In India, agriculture is one of the major application areas of the remote sensing technology. Various national level agricultural applications have been developed which showcases the use of remote sensing data provided by the sensors/satellites launched by the country’s space agency, Indian Space Research Organisation (ISRO)

1. APPLICATION OF REMOTE SENSING IN
INDIAN AGRICULTURE
Chitra .p
Second year
MSc environmental science

2. REMOTE SENSING
The science of acquiring information about
an object, without entering in contact with it,
by sensing and recording reflected or emitted
energy and processing, analysing, and
applying that information.

3. • Different objects based
on their structural,
chemical and physical
properties reflect or
emit different amount
of energy in different
wave length ranges of
the E.M.S
• The sensors measure
the amount of energy
reflected from that
object. .
BASIC PRINCIPLE

4. ESSENTIAL COMPONENT OF REMOTE
SENSING
1. Signals from a source/light
2. Sensors on a plate form
3. Sensing (Signal reception, storage,
processing, information extraction
and decision making)

5. THE REMOTE SENSING PROCESS
Visual
Digital
Reference
data
Air photos
Digital data
Maps
Statistics
GIS data
sets
User
Decision
Maker
Data
products
Inter-
pretation
Information
products
Target
audience

6. TYPES OF REMOTE SENSING
1.Passive remote sensing
2.Active remote sensing

7. •

8. ELECTROMAGNETIC SPECTRUM
VisibleLight
Nearinfrared
Midinfrared
Thermalinfrared
Microwave
TVandradio
blue
red
UV near
infrared
110-110-210-310-410-510-6 10410310210 107106105
wavelength (mm) wavelength (mm)
0.4 0.5 0.6 0.7 mm

9. • Identification, area estimation
and monitoring
• Crop nutrient deficiency
detection
• Soil mapping
• Crop condition assessment
• Agricultural draught assessment
• Reflectance modelling
• Crop yield modelling and
production forecasting
APPLICATION OF RS IN AGRICULTURE

10. IDENTIFICATION, AREA ESTIMATION AND
MONITORING
• The specific requirement of climate and soil
conditions coupled with the specialized
management practices make the distribution of
plantation crops rather more localized in
comparison to other agricultural crops.
• The identification, estimation of growing stock,
analysis of distribution and monitoring at regular
intervals are major aspects in plantation crops

11. CROP NUTRIENT DEFICIENCY DETECTION:
• The nutrient deficiency in plants
affects the colour, moisture content
and internal structures of the leaves
and as a result their reflecting power
changes

12. SOIL MAPPING
• Advancements in space technology opened application
possibilities of remote sensing in soil mapping
• Soil properties have also been inferred from optical and
microwave data using physically-based and empirical
methods. Soil properties that have been measured using
remote or proximal sensing approaches include
mineralogy, texture, soil iron, soil moisture, soil organic
carbon, soil salinity and carbonate content.

13. CROP CONDITION ASSESSMENT
• The physiological changes that occur in a plant due to
stress may change the spectral reflectance characteristics
resulting in the detection of stress amenable to remote
sensing techniques.
• Crop monitoring at regular intervals during the crop
growth cycle is essential to take appropriate measures
and to asses information on probable loss of production

14. AGRICULTURAL DRAUGHT ASSESSMENT
• Draught assessment is yet another area wherein
remote sensing data has been used at operational
level.
• The district level drought assessment and
monitoring using NDVI generated from NOAA-
AVHRR data helps in taking timely preventive and
corrective measures for combating drought.

15. REFLECTANCE MODELLING
• Physical reflectance models for crops serve the important
purpose of understanding the complex interaction
between solar radiation and plant canopies
• . In order to obtain a reliable yield prediction, growth of
crops has to be modelled by means of crop growth
models.
• Crop growth models describe the relation between
physiological process in plants and environmental factors
such as solar radiation, temperature, water and nutrient
availability

16. CROP YIELD MODELLING AND PRODUCTION
FORECASTING
• The information on production of crops
before the harvest is very vital to the
national food policy planning and economy
of the country.
• Reliable crop yield estimate is one of the
most important components of crop
production forecasting

