C LISS-III data and 36.25 m spatial resolution IRS-1B LISS-II data, both acquired very closely and having comparable spectral bands, for mapping SAS and waterlogged areas in the IGP of northern India. The classification accuracy was better for latter (88.7%) dataset than the former (87.69%). However, the difference was not significant and was attributed to within-class spectral variations encountered in LISS-III data by virtue of their higher spatial resolution. For the same reason, accuracies were found to be better in case of LISS-II (89.6%) than LISS-III (85.9%) and LISS-III+PAN (81.5%) data in identifying SAS (Dwivedi et al., 2001). Gutierrez (2002) applied MLH on three TM composites (bands 4, 3, 2; bands 4, 5, 3; bands 4, 5, 7) and the TCT composite 1, 2 and 3. All these FCCs were found similar in their ability to discriminate among SAS and between sandy soils and SAS. Abbas et al., (2013) applied MLH on IRS 1B- LISS II data of an irrigated agricultural area in Pakistan to differentiate SAS from bare land, fallow, crop land, urban and waterlogged areas with an overall accuracy of 98.8%. Wu et al., (2008) classified SAS in slightly, moderately and strongly saline soils and slolonchak with an accuracy of 90.2% by applying MLH on MSS and TM of three seasons supported by IRS Advanced Wide Field Scanner (AWiFS) and China-Brazil Earth Resource Satellite (CBERS) data when required.
       
MLH is the most used supervised classification technique for identifying SAS (Saha et al., 1990; Dwivedi and Sreenivas, 1998a,b; Abbas et al., 2013). However, it is a parametric classifier which needs the data to be normally distributed and fails in resolving interclass mix-up if the data employed do not have a normal distribution (Rowan et al., 1977; Quinlan, 1993). Many non parametric classifiers like neural network (ANN) algorithms, DT and SVM have been developed and are increasingly being used to cope with non normal distributions and intraclass variation found in a variety of spectral data sets (Hansen et al., 1996; Huang et al., 2002; Venables and Ripley, 1994). Nonparametric classifiers have frequently been found to yield higher classification accuracies than parametric classifiers (Pal and Mather 2003; Rao et al., 2005). DT is one such technique, found to be very effective for land use and land cover (LULC) classification and salt-affected areas mapping. DT has an advantage over the other nonparametric algorithms that it can handle even categorical inputs in a natural fashion, and the classification structure is explicit and easily interpreted (Friedl and Brodley 1997; Friedl et al., 1999).
       
Rao et al., (2006) compared MLH and DT to identify SAS and found the latter more efficient in differentiating SAS, residential areas and sand areas. The inputs used were first component of TCT, TM 6 imagery (thermal infrared imagery) and NDVI of Landsat TM. Chen and Rao (2008) differentiated degraded grasslands and SAS with an accuracy of more than 85% by applying DT on inputs including TM 6, NDVI and brightness, greenness and wetness generated by TCT. The DT allows using inputs continuous as well as discrete in nature. Elnaggar and Noller (2010) used Landsat TM bands, different indices (NDVI, NDSI, SAVI and TCT), terrain attributes (elevation, slope, aspect) and discrete attributes like landform, geology, historic vegetation, distance to streams, etc. as DT inputs to classify SAS with an accuracy of 99%. In the same study MLH of the Landsat images could yield only two salinity classes: non-saline soils (EC < 4 dSm-1), prediction accuracy of 97% and saline soils (EC > 4 dSm-1), prediction accuracy 60%. Ding et al., (2011) identified TM band 1, PC3 and NDVI and NDWI as the character variables for slight and moderate saline soils, strongly saline soils and vegetation and water areas, respectively in a DT analysis. Afrasinei et al., (2017) used DT on a composite of several indices derived from landsat images to identify moderate and strongly saline soils in salt-affected areas of Algeria.
       
Kumar et al., (2019) developed LR models for identifying SAS using Landsat 8 OLI in IGP. They found bands green, red and SWIR 1 and salinity indices developed by using these bands, such as SI6-10 and COSRI, suitable for identifying SAS. Cai et al., (2010) applied SVM classifier on CBERS-02B CCD multi-spectral image to get an accuracy of 82% with the kappa coefficient of 0.79. This accuracy was improved up to 84.7% with the kappa coefficient of 0.82 by using additional inputs of textural feature such as, mean, variance and homogeneity. Abbas et al., (2013) applied binary probability density function on different salinity indices to classify SAS and normal soils. Asfaw et al., (2018) applied probability density function to get two classes of SAS, moderate and slight, apart from non-saline soils. The variable included other variables such as, geology, elevation, soil texture and ground water table in addition to NDVI.
 
Modelling soil salinity
 
This method to map SAS involves development of various statistical models to estimate salinity of the surface soils, quantitatively, based on bands, indices and transforms of multispectral or hyperspectral images. The model may also include other variables related to soil, terrain and climate - the soil forming factors. Hyperspectral modelling of soil salinity has been found successful. However, lack of imaging hyperspectral satellites limits salinity mapping with hyperspectral images. Several studies have been conducted to model soil salinity with multispectral images (Shrestha et al., 2006; Abbas et al., 2013; Asfaw et al., 2018). However, limited studies have been made to map salt affected soils with these models. Asfaw et al., (2018) mapped EC of an irrigation farm in Ethiopia with spectral indices derived from Landsat images and found SI7 to be better in prediction over other indices. Harti et al., (2016) used OLI-SI for modelling and mapping temporal soil salinity for monitoring its spatiotemporal dynamics during the period of 2000-2013. High correlation was observed between EC and OLI-SI. They reported an increase in spatial extent and decreased in intensity of soil salinity during the period. Tajgardan et al., (2007) predicted and mapped EC using simple linear regression with PC derived from Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) data at the north of the Aq-Qala Region in northern Iran. Azabdaftari and Sunar (2016) found performance of multiple linear regressions to be better than the simple linear regression in modelling and mapping EC. Mehrjardi et al., (2008) found an exponential relation between EC and ETM+ band 3. Fernandez-Buces et al., (2006) found EC and SAR exponentially related to COSRI with correlation coefficients of 0.82 and 0.75, respectively. Wu et al., (2014) used logarithmic models to map EC with Generalized Difference Vegetation Index (GDVI) in Iraq. Wang et al., (2019) found Sentinel-2A MSI data effective in mapping and monitoring salinity in dry and wet seasons. They used Partial Least Squares Regression (PLSR) to establish the relationship between EC and the 67 spectral covariates (bands and indices) derived from the Sentinel data.

CONCLUSION

Mapping and monitoring the extent, type and severity of soil salinity is needed for sound agricultural planning to ensure food security. We understood from the above review that remote sensing is a promising tool for mapping salt affected soils. It has advanced from visual interpretation of aerial photographs and satellite images to use of machine learning techniques for identification of SAS and further quantifying the soil salinity with multi-/ hyper- spectral images. The advancements in the sensors and the advanced digital image analysis techniques have made the identification and mapping of SAS more accurate and rapid. However, these methods identify the surface features related to SAS and produce a qualitative map indicating spatial distribution of SAS. The quantitative mapping of soil salinity will be of more use for better reclamation plan. The use of machine learning techniques on different terrain and environmental covariates along with satellite images and their derivatives has been found promising for quantitative mapping of SAS.

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