1. Professor Jayashankar Telangana State Agricultural University
College of Agriculture, Rajendranagar, Hyderabad
Remote Sensing in Agriculture
Submitted to
A.Madhavi lata
Professor
Department of Agronomy
Submitted by
M. Veerendra
RAM/18-07
Department of Agronomy
AGRON 501
2. Introduction
• Remote sensing - sensing things from a distance.
• Art and science of obtaining useful information about an
object with out being in physical contact with it.
without physically contact between the object and sensor”.
• Remote sensing uses the electromagnetic spectrum to image
the land, ocean and atmosphere.
• All objects on the surface of the earth have their own
characteristic spectral signature
Process of Remote Sensing
4. A. Active Remote sensing
• Energy leading to radiation received comes from sensors
• Eg., Radar
B. Passive Remote sensing
• Energy leading to radiation comes from sun
• Eg., Multi spectral scanners
5. Remote sensing platforms
A. Ground based platforms :
• Tripod stands
B. Air based platforms :
• Balloons
• Air crafts
• Drones
C. Space based platforms :
• Earth synchronous satellites
• Sun synchronous satellites
7. Sensors
• It refers to the device that record the electromagnetic
radiation reflected from the object .
• The detection of electromagnetic energy can be
performed either photographically or electromagnetically.
14. SNO SATELLITE/SENSOR SPATIAL
RESOLUTION
TEMPORAL
RESOLUTION
1 NOAA-AVHRR ( swath -2700km) 1100 Twice a day
2 IRS 1C/1D WiFS(810 km) 188 5 days
3 IRS P3 WiFS(810km) 188 5 days
4 RESOURCESAT 1 AWiFS(740km) 56 5 days
Table 1.Satellites and Sensors being used for drought monitoring in NADAMS project
15. Electro Magnetic Spectrum
Light energy is explained as EMR and can be classified according to the
length of the wave. All possible energy channels called as Electro Magnetic
Spectrum (EMS).
Human eyes can only measure visible light but sensors can measure other
portions of EMS.
16. How the Object is Identified by Sensor?
The Basic principle of Remote Sensing is that each object reflect and emit
energy of particular part of EMR in a unique way. Therefore, the
signatures received from different objects is always different. This is
called its Spectral signature. This is the key for interpretation in RS.
17.
18. Spectral Reflectance Curve
Is the plot between the Spectral reflectance (ratio of reflected energy to
incident energy) and wave length.
It depends upon the Chemical composition and Physical conditions.
Typical Spectral reflectance curve for Vegetation, Water & Soil
20. Radiation - Target interactions
1. Absorption (A)
2. Transmission (T)
3. Reflection (R)
Immature leaves contain less chlorophyll than older leaves, they reflect more
visible light and less infrared radiation.
21. There are different remote sensing indices
available such as:
1.Normalized Difference Vegetation Index
2.Vegetation Condition Index
3.Temperature condition index
4.Vegetation health index
22. • The NDVI is a measure of the greenness or vigor of
vegetation. The basic concept of NDVI is that the healthy
vegetation reflects NIR radiation and absorbs RED radiation.
This becomes reverse in case of unhealthy or stressed
vegetation.
NDVI is computed by the formula as:
NDVI= (NIR-RED) /(NIR+RED)
Absorbance and reflectance of radiation in a healthy and unhealthy plant
NORMALIZED DIFFERENCE VEGETATION INDEX
23. It is an NDVI derived index.
• It uses the NOAA-AVHRR NDVI data ,normalises the
geographical differences and creates a possible
comparision between different regions (it filters out the
contribution of local geographic resources to the spatial
variability of NDVI).
• It can estimate the status of vegetation according to the
best and worst vegetation vigour over a particular period
in different years that gives more accurate results as
compared to NDVI while monitoring the drought at
regional scale.
