APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURE

Credit Seminar
on
APPLICATION OF REMOTE SENSING AND
GIS IN AGRICULTURE
SPEAKER
LAGNAJEET ROY
2019-AMJ-34
Department of Agrometeorology

CONTENTS
1. What is Remote Sensing
a) Definition
b) Components
c) Process
d) Types
2) What is GIS and its relationship with Remote Sensing
a) Collection of data
b) Data interpretation and Analysis
c) Mapping and digitizing
d) Georeferencing
e) Raster and vector models
f) True & False color composite
3) Case studies
4) Summary and Conclusion

What is Remote Sensing
Remote sensing can be defined as the science, technology and art of acquiring
information about an object which are not in the physical contact with the
object itself. -Sahu and Solanki
Source – GIS Geography
•DEFINITION:

Components of Remote Sensing
1. A source of Energy
2. Interactions of energy with the atmosphere
3. Interactions of energy with earth surface
4. A sensor with platform
Department of Agricultural Meteorology, CCS HAU Hisar

SOURCE OF ENERGY
The sun provides a very convenient source of
energy for remote sensing. The sun's energy is
either reflected, as it is for visible wavelengths,
or absorbed and then re-emitted, Remote
sensing systems which measure energy that is
naturally available are called passive source.
Ex: MODIS (Moderate Resolution Imaging
Spectroradiometer)/ Sentinel-2
Active source, on the other hand, provide their
own energy source for illumination. The sensor
emits radiation which is directed toward the
target to be investigated. The radiation
reflected from that target is detected and
measured by the sensor.
Ex: PALSAR-2
Source- GIS and Earth

Ok so we read about energy, so what is it?
The answer is Electro Magnetic Radiation and different Kind (Wavelength and
Frequency) of Radiation emit from the source
As we know that planks law is
E= hf
f=c/ λ
That shows wavelength and
frequency are inversely
proportional
Now we will se the different Radiation that are used in Remote sensing
SOURCE - WIKIPEDIA

Interaction with the
atmosphere
Before radiation used for remote sensing
reaches the Earth's surface it has to
travel through some distance of the
Earth's atmosphere. Particles and gases
in the atmosphere can affect the
incoming light and radiation. These
effects are caused by the mechanisms
of scattering and absorption.
Source: NASA
By interacting with atmosphere:
1) It helps in detection of cyclone
2) Helps in detection of cloud and its
type from where it is easy to predict
rainfall or hail storm
3) It helps in detecting the amount of
Dust particle present in atmosphere
specially after dust storm or any
volcano eruption in any region

Atmospheric Window and its importance in remote sensing:
Some types of electromagnetic radiation easily pass through the atmosphere, while other
types do not. The ability of the atmosphere to allow radiation to pass through it is referred
to as its transmissivity, and varies with the wavelength of the radiation. The gases(O3, CO2,
Water vapour,CH4) that comprise our atmosphere absorb radiation in certain wavelengths
while allowing radiation with differing wavelengths to pass through. In contrast to the
absorption bands, there are areas of the electromagnetic spectrum where the atmosphere
is essentially transparent (with no absorption of radiation) to specific wavelengths. These
regions of the spectrum or wavelengths are known as "atmospheric windows“. Visible light
and radio waves can pass relatively freely through the atmosphere, while X-Rays can not.

Interaction with the earth surface:
When electromagnetic energy reaches the Earth's surface there are three possible energy
interactions with the surface feature:
Reflection: occurs when radiation "bounces" off the target and is redirected
Absorption: occurs when radiation (energy) is absorbed into the target
Transmission: occurs when radiation passes through a target
EI(λ) = ER(λ) + EA(λ) + ET(λ)
Where:
EI(λ) = Incident energy (from sun)
ER(λ) = Reflected energy
EA(λ) = Absorbed energy
ET(λ) = Transmitted energy
•Vegetation and soils can reflect approximately 30-50% of the incident energy (across
the entire EM spectrum)
• while water on the other hand reflects only 10% of incident energy. Water reflects
most of this in the visible range, minimal in the NIR( Near Infrared) and beyond 1.2 μm
(mid-infrared) water absorbs nearly all energy.

