Applications of Remote Sensing
in Site-Specific Management
Precision farming introduction
Use of remote sensing in precision farming
Precision farming introduction
Precision farming is an emerging agricultural technology that involves
managing each crop input on a site-specific basis to reduce waste,
increase profits, and maintain the quality of the environment.
Remote sensing is a technology that can be used to obtain various
spatial layers of information about soil and crop conditions.
It allows detection and/or characterization of an object, series of
objects, or landscape without having the sensor in physical contact.
Use of remote sensing in precision
farming Typically remote sensing is conducted by positioning a sensor above
the object (target) being observed.
Platforms that support the sensors vary, depending on the altitude above
Today two main observation platforms are used to collect remote
• Aircraft-based (aerial) and satellite-based.
• Ground-based sensors also have been used for certain specific
applications and research studies.
Most remotely sensed data can be imported into Geographic
Information System (GIS) software to overlay with other layers of
information such as yield maps, field boundaries, soil sampling
locations, and analytical results.
A hard copy of obtained imagery (like from 35 mm film) also can be
produced to visually compare observed field patterns and to identify
Each image should be scaled and oriented according to true
geographic coordinates of every pixel.
Once the data are in digital format, various analytical algorithms can
Fig 01: Background image Fig 02: USDA soil survey boundaries
superimposed on bare soil image.
The most common indirect use of remote sensing images is as a base
map onto which other information can be layered using GIS
A georeferenced image provides a background for ownership
boundary, yield, and fertility maps.
Illustrates a remote sensing image used as a background for different
spatial data layers.
all layers must be georeferenced to the same geographic projection
in order to display properly.
Other applications include: Use of derived vegetation parameters
in crop simulation models, defining soil and plant parameters to
improve soil sampling strategies, and discovering and locating crop
stresses such as weeds, insects, and diseases.
These data can be used to identify areas of the field with similar
physical soil properties.
This method has not been widely accepted as an adequate method of
soil mapping because the reflectance characteristics of the desired soil
properties often are affected by variability in soil moisture content, crop
residue coverage, surface roughness, atmospheric conditions, solar
zenith angle, and view angle.
Digital Orthophoto Quadrangles (DOQ) are the most popular examples
of imagery that can be downloaded from various data bank web sites.
Experimentation in this area has been ongoing since before the launch
of NASA’s first Earth Resources Technology Satellite (ERTS-1, now
known as Landsat) in 1972.
Yield in two ways:
Focused on crop-growth models, uses remote sensing as a calibration
tool for a particular model.
Approach involves estimating yield based on vegetative indices such
as the Normalized Difference Vegetation Index (NDVI)).
To help reduce the time required and spatial uncertainty associated
with irrigation scheduling, remote sensing can be used to help
automate and perhaps more accurately schedule irrigation.
Research to improve irrigation scheduling with remote sensing
technologies has been conducted using on-site, airborne, and satellite
sensors to accurately detect canopy temperature, pigment content and
composition, vegetation indices, leaf cell structure,
canopy architecture, and leaf-water content.
One technique for detecting crop stress (including that due to a water
deficit) is visible.
G. Dry dale and G. Metternich, (2000). Remote sensing for site-specific crop
management: Evaluating the potential of digital multi-spectral imagery for monitoring crop
variability and weeds within paddocks. Precision Agriculture (PA) has been defined as
‘observation, impact assessment and timely strategic response to fine-scale variation in
causative components of an agricultural production process’, and thus may cover a range of
agricultural enterprises, and can be applied to pre- and post-production aspects of
Robert, (2001). Remote Sensing for Crop Management, Scientists with the Agricultural
Research Service (ARS) and various government agencies and private institutions have
provided a great deal of fundamental information relating spectral reflectance and thermal
emittance properties of soils and crops to their agronomic and biophysical characteristics.
This knowledge has facilitated the development and use of various remote sensing methods
for non-destructive monitoringof plant growth and development and for the detection of
many environmental stresses which limit plant productivity.
Adamchuk, Viacheslav I.; Perk, Richard L.; and Schepers, James, (2003). Precision
Agriculture: Applications of Remote Sensing in Site-Specific Management" Precision
farming is an emerging agricultural technology that involves managing each crop input on
a site-specific basis to reduce waste, increase profits, and maintain the quality of the
environment. Remote sensing is a technology that can be used to obtain various spatial
layers of information about soil and crop conditions. It allows detection and/or
characterization of an object, series of objects, or landscape without having the sensor in
David R. Shaw, (2004). Translation of remote sensing data into weed management
decision remote sensing and associated spatial technologies provide tremendous
opportunity to enhance weed management and improve–protect the environment through
judicious use of the most efficacious control methods for a given site.