17. CASE STUDY
AGRICULTURAL DROUGHT ANALYSIS USING THE NDVI AND
LAND SURFACE TEMPERATURE DATA; A CASE STUDY OF
RAICHUR DISTRICT
S SRUTHI., M.A.MOHAMMED ASLAM
• Agricultural drought is nothing but the decline in the productivity of crops due
to irregularities in the rainfall as well as decrease in the soil moisture, which in
turn affects the economy of the nation.
• Raichur District, of Karnataka is a drought prone region and falls within the
most arid band of the country.
• The purpose of the study is to analyse the vegetation stress in the Raichur
district with the calculation of NDVI values and the land surface temperature
(LST).
• The Combination of (NDVI) normalized difference vegetation index and LST,
provides very useful information for agricultural drought monitoring and early
warning system for the farmers.

18. • By Remote sensing we can check the help what fraction of the
photosynthetically active radiation is absorbed by vegetation.
• Since green vegetation had strong absorption of spectrum in red region and
high reflectance in infrared region, vegetation index was thus generally
formulated as various combinations of red and infrared bands.
• This data can be used to obtain characterize the health of the vegetation
there, relative to the norm with the calculation of NDVI, value of the region
NDVI = (λNIR - λRED) / (λNIR + λRED)
Where, λNIR and λRED are the reflectance in the near infrared (NIR) and Red
bands respectively.
• The digital numbers (DN) of LST data is converted to degree Celsius by
using following formula
Temperature = (DN * 0.02) - 273.15 ºc
•

19. • It can be clearly noticed that both the
parameters are inversely proportional
to each other. When the temperature
is greater, the NDVI value is lesser
which points out the decrease in the
vegetation density.
• The decrease in soil moisture due to
lack or untimely onset of rainfall along
with the increased temperature causes
the agricultural drought to be severe.
• By calculating the correlation between
LST and NDVI, it can be clearly noticed
that they show a high negative
correlation.

20. • NDVI is commonly used parameter due to its
simple calculation and largely used for the
vegetation studies in a regional as well as global
level.
• It is always advisable to combine the NDVI along
with other parameters to get better results.
• The LST when correlated with the vegetation
index it can be used to detect the agricultural
drought of a region.

21. CONCLUSION
• Remote sensing technology has developed from balloon photography
to aerial photography to multi-spectral satellite imaging.
• Some of the benefits that can be gained from the use of remote
sensing -
• Early identification of crop health and stress
• Ability to use this information to do remediation work on the problem
• Improve crop yield
• Crop yield predictions
• Reduce costs
• Reduce environmental impact
• Crop management to maximise returns through the season
• Crop management to maximise returns during harvest time.

22. REFERENCES
• Aggarwal, Shefali. Princple Of Remote Sensing. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. pp.
23-38
• Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). The Guilford Press. ISBN 1-57230-640-8.
• Mulder,V.L. de Bruin,S. Schaepman, M.E. etal . The use of remote sensing in soil and terrain mapping — A review. Received 25
November 2009, Revised 24 November 2010, Accepted 26 December 2010, Available online 5 February 2011.
• Menon, A.R.R. Remote sensing application in Agriculture and forestry. Centre for Environment and Development.
• Rai,Anil. Remote sensing application and GIS application in agriculture. Indian Agriculture Statistics Research Institute.
• S.C. Santra . Remote Sensing. Environmental Science. (Second Edition,2005),page no-477-507,
• S Sruthi., M.A.Mohammed Aslam . Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case
Study of Raichur District. International Conference On Water Resources, Coastal and Ocean Engineering(ICWRCOE 2015).
Pg1258 – 1264
• Shibendu Shankar Ray. S,Neetu. Mamtha etal . Use of Remote Sensing in Crop Forecasting and Assessment of Impact of
Natural Disasters: Operational Approaches in India. Mahalanobis National Crop Forecast Centre. Department of Agriculture &
Cooperation, Ministry of Agriculture.
• Shibendu Shankar Ray. Remote sensing application: Indian Experience. Mahalanobis National Crop Forecast Centre. Department
of Agriculture & Cooperation, Ministry of Agriculture.
• Singh, J.S. Singh,S.P. Gupta,S.P. Gupta,S.R. Remote sensing and GIS. Ecology Environment Science and Conservation.Pg 715-
743.Chand,S publication
• http://phenology.cr.usgs.gov/ndvi_avhrr.php