Reflectance
Reflection occurs when incoming energy bounces off a surface and is reflected back.
The amount of reflection varies with:
•Wavelength of Energy
•Geometry of the Surface
•Surface Materials
The total quantity of incoming energy
(light) from the sun is known as
irradiance. Satellites measure radiance
(brightness), or the amount of
light. Reflectance is the percent of
incoming incident energy that is
reflected. This is always measured as a
function of wavelength and is given as
a percent.
Spectral Reflectance
ρλ = ER(λ) / EI(λ) or
% Reflectance = Amount of Reflected Energy
/Total Energy x 100

Remote sensing and Plants primary target in agriculture
Spectral signatures of crops and soil (Kyllo, 2003).
When electromagnetic
energy from the sun
strikes plants, the
energy will be reflected,
absorbed, or
transmitted
The relationship
between reflected,
absorbed and
transmitted energy is
used to determine
spectral signatures of
individual plants
Spectral signatures
are unique to plant
species.

SENSOR and PLATFORM:
Platforms refer to the structures or vehicles on which remote sensing instruments are
mounted
Ground based - To study properties of a single plant or a small patch of grass, it would
make sense to use a ground based instrument.
Airborne - At present, airplanes are the most common airborne platform. The whole
spectrum of civilian and military aircraft are used for remote sensing applications.
Satellite -- The most stable platform aloft is a satellite, which is spaceborne. There are
two types of satellite: Geostationary satellite and Sun synchronous satellite
SOURCE-
REMOTE SENSING
TECHNOLOGIES FOR
POST-EARTHQUAKE
DAMAGE ASSESSMENT:
A CASE STUDY ON THE
2016 KUMAMOTO
EARTHQUAKE BY
Fumio YAMAZAKI and
Wen LIU

SENSOR
ACTIVE
Imaging
Non-
imaging
PASSIVE
Imaging
Non-
imaging
BASED ON
ILLUMINATION
Sensor
Near UV
region
Visible
region
Infrared
region
Microwave
region
Conventional
camera
Panchromatic,
vidicon
camera
Radiometer
, Optical
mechanical
scanner
Multi spectral
scanner, thematic
mapper, Radiometer
BASED ON SPECTRAL
REGIONS

When we come to know about sensor as the image is digital resolution of a system
refers to its ability to record and display fine details and images are defined in scale.
RESOLUTION
Spatial
resolution
Spectral
resolution
Radiometric
resolution
Temporal
resolution
It refers the size of smallest
possible feature can be
detected (PIXEL). It depends
upon IFOV of the sensor Lower
the resolution clear the picture
It
characterizes
the ability of
the sensor to
resolve energy
received in a
given spectral
bandwidth to
determine
different
constituent of
earth surface(
location)
It mainly
define that
how often the
image is
collected?
That shows
how many
time the
satellite
crossed a
specific
region in a
specific time
period
To distinguish fine
variations in the radiance
values of different objects
help in measuring dark
areas water/shadow

First the
electromagnetic
radiation emitted from
the source
The radiation goes
through atmosphere and
interact with earth surface
( Crop, soil , water bodies
etc.)
Next those surface
materials Reflect
(Specular or Diffused)
some amount of radiation
and the other parts were
Absorbed or transmitted
The reflected radiation
come back to the sensor
that is placed on a
platform
Different types of sensors
are specialized to detect
different wave length
bands of radiation
The reflected EMR is
different for different
bodies as known as
spectral reflectance
The photographic system
(identify soil types, plant
types) and the scanning
system, Microwave
system of sensor help to
collect data
The images we
obtained by sensors
are digital in nature
and it consist of large
no. of pixels
But we didn’t get the
minute specification
in those objects but
there is GIS…
Process of remote sensing

RELATIONSHIP BETWEEN REMOTE SENSING AND GIS (Geographic information system)
REMOTE SENSING
GIS
Provide data input
Different software input
Ultimate Output

This is how both remote sensing and GIS work in Agriculture
A
A. Source
B. Light coming to
plants
C. Plant
D. Reflected energy
sensed by a sensor
in satellite
E. It send information
to ground station
F. Using computer
and software's to
analyze the data
G. Finally reliable info.
Is given to the
farmer that make
their farming easy
Source- GIS and Earth observation

GIS (Geographic information system)
“A GIS is a computer-based system that provides the following four sets of
capabilities to handle georeferenced data:
- Input
- Data management (storage and retrieval)
- Manipulation and analysis
- Output.”
(Aronoff, 1989)
GIS FUNCTIONAL MODULES Data input
Data base
Query and
analysis
Output and
visualization