Shweta Karsauliya, (2009). Department of Remote Sensing, Site suitability criteria of
solid waste is mainly focused on two categories physical and socially. River Yamuna
which is one of the largest tributaries of River Ganga and one of the most prominent rivers
of our country is getting deteriorated due to various human activities, unfortunately
certain stretches of River Yamuna are much polluted due to deposition of solid wastes
from various sites.
Case Study – 1
TITILE -A Case Study of Surroundings of River Yamuna, India
AUTHORS -Shweta Karsauliya Department of Remote Sensing,
Banasthali University, Rajasthan.
JOURNAL- (Ozeair Abessi at.2009).
Water pollution in the river Yamuna is severely caused due to the
solid wastes, that is disposed into the river through various sites in
Wastes being solid in state are non biodegradable which cannot be
decomposed by the micro organisms.
Hence these wastes lead to water pollution in the river. Waste
remains in water forever until it is separated out by any physical
Study area is located in the western part of Uttar Pradesh state which is
the surroundings of Yamuna River. It covers some parts of three big
cites Agra, Mathura, Firozabad.
15Fig 03: Study Area.
Data used and methodology
Visual interpretation LANDSAT-TM imagery is done using Google
earth and image interpretation elements like tone, texture, association,
shape, size, pattern and different erosion characteristics.
Weightage assigned for each categories:
Land use land cover
Land use land cover refers to the type of material present on the land
space, Information of the area land use land cover can be get from the
Fig 04: Land use land cover map Fig 05: Study area classification
TITILE- Remote sensing for site-specific crop management:
Evaluating the potential of digital multi-spectral imagery for
monitoring crop variability and weeds within paddocks
AUTHORS-G. Dry dale* and G. Metternich**
JOURNAL- Whelan and McCartney, (2000).
Precision Agriculture (PA) has been defined as ‘observation, impact
assessment and timely strategic response to fine-scale variation in
causative components of an agricultural production process’, and thus
may cover a range of agricultural enterprises, and can be applied to pre-
and post-production aspects of agricultural enterprises (Australian
Centre for Precision Agriculture, 2002).
Site-specific crop management (SSCM) is one facet of precision
agriculture and is defined as ‘matching resource application and
agronomic practices with soil and crop requirements as they vary in
space and time within a field’ (Whelan and McBratney, 2000).
Data sets and study area
The DMSI system
The field selected for the study falls in the northern region of the shire of
Wickepin, located in the South West of Western Australia.
The DMSI was captured using SpecTerra Services Digital Multi-Spectral
Camera (DMSC), which is comprised of four 12 bit digital CCD cameras
recording 1024 pixels of 1024 pixels per line.
Field data collection
During the seeding operations for cereal crops, generally, a
constant seed rate is set for the entire paddock. The number of
plants established depends on factors such as soil moisture,
surface crusting, seedling vigour, sowing depth, fertiliser level,
disease and insect attack (Madin et al., 1993).
Fig 08: Quadrants of the field transect:
a) Sample site C5 showing high density of canola and weeds;
b) Sample site C14 showing low density of canola and weeds.
Fig 09: Transect field samples:
a) Sample C5 showing an average canola height of 20cm;
b) Sample C14 showing and average canola height of 4cm.24
Using these remote sensing techniques we are able to get the total soil
strategy from external point of view as well as internal point of view.
But these remote sensing techniques are not being used by many people,
so there is a need to improve these techniques so that every farmer can
do his work in less time.
These techniques should be made economic such that every individual
can use it.
Adamchuk, Viacheslav I.; Perk, Richard L.; and Schepers, James S.,
"EC03-702 Precision Agriculture: Applications of Remote Sensing in Site-
Specific Management" (2003). Historical Materials from University of
Nebraska-Lincoln Extension. Paper 705.
David R. Shaw, (2004). Translation of remote sensing data into weed
management decision remote sensing and precision agriculture: ready for
harvest or still maturing? Photogram. Eng. Remote Sens 65:1118–1123.
G. Drysdale and G. Metternicht (Whelan and McCartney, 2000). Remote
Sensing For Site-Specific Crop Management: Evaluating the Potential of
Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds
within Paddocks. Department of Spatial Sciences Curtin University of
Technology GPO Box U 1987, Perth, WA 6845 27
Robert, (2001). Remote Sensing for Crop Management.
Photogrammetric Engineering & Remote Sensing Vol. 69, No. 6, June
2003, pp. 647–664.
Shweta Karsauliya, (Ozeair Abessi at 2009). Application of Remote
Sensing and GIS in Solid Waste Management: A Case Study of
Surroundings of River Yamuna, India. International Journal of
Environmental Engineering and Management. ISSN 2231-1319,
Volume 4, Number 6 (2013), pp. 593-604.