VEGETATION CONDITION INDEX
24. VCI= (NDVI j – NDVI min)/(NDVI max- NDVI min)
Where, NDVIj = NDVI of date j
..it separates the long term ecological signal from the short
term climate signal.
• It is expressed in % ranging from 1 to 100.
50 to 100 = above normal vegetation
50 to 35 = drought condition
below 35= severe drought condition.
25. • During the rainy season in general, it is common for
overcast conditions to prevail for up to three weeks. When
conditions last longer than this, the weekly NDVI values
tend to be depressed, giving the false impression of water
stress or drought conditions.
• To remove the effects of contamination in satellite
assessment of vegetation conditions, Kogan (1995, 1997)
suggested the use of a Temperature Condition Index (TCI).
• The TCI is calculated much in the same way as the VCI,
but its formulation is modified to reflect the vegetation’s
response to temperature (i.e. the higher the temperature
the more extreme the drought).
• Slight changes in vegetation health due to thermal stress
could be monitored using the analysis of TCI data.
TEMPERATURE CONDITION INDEX
26. TCI=100( BT max – BT)/(BT max-BT min)
• where BT, BTmax and BTmin are the smoothed weekly
brightness temperature, multi-year maximum and multi-year
minimum, respectively, for each grid cell.
VEGETATION HEALTH INDEX
VHI= 0.5 (TCI) + 0.5(VCI)
It is the combination of TCI and VCI
The vegetation health index (VHI) has been developed using
the VCI and TCI and is found to be more effective compared to
other indices in monitoring vegetative drought (Kogan 1990,
2001; Singh et al. 2003).
27. Applications of Remote sensing in Agriculture
Crop identification
Crop acreage estimation
Crop condition assessment and stress detection
Identification of planting and harvesting dates
Crop yield modeling and estimation
Identification of pest and disease infestation
Soil moisture estimation
Irrigation monitoring and management
Soil mapping
Monitoring of droughts
Land cover and land degradation mapping
Identification of problematic soils
28. 1. Multi-temporal wheat disease detection by multi-spectral remote sensing
For the implementation of site specific fungicide applications, the spatio
temporal dynamics of crop disease must be well known. With in an experimental
field, a 6ha plot of winter wheat was grown, containing leaf rust caused by
Puccinia recondita. There was an accuracy of 56% during the 4th month and 65%
accuracy during 5th month. The results showed that high resolution multi
spectral data are generally suited to detect in field heterogeneities of crop vigour.
Jonas et al., 2007
29. 2.Variable rate nitrogen fertilizer response in wheat using remote sensing
Figure .2.aSpatial distribution of the
randomly determined N treatments for
the growing seasons 2008/09
Basso et al., 2016
Figure .2.bMap of mean grain yield for
each N plot for the 2008/09 growing
season
30. • N fertiliser application can lead to increased crop yields
but its use efficiency remains generally low which can
cause environmental problems related to nitrate leaching
as well as nitrous oxide emissions.
• This study evaluated three N fertiliser rates (30,70,90 kg
N /ha) and their response on durum wheat yield across
the field.
• As per the remote sensing data treatments applied with
70 kg N shown good performance, which is in good
correlation with the ground based yield data.
Basso et al., 2016
31. Flood monitoring was conducted using optical imagery from the MODIS
to delineate the extent of 2010 flood along the Indus river , Pakistan. A high
correlation was found , as indicated by Pearson correlation coefficient of
above 0.8 for the discharge guage stations located in the south west of
Indus river basin. It can be concluded that RS data could be used to
supplement stream guages in sparsely guaged large basins to monitor and
detect floods.
Sadiq et al., 2014
3.Flood monitoring
Figure 3: Comparing the remote sensing imageries before the occurrence and after
the occurrence of flood
32. • It was found that severe drought condition prevailed during kharif
season of the year 2002 over a large area of Rajasthan. The onset and
extent of drought can be clearly observed from the VCI maps.