Database definitions
A database system in which most of the data are spatially indexed, and upon which a set of
procedures are operated in order to answer queries about spatial entities in the database.
Geospatial Data
“Geographically referenced data that describe both the location (geometry) and the
characteristics of spatial features.”
(Chang, 2009)

Components
of GIS
Hardware
+
Software
+
Data
+
People
= GIS
SOURCES OF INPUT DATA
LIDAR
Photogra
mmetry
GPS Remote
sensing
Hard
copy
maps
Total
Stations

Black and White
• : Older and lower cost surveys are collected
on black and white media
Color:
• More recent or higher cost aerial photo
surveys are on color media
Infrared:
• Primary use is vegetation studies as
vegetation is a very strong reflector of
infrared radiation.
AERIAL PHOTOGRAPHY (Main source of data in Agriculture)
1) collection of
measurements
from aerial
photos in the
preparation of
maps
(2) To
determine
land-use,
environmental
conditions, and
geologic
information
(3)Aerial photos
often display a
high degree of
radial distortion
that must be
corrected. GIS is
used then.

HOW WE COLLECT DATA NOW?
Manually digitizing from image
or map sources
• manually drawn maps
• legal records
• coordinate lists with
associated tabular data
• Aerial photographs
Field coordinate measurement
• Coordinate Surveying
GPS Image data
• Manual or automated
classification
• direct raster data entry
DATA SOURCES and INPUT

• Scan map or image
• If image not referenced, collect ground
coordinates of control points
• Digitize control points (tics, reference
points, etc.) of known location
• Transform (register) image to known
coordinate system
• Digitize feature boundaries in stream or point
mode.
• Edit
Digitizing
in GIS is the process of
converting geographic data
either from a hardcopy or a
scanned image into vector
data by tracing the features.
Definition
Manual
digitization
Coordinate
geometry
Global
positioning
system
Geocoding
Heads up
digitizing
Heads-up digitizing of building outlines performed in ArcMap10

Georeferencing is the process of defining exactly where on the earth’s surface
an image or raster dataset was created.
Georeferencing means that the
internal coordinate system of a
digital map or aerial photo can be
related to a ground system of
geographic coordinates.
A georeferenced digital map or
image has been tied to a known
Earth coordinate system, so users
can determine where every point
on the map or aerial photo
is located on the Earth's surface.
Georeferencing in the digital file
allows basic map analysis to be
done, such as pointing and clicking
on the map to determine the
coordinates of a point, to calculate
distances and areas, and to
determine other information.
The relevant coordinate transforms
are typically stored within the
image file (GeoPDF and GeoTIFF
are examples of georeferenced file
formats), though there are many
possible mechanisms for
implementing georeferencing.

Data Preparation for Geologic Mapping
Preparing digital base
map data (i.e.
downloadable or
previously
stored thematic,
topographic or remotely
sensed data, or data that
you digitize, scan
and georeference);
Creating a database and/or
individual files to store data
that will be gathered in the
field (e.g. the locations and
descriptive attributes of
plant units, soil plant unit
contacts, and measured
attitudes)
Creating a map that is
ready for editing in the
field

Image Processing
Geodatasets can be derived from digital imagery. Most commonly satellite imagery is
utilized in a process called supervised classification in which a user selected a sampling of
pixels for which the user knows the type (vegetation species, land use, etc). Using a
classification algorithm, remote sensing software such as ERDAS or ENVI classifies a digital
image into these named categories based on the sample pixels. In contrast to the other
methods discussed, supervised classification results in a raster dataset. Image
restoration(Preprocessing), Image enhancement, classification and information extraction.
Attribute data is
information appended in
tabular format to spatial
features. The spatial data is
the where and attribute
data can contain
information about the what,
where, and why.
ATTRIBUTE DATA
Character
Floating
Integer
Database management system: Info, dBase, Oracle,
Informix, SYBASE, Access, FoxPro etc.

