ASSESSMENT OF SOIL SALINITY
USING REMOTE SENSING
- The amount of salt contained in the soil
Sources of salinity
- Release of salts from weathering of primary
- High doses of chemical fertilizers
- Irrigation water
Human-induced salinization is the process of
increasing the original status of salt content in the soil.
Salt is the savor of foods but the scourge of agriculture.
Soil salinity can be remotely sensed by several
airborne/space-borne techniques such as multi-/hyper-
spectral imagery, and active/passive microwave
Nicolas et al. (2006) detected salinity by means of
combining soil and remote-sensing data. He demonstrated
that his method has given rise to significant improvements in
salinity estimations, as compared to purely-regressive
Lounis et al. (2012) exploited the multi-spectral optical data
from the LANDSAT ETM + (Enhanced Thematic Mapper) to
map salinity .
Goldshleger et al. (2013) explored whether spectroscopy
could quantitatively assess salinity. It was concluded that
reflectance spectroscopy is useful for characterizing the key
properties of salinity in growing vegetation and assessing its
Lalit Kumar et al. (2014) Modelled Spatial Variation in Soil
Salinity based on Remote Sensing Indicators.
CASE STUDY - 1
Title: Monitoring of salinity in the area using multi-temporal satellite
Authors: Koshal, A. K.
Journal: International Journal of Remote Sensing and GIS
The study area lies between geo-coordinates 30 00 to 30 15` N &
76 30` to 76 45`E, Covering 577.86 sq km area of south west
Punjab (Bhatinda and Muktsar districts).
DATA USED AND METHODS
Remote sensing data
IRS 1D images of the study area, were procured from the
National Remote Sensing Agency (NRSA) Hyderabad.
Table 1 -Details of satellite data
The information of contour, administrative boundaries
such as sand dunes, canals, important towns, villages
and roads and highway were digitized to prepare the
The published soil survey reports, soil maps, water
quality reports for the study area were collected and
utilized during interpretation and field work
PRE FIELD INTERPRETATION
Standard FCC was visually interpreted for salt affected soils.
The salt affected soils usually appear in tones of bright white to
dull white with medium to coarse texture on Standard FCC due
to the presence of salts, on soil surface.
The obstructions to natural drainage like roads, railway lines,
distributaries, etc. can easily be identified on the FCC images.
The waterlogged/ pond areas appear on the FCC image in dark
blue to a black tone with a smooth texture.
Fig 2 A map showing preliminary interpreted units on FCC with
base details was generated before going into the field.
In total, the ground truth was collected from 24 villages, 120
samples were taken from salt affected areas and non-salt affected
A reconnaissance survey of the study area was done using satellite
Salt affected lands and affected crops were identified on the
ground and ascertained on the satellite image by characterizing
Satellite image of IRS 1D LISS III of March & May 2000 were
used for the purpose
POST FIELD WORK
The tentative legends were prepared during the pre-
fieldwork were also finalized.
Using GIS database a final map showing visual salt affected
soils was prepared.
IRS –1D Image data have been used to assess the salt
During ground verification salt accumulation was also found
to be associated with salt grass and salt tolerant wild
vegetation. The area mapped in the classes of moderate and
severe salt affected soil was 1.72 % and 7.90% of the total
CASE STUDY - 2
Title: To delineate surface soil salinity in the prime rice-wheat cropping
Authors: Iqbal. F.
Journal: African Journal of Agricultural Research, 2011
The study area, district Gujranwala in Central Punjab province, is
located in Rachna Doab, which lies between longitudes 73°38’52”,
74°34’55” East and latitude 31°47’36”, 34°34’2” North.
Satellite imagery of Landsat and published map by SSP (Soil
Survey of Pakistan) were used for detection of salt affected
The raw images were geo-referenced to a common UTM
(Universal Transverse Mercator) coordinate system.
A self-adaptive filter method was used to remove non-periodic noise and the FFT (Fast
Fourier Transform) method was used to remove periodic noise.
To analyze the pattern of salinity in the study area, the maps must be co-registered in the
same coordinate system (for example, UTM).
For salt affected soil detection, NDVI, NDSI, SI, MSI and SR indices were applied.
The normalized difference vegetation index (NDVI), simple ratio (SR), normalized
difference salinity index (NDSI), moisture stress index (MSI) and normalized difference
built-up area index (NDBI) were computed using the satellite images.
The extracted soil through satellite imagery was superimposed
with the salinity maps extracted through soil association map.
Finally the overlay of both NDSI was performed to extract the
common saline areas.
The vegetation area was masked by NDVI and MSI and
overlapped with built-up area to prepare the final map of land
The salt prone soil showed significant reflection in thermal
IR band and minimum in near infrared band.
About 70% of salt affected area is computed through satellite
Results showed that 19% of the rice-wheat cropping area of
Gujranwala district in Rachna Doab of central Punjab
province of Pakistan is salt affected.
Soil salinity is a major environmental hazard, that impacts
the growth of many crops.
Satellite imagery and false colour composites were
visually interpreted to identify salt affected lands.
Advantage of using remote sensing technology include
wide coverage (the only source when data is required over
large areas or regions), faster than ground methods, and
facilitate long term monitoring.
Goldshleger, N., Chudnovsky, A & Binyamin, R. B., (2013),
Predicting salinity in tomato using soil reflectance spectra, Int. J.
Remote Sens. 2013, 34, 6079–6093.
Iqbal. F., (2011), Detection of salt affected soil in rice-wheat area
using satellite image, Afr. J. Agric. Res. 2011, 6, 4973–4982.
Koshal, A. K., (2012), Satellite image analysis of Soil Salinity
Areas in Parts of South-West Punjab through Remote Sensing and
GIS, International Journal of Remote Sensing and GIS, Vol. 1, No.
2, 2012, pp. 84-89.
Lalit Kumar, Priyakant Sinha and Allbed. A., (2014), Mapping and
Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on
Remote Sensing Indicators and Regression Techniques, International Journal of
Remote Sensing, 2014, 6, 1137-1157.
Lounis, M and Dehni, A., (2012), Remote Sensing Techniques for Salt
Affected Soil Mapping: Application to the Oran Region of Algeria, Procedia
Engineering, Vol. 33, 2012, pp. 188-198.
Minasny, B., Taghizadeh, R., Sarmadianc, F and Malone, B. P., (2014),
Digital Mapping Of Soil Salinity In Ardakan Region, Central Iran, Geoderma
2014, 213, 15–28.