assessment of soil salinity using remote sensing

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ASSESSMENT OF SOIL SALINITY USING REMOTE SENSING

Presented by

T.Ram Reddy

131874

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CONTENTS

Introduction

Literature Review

Case study

Summary

References

INTRODUCTION

Soil salinity

- The amount of salt contained in the soil Sources of salinity

- Release of salts from weathering of primary minerals

- High doses of chemical fertilizers

- Irrigation water

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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

sensing.

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LITERATURE REVIEW

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

approaches.

Lounis et al. (2012) exploited the multi-spectral optical data

from the LANDSAT ETM + (Enhanced Thematic Mapper) to

map salinity .

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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

salt quality.

Lalit Kumar et al. (2014) Modelled Spatial Variation in Soil

Salinity based on Remote Sensing Indicators.

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CASE STUDY - 1

Title: Monitoring of salinity in the area using multi-temporal satellite

images.

Authors: Koshal, A. K.

Journal: International Journal of Remote Sensing and GIS

Study Area

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).

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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

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Ancillary data

The information of contour, administrative boundaries

such as sand dunes, canals, important towns, villages

and roads and highway were digitized to prepare the

base map.

The published soil survey reports, soil maps, water

quality reports for the study area were collected and

utilized during interpretation and field work

10 Fig 1 Overview of Methodology

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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.

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Fig 2 A map showing preliminary interpreted units on FCC with base details was generated before going into the field.

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FIELD INVESTIGATIONS

In total, the ground truth was collected from 24 villages, 120 samples

were taken from salt affected areas and non-salt affected areas.

A reconnaissance survey of the study area was done using satellite

images (FCC).

Salt affected lands and affected crops were identified on the ground

and ascertained on the satellite image by characterizing image

characteristics.

Satellite image of IRS 1D LISS III of March & May 2000 were used

for the purpose

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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.

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RESULTS

IRS –1D Image data have been used to assess the salt

affected land.

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

area.

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CASE STUDY - 2

Title: To delineate surface soil salinity in the prime rice-wheat cropping

area.

Authors: Iqbal. F.

Journal: African Journal of Agricultural Research, 2011

Study Area

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.

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METHODOLOGY

Satellite imagery of Landsat and published map by SSP (Soil

Survey of Pakistan) were used for detection of salt affected

soils.

The raw images were geo-referenced to a common UTM

(Universal Transverse Mercator) coordinate system.

18 Fig 3 Overview of Methodology

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Image preprocessing

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).

Image processing

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.

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GIS analysis

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

cover.

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RESULTS

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

imagery.

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.

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Fig 4 Salt Affected Soils In Study Area

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SUMMARY

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.

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REFERENCES

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.

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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.

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THANK YOU