Spatial data models
Two fundamental approaches:
 Raster model
 Vector model

Raster model
The entity information is explicitly recorded for
a basic data unit (cell, grid or pixel)
Rasters can be used to show rainfall
trends over an area, or to depict the fire
risk on a landscape.
This satellite image looks good
when using a small scale...
...but when viewed at a large
scale you can see the individual
pixels that the image is
composed of.

vector model
• In a vector-based GIS data are handled as:
– Points
– Lines
– Areas
X,Y coordinate
X,Y coordinate pair + label series of points
line(s) forming their boundary (series of polygons)
line
feature
area
feature
point
feature

vector model Layers in an vector-based model

Standard overlay operators
take two input data layers;
assume they are georeferenced in the
same system;
overlap in study area.
If either condition is not met, the use of
an overlay operator is senseless.
The principle is to:
compare the characteristics of the
same location in both data layers,
and
to produce a new output value for
each location.
Overlay operation

Jorhat city map Jorhat district map Jorhat road map
Jorhat tehsil(Block) map Jorhat railway map

Natural or True Color Composites
A natural or true color composite is an image
displaying a combination of the visible red,
green and blue bands to the corresponding
red, green and blue channels on the
computer display. Natural color images can
be low in contrast and somewhat hazy due
the scattering of blue light by the
atmosphere.
False Color Composites
False color images are a representation of a
multispectral image produced using any bands
other than visible red, green and blue as the red,
green and blue components of the display. False
color composites allow us to visualize
wavelengths that the human eye can not see
(i.e. near-infrared and beyond).

Different Indices used in agriculture
To define different characteristics in agricultural domain from vegetation to water or
from soil to stress different index are used to define the condition in a simple manner
that we can get from here.

Crop water stress mapping for site-specific irrigation
by thermal imagery and artificial reference surfaces
CASE STUDY: 1
Ground-based measurements were obtained
during the summer of 2007 at a commercial
cotton (Gossypium hirsutum x barbadense
hybrid c.v. Acalpi) field in the Hula Valley of
Israel. The soil at the site is a brown alluvial
hydromorphic gromosol, and the climate is
Mediterranean. The field was selected as an
experimental site from previous
observations of variable crop development,
apparently related to very variable soil
water-holding characteristics caused by the
spatially variable alluvial deposits Infrared scanner mounted on a high pass sprayer
False color IR image of a cotton row:
a location marker and plant height measuring
stick and
b leaf water potential (LWP) sampling points

Cotton CWSI ( Crop water stress index) maps of the field monitored in the Hula Valley
acquired on six separate days by ground survey in 2007. The LWP (Leaf water potential)
sampling points for 18 June are shown in map

Water stress map of a peanut field during irrigation
on 20 August 2007. Mean CWSI values are
indicated ahead and behind the lateral move
position (arrow)
Water stress map of a drip
irrigated process tomato field
on 20 August 2007
Water stress map of a cotton field before last
irrigation on 20 August 2007. Arrows
indicate lateral move positionand pivoting
directions of the irrigation rig. Numbers are
the mean CWSI levels for the East and West
parts of the field. The bold polygon marks the
ground monitored part of the field

Water stress map of a
center pivot irrigated
cotton field after the
last irrigation on 20
August 2007. Scattered
line and arrow indicate
final pivot position and
turning directions of
the irrigation rig
Mean crop stress levels
(CWSI) and their
distribution for the August
20 aerial survey fields
Meron, M., Tsipris, J., Orlov, V., Alchanatis, V., & Cohen, Y. (2010). Crop water stress mapping for site-
specific irrigation by thermal imagery and artificial reference surfaces. Precision agriculture, 11(2),
148-162.
When farmer come to know about the water stress in
the specific position of his field then he can give
irrigation according to the need of the crop to reduce
crop failure and thus remote sensing and GIS provided
the information to the farmer

CASE STUDY - 2
RESEARCH PROPOSAL:
Application of remote sensing and GIS for acreage estimation
of wheat
This study was conducted in five districts situated in central part
of Maharashtra i.e. Pune, Solapur, Ahmednagar, Beed and
Osmanabad wherein spatially, extensive and contiguous sites
contribute to wheat production
Location of
study :
Multi-date, multispectral satellite
images of IRS- P6, AWiFS (Advanced
Wide Field Sensor) Sensor for five
consecutive months of wheat season
(October/November/December
/January/February) of the year 2012-
13 were used for this study (Table 1).
Rectangular subset images covering
the study area were obtained and
processed in ERDAS (Earth Resources
Data Analysis System) Imagine to
generate Normalized Difference
Vegetation Index (NDVI) images on
all the dates of satellite pass.
The study
area and
location of
ground
truth
stations