• Acute water stress is evident all over the state during the 1st fortnight of
august 2002. From the second fortnight of august 2002 the condition
was quite improved over the eastern part.
Dipanwita et al., 2018
4. DROUGHT MONITORING
Figure 4:Comparing VCI map of 2002 ( drought year) and 2003( normal year)
33. • The use of remote sensing data, followed by site observations is a
powerful tool in detecting salt affected areas.
• As nearly 80% of the saline areas could be delineated, it is a good
indication for the validity of the model. Hence, this model can be
used in similar areas that experience salinization problems. The
simplicity of this model and acceptable degree of accuracy make it a
promising tool for use in soil salinity prediction.
• Combining these remotely sensed and ECe variables into one model
yielded the best fit with R2 = 0.78.
Engdawork et al., 2016
5. SOIL SALINITY MAPPING
34. France, Maize crop having weeds
like Chenopodium album, Cirsium arvense
Figure 6.Example of the spatial and spectral combination results using an SVM
classifier.
(a)Multispectral orthoimage;
(b) Crop (green) and weed (red) location deduced from spatial information;
(c) Weed (green) and crop (red) location deduced from spectral information;
(d) Weed(green) and crop (red) location deduced from the combination of spatial and
spectral information.
Marine etal., 2018
6. Weed management
35. Norway, Carrot field having weeds like Chenopodium album, Poa annua, Stellaria.
Figure 7.Visualization on Drop-on-Demand herbicide application
Utstumo., 2019
7.Precision farming
36. • The label application for Glyfonova Plus ranges from
540 g/ha to 2880 g/ha depending on the types of
weeds and weed pressure (Cheminova AS, 2015).
• A treatment scheme with the robot and the DoD
system, would consist of 2-3 treatments in
combination with mechanical weed control in
between the rows. Building on the experience from
the lab and field trials, we would estimate a total
application of 50 - 150 g/ha glyphosate. This
represent a ten-fold reduction in applied herbicide.
40. Detecting N deficiency with the help of RS techniques
Detecting the spatial extent of soil erosion
41. Advantages of Remote sensing
• Extent of coverage
• Permanent and reliable record
• Speed and consistency of interpretation of data
• Reliable information
• The data are available to multi-disciplinary use
• The process of data acquisition and analysis is
faster
42. Disadvantages of Remote sensing
• Satellite data is too expensive.
• Remotely sensed data is complicated to use.
• Remotely sensed data is not readily available.
• Satellite based remote sensing does not have
sufficient resolution.
43. Problems of Remote sensing in Indian conditions
• Small size of plots.
• Diversity of crops sown in a particular area.
• Variability of sowing and harvesting dates in
different fields.
• Inter cropping and mixed cropping practices.
• Extensive cloud cover during the rainy season.
44. Conclusions
• Remote sensing is a valuable tool in mapping and
monitoring of biodiversity and provides valuable
information.
• It is useful for suggesting action plans and management
strategies for agricultural sustainability of any region.
• Remotely sensed images can be used to identify nutrient
deficiencies, diseases, water deficiency, weed infestations,
insect damage etc.
• Remote sensing technology can be used to assess various
abiotic and biotic stresses in different crop.
• These technologies plays an important role in detecting
and management of various crop issues even at a small land
holding with high resolution.
• Change detection is possible by these technologies in less
time with better accuracy.
45. Kogan, F.N., 1990: Remote sensing of weather impacts on vegetation
in non-homogeneous areas. International Journal of Remote Sensing,
11:1405–1419.
Kogan, F.N., 1997: Global drought watch from space. Bulletin of the
American Meteorological Society, 78:621–636.
Aggarwal, S. Princple Of Remote Sensing. Satellite Remote Sensing and
GIS Applications in Agricultural Meteorology. pp. 23-38
Mulder,V.L., Schaepman, S. 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.
Santra , S.C. 2005: Remote Sensing. Environmental Science. 2:477-507
References