•Images of Normalized
difference Vegetation Index
(NDVI) on all the dates of
pass were generated using
ERDAS Imagine software and
stack was prepared
•Graphs of NDVI values for
different dates of satellite
image acquisition were
plotted for rabi sorghum,
wheat, sugarcane and other
areas. These plots represent
Reference Temporal Spectral
Profile (RTSPs) of the
respective class
•The signature file so
generated was used to obtain
Temporal Spectral Profile
(TSPs) for each class. These
TSPs were compared visually
with RSTP of wheat crop and
the related classes were
assigned as wheat crop
Classified Image after unsupervised classification
The classified image obtained after unsupervised
classification is shown. It was found that TSP of 15 classes
was matching with RTSP of wheat. The images showing
only wheat crops (recoded images) in the subset area
were obtained. The district wise pixel count of wheat crop
was obtained by applying zonal attributes/majority count
function of ArcGIS. For this process, option of intersection
/union and ignore zero values was selected.

Area under wheat crop in the study area was estimated by remote sensing as 189481 ha
against actual area of 172600 ha reported by Department of Agriculture showing 9.78
percent over estimation. This may be because of similar spectral profiles of coexisting
crops like oats. Wheat being irrigated crop, soil moisture affects the reflectance of crop.
Lowest variation of 6.31% was observed in Ahmednagar district whereas highest variation
12.66% was observed in Pune district.
Crop District RS
Estimat
es (ha
DOA
estimates
(ha)
Deviation (%)
Ahmednagar 41357 38900 6.31
Pune 59708 53000 12.66
Wheat Solapur 30861 28800 7.16
Beed 31805 27700 11.21
Osmanabad 26750 24200 10.53
189481 172600 9.78
RESULTS AND DISCURSSION
Pimpale et al., International Journal of Engineering, Business and Enterprise Applications,
12(2), March-May 2015, pp. 167-171

Software Used
ERDAS IMAGINE 9.0, ARC GIS-9.3
Application of Remote Sensing & GIS in Crop Information System –
a case study of Paddy monitoring in Jamalpur Block
METHODOLOGY
CASE STUDY - 3

RICE TYPES CULTIVATING SEASON
AUS JULY-AUGUST
AMAN Generally DECEMBER-JANUARY
BORO MARCH-MAY
six classes derived from that image, mainly
agricultural land, vegetative land, Water
logged area, Flood prone area, Water
bodies and Built-up land are shown
It is easy to identify after it has been
processed with NIR band of Landsat
TM data
The secondary data has been collected from the ADA office of Jamalpur. These data
obtained to show the production curve of different types of rice and the change of
rice cultivated area in the last five years
Land use and Land cover map of Jamalpur Block

SPATIAL DISTRIBUTION OF AMAN
DERIVED FROM JANUARY 2010 IMAGE
SPATIAL DISTRIBUTION OF AUS
DERIVED FROM JULY 2010 IMAGE
: SPATIAL DISTRIBUTION OF BORO
DERIVED FROM APRIL 2010 IMAGE
YEAR AUS AMAN BORO
Production Cultivated
area
(hector)
area
(hector)
area
(hector)
2007- 08 47600 8,300 41570 14,200 46894 3,800
2008- 09 47350 8,000 33533 12,000 31675 5,000
2009- 10 50300 8,100 42484 12,000 38979 400
2010- 11 40900 7,500 39618 12,300 40621 1,080
Variation
of Rice
cultivable
area
Variation of
Rice
production
Pani, S., Chakrabarty, A., & Bhadury, D. S. (2014). Application of Remote Sensing & GIS
in Crop Information System–a case study of Paddy monitoring in Jamalpur Block. IOSR
Journal of Agriculture and Veterinary Science (IOSR-JAVS) e-ISSN, 2319-2380.

Application of GWQI to Assess Effect of Land
Use Change on Groundwater Quality in
Lower Shiwaliks of Punjab: Remote Sensing
and GIS Based Approach
CASE STUDY :4
Study area and its geomorphology
study area was divided into grids of size
10 × 10 km2
Samples were collected on the basis of
spectral signature as observed on satellite
image from each grid (22 SAMPLES)
The water samples were collected from
nearly same depth (35∼40 m). The pH,
Electrical conductivity and Total dissolved
solids (TDS) meter (HANNA) were used
to measure pH, EC and TDS in the field.
The samples were filtered using vacuum
filtration unit. and analyzed using atomic
absorption spectrophotometer
The study was carried out with the help
of topographic sheets, Garmin Global
positioning system (GPS) and ground
truthing and then GIS for map making

The landsat image of the year 1989 procured from United States
Geological Survey (USGS) and the Linear imaging scanning
system (LISS) III (geo-coded) satellite image of December, 2006
acquired from National Remote Sensing Centre (NRSC),
Hyderabad had been used for the present study. In addition,
toposheets on 1:50,000 scales procured from Survey of India (SOI),
Dehradun, were used for georeferencing the satellite images
. The geographical coordinates
of various LULC classes were
recorded using GPS
The image was re-projected
into World Geodetic System
1984 (WGS-84) spheroid and
datum, zone 43 North of the
UTM projection.
The classified image was finally
recoded into 12 classes

a Landsat data of 1989 (bands 4, 3, 2 and
1) and land use/land cover of study area. b
IRS LISS III data of 2006; bands 3, 2, and
1) and land use/land cover of study area
Class Area in km2 Area in km2
in 1989 in 2006
River 33.17 27.32
Settlement 17.56 111.23
Cropland 882.09 690.84
Fallow land 361.03 649.86
Dense forest 487.88 335.66
Salt affected land 11.70 21.46
Canal 13.66 13.66
Water body 17.56 10.73
Seasonal streams 33.17 21.46
Plantation 33.17 12.68
Land with scrub 17.56 52.69
Land without scrub 62.44 23.41
Area covered by LULC classes in year 1989 and 2006

The water quality parameters that were analysed are given
GWQI = Anti log[∑Wn=1log10qn]
Groundwater GWQI
Very good 0-25
Good 25-50
Moderate 50-75
Poor 75-100
Very poor 100-125
Unfit >125
Singh, C.K., Shashtri, S.,
Mukherjee, S. et al. Application of
GWQI to Assess Effect of Land
Use Change on Groundwater
Quality in Lower Shiwaliks of
Punjab: Remote Sensing and GIS
Based Approach. Water Resour
Manage 25, 1881–1898 (2011)

DEVELOPMENT OF AN AIRBORNE REMOTE
SENSING SYSTEM FOR CROP PEST MANAGEMENT:
SYSTEM INTEGRATION AND VERIFICATION
CASE STUDY:5
The MS‐4100 is currently a
Geospatial Systems, Inc.
product. It is a multi‐spectral
HDTV (High Definition
Television) format 3‐CCD
(Charge‐Coupled Device)
color/ CIR digital camera
MS‐4100 spectral configurations
RGB Red (660 nm with 40‐nm bandwidth), Green
(540 nm with 40‐nm bandwidth), and Blue
(460 nm with 45‐nm bandwidth)
‐color imaging
CIR Red (660 nm with 40‐nm bandwidth), Green
(540 nm with 40‐nm bandwidth), and NIR (800
nm with 65‐nm bandwidth)
‐color infrared imaging
RGB/CI
R
RGB and CIR in a single camera
Multi‐spectral Custom spectral configuration to
customer specifications
STUDY AREA

An image of the cotton field
on 20 September 2007
illustrates the RMS values of seven georeferenced
images acquired using the camera control system with
automatic control of roll, pitch, and yaw camera
stabilization during the flights for this research. With
the flight altitude of 2600 m, the image resolution
was 1.56 m/pixel for the MS‐4100 camera.
RGB digital image and georeferenced image with
overlay of GIS polygon of the big pivot west field.

Reflectance = H * Radiance / Irradiance The relationship is pixel radiance divided by
solar irradiance illuminating the target (H =
3.1416):
By ground truth field inspection, it
was discovered that a number of
abandoned irrigation structures
remained in Region A. Region B was
infested with cotton root rot disease
A
B
we found that the images of NIR and red bands
indicated the existence of root rot regions. The green
band showed mostly noise and did not present visual
differences. It appears by subjective evaluation of that
the NIR and red images and possible band
combinations such as NIR/red ratio and NDVI
(Normalized Difference Vegetation Index) are probably
sufficient to identify the region of root rot infestation
CIR
RED
NIR
GREEN
CIR AOI and individual band AOI images

8-20-07
9-14-07 9-20-07
9-27-07
10-2-07 10-5-07
10-11-07
NIR images of the root rot infested region on different dates.
Lan, Y., Huang, Y., Martin, D. E., & Hoffmann, W. C. (2009). Development of an airborne
remote sensing system for crop pest management: system integration and
verification. Applied Engineering in Agriculture, 25(4), 607-615.
This paper show us the respective day to day variation in cotton plant after cotton root
rot disease. The no of days increasing that show in this NDVI map that the dark patches
are increasing and that showing the disease infestation vigourity and that help farmer if
he know about this situation.

Estimation of Soil Erosion Using Remote
Sensing and GIS, Its Valuation and Economic
Implications on Agricultural Production
CASE STUDY : 6
E30 model for estimating soil
erosion using NDVI
The soil erosion model given in
Equation 1 was used to estimate the
annual rate of soil erosion in the Mae Ao
watershed (Honda, 1993, 1996 and
1998). This model is mainly governed
by slope gradient and vegetation index
and the annual soil erosion rate (E) is
defined as:
E = E30 (S/S30)0.9
(1)
where S= gradient of the point under
consideration, S30= tan (30), and E30=
rate of soil erosion at 30 slope and
defined as given below
The Normalized Difference Vegetation Index
(NDVI) as defined by Equation 3 was used to
assess the vegetative cover. To avoid negative
values and for easy handling of digital data,
NDVI value obtained for Landsat-TM data
(30m spatial resolution) were re-scaled as
shown in Equation 3.
E30  Exp[(log0.132-log17.12)
NDV Im ax  NDV Imin
( NDVI  NDV Imin ) 
Log17.12] …………..(1)
NDVI = [( Band 4 - Band 3 ) + 1] *100 …….(2)
Band 4 + Band 3
The maximum and minimum rates of soil
erosion at 30◦ slope in the study area
collected from field stations were
17.12 mm/year and 0.132 mm/year in the
study area as shown in Equation 2

Two Landsat TM images from 1992 and 1996 were used in this study and the necessary
radiometric correction was done by using the 1996 Landsat TM data as the base image
linear interpolation was carried out to make radiometric correction of 1992 Landsat TM
data.
Corrected NDVI of 1992 =
[( Maximum96  Minimum96
) 
Maximum92  Minimum92
( NDVI 92  Minimum92 )]
 Minimum96 …….(3)
Landsat-TM data
NDVI
E30 value
Soil erosion estimation
Soil erosion map
Topographic map
DEM
Slope gradient
Soil Erosion in each
soil-mapping units
NPK content in each
soil-mapping units Loss of NPK from each
soil-mapping units
Market price of NPK Cost of lost NPK in
each soil-mapping units
Cost of soil erosion in
the study area
Methodology for estimating the
annual soil erosion rate
Methodology for estimating the
cost of soil erosion

By calculating thevalue for each pixel using Equation 1, soil erosion from each pixel with
a different slope was calculated using Equation 1. A raster map of slope gradient was
prepared with a pixel size of 30m (same as Landsat-TM data), using a Digital Elevation
Model (DEM) to provide the slope information for Equation 1
Soil erosion map showing average annual
rate of erosion in 1992.
Soil erosion map showing average
annual rate of erosion in 1996
The average annual
rate of soil erosion in
the study area in1992
and 1996 is shown in
Figure 3 and Figure
4, respectively. The
average annual soil
erosion rate in the
study area decreased
from 1.24 mm/year in
1992 to 0.91
mm/year in 1996.
plantation program
taken up
Hazarika, M. K., & Honda, K. (2001). Estimation of soil erosion using remote sensing and
GIS: Its valuation and economic implications on agricultural production. Sustaining the
global farm, 1, 1090-1093.

RESULT AND CONCLUSION
Remote sensing and GIS combination of both of them help in agricultural
activities.
It is easy to get the information about that area where human cannot check
the condition everyday and help in gathering the data
From disease estimation to stress factor due to water from ground water
quality index to acreage estimation in various way agriculture is being profited
by the application of remote sensing and GIS in agriculture
The application of those software or techniques are very new to the
agriculture domain still much more exploration is needed in this part
New software are developing in different parts of the world and remote
sensing,GIS is used in various other factors not only in agriculture like any kind of
mapping pattern may be from the area estimation from Australian bush fire to
the statistical analysis of covid 19 affected people.
Today farmers are understanding the beneficiaries of this kinds of techniques
to the farm field which help in increasing productivity that will help future
generation as technology is a hype in traditional system of farming

APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURE

APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURE

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