This presentation cover description of microwave remote sensing, Active and Passive Microwave remote sensing, RADAR, Slant range distortion like Foreshortening and Layover, Sar image and some Recent works in where microwave remote sensing has used to detect natural calamities

1. Role of Microwave Remote Sensing for Evaluation of
Agriculture Crop Damage by Natural Calamities
Name of Supervisors:
Dr. Bholanath Roy
Asst. Prof. ,Department of CSE
MANIT, Bhopal
Presented By:
Rahul Singh
Ph. D Scholar,
Department of CSE, MANIT

2. Outline
• Microwave Remote Sensing
• Active Microwave Remote Sensing
• Passive Microwave Remote Sensing
• RADAR Basics
• Viewing Geometry and Spatial Resolution
• Slant Range Distortion
• Foreshortening
• Layover
• SAR Image
• Recent works in Natural Calamities
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3. Microwave Remote Sensing
• Microwave remote sensing covers EM spectrum in the range from
approximately 1mm to 1m.
• Because of their long wavelengths, compared to the visible and
infrared, microwaves have special properties that are important for
remote sensing.
• Longer wavelength microwave radiation can penetrate through cloud
cover, haze, dust, and all but the heaviest rainfall as the longer
wavelengths are not susceptible to atmospheric scattering which
affects shorter optical wavelengths.
• This property allows detection of microwave energy under almost all
weather and environmental conditions, so that data can be collected
at any time.
• Microwave remote sensing can be divided in 2 main categories
• Active microwave remote sensing
• Passive microwave remote sensing
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4. Active Microwave Remote Sensing
• Active microwave sensors provide their own
source of microwave radiation to illuminate the
target
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5. Passive Microwave Remote Sensing
• A passive microwave sensor detects the naturally
emitted microwave energy related to the
temperature and moisture properties of the
emitting object or surface within its field of view.
• Passive microwave sensors are typically
radiometers or scanners and an antenna is used to
detect and record the microwave energy.
• Because the wavelengths are so long, the energy
available is quite small compared to optical
wavelengths, thus, the fields of view must be large
to detect enough energy to record a signal.
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6. Active Microwave Remote Sensing
• Provides their own source of microwave radiation to illuminate the
target.
• Mainly of two types:
a) Imaging
b) Non-imaging
• The most common form of imaging active microwave sensors is
RADAR (RADAR is an acronym for RAdio Detection And Ranging).
• The sensor transmits a microwave (radio) signal towards the target
and detects the backscattered portion of the signal.
• The strength of backscattered signal is measured to discriminated
between different targets and the time delay between the
transmitted and reflected signals determines the distance (or range)
to the target.
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8. RADAR Basics
• A radar is essentially a ranging or distance
measuring device.
• It consists fundamentally of a transmitter, a
receiver, an antenna, and an electronics system
to process and record the data.
• The transmitter generates successive short
bursts (or pulses of microwave (A)) at regular
intervals which are focused by the antenna into a
beam (B).
• The radar beam illuminates the surface obliquely
at a right angle to the motion of the platform.
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9. RADAR Basics
• The antenna receives a portion of the
transmitted energy reflected (or
backscattered) from various objects
within the illuminated beam (C).
• By measuring the time delay between
the transmission of a pulses and the
reception of the backscattered “echo”
from different targets, their distance
from the radar and thus their location
can be determined.
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10. Viewing Geometry and Spatial Resolution
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11. Viewing Geometry
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12. Slant Range Distortion
• Foreshortening occurs when the
radar beam reaches the base of a tall
feature tilted towards the radar (e.g.
a mountain) before it reaches the
top.
• Because the radar measures distance
in slant–range, the slope (a to b) will
appear compressed and the length
of the slope will be represented
incorrectly (a’ to b’) at the image
plane.
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13. Foreshortening
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14. Slant Range Distortion
• Layover occurs when the radar
beam reaches the top of a tall
feature (b) before it reaches
the base (a).
• The return signal from the top
of the feature will be received
before the signal from the
bottom.
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15. SAR Image
• A digital SAR image can be seen as a
mosaic (i.e. a two dimensional array
formed by columns and rows) of
small picture elements (pixels).
• Each pixel gives a complex number
that carries amplitude and phase
information about the microwave
field backscattered by all the
scatters (rocks, vegetation, buildings
etc.) within the corresponding
resolution cell projected on the
ground.
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• Different rows of the image are associated with different azimuth locations,
whereas different columns indicate different slant range locations.

16. Recent works
• Title: Limitations of cloud cover for optical remote sensing of agricultural
areas across South America[2]
• The monitoring of agricultural areas is highly affected by cloud cover
frequency (CCF), especially in the rainy season, and the implications of
cloud cover for Optical remote sensing (ORS) of agricultural areas are still
poorly understood in South America.
• The combination of multi-sensors (e.g. microwave and optical sensors) and
CubeSats can improve the earth observation frequency and help to work
around this limitation.
• The development of sensors with a better temporal resolution, the use of
microwave sensors, and the combination of optical and microwave sensors
are presented as methods to overcome the limitations of data availability
for the monitoring of the agricultural croplands highlighted in this study.
• CubeSats[3] are built to standard dimensions of 10 cm x 10 cm x 10 cm.
Their weigh can be less than 1.33 kg.
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17. Recent works
• Title: Near Real Time Crop Loss Estimation using Remote Sensing Observations[1]
• This study addresses the problem of near-real-time qualitative crop loss
assessment due to tropical Gaja cyclone in the affected district of Thanjavur,
Tamil Nadu, India, using the temporal data from Sentinel 1 and 2 satellites.
• As a first step, they used time series data of Sentinel1 available between Aug.-
Nov. 2018 to map the Kharif rice area.
• Further, the second step involved the estimation of crop loss using NDVI derived
from Sentinel-2 observations.
• Also, cloud-free Sentinel 2 scenes available during Mar.-May. 2018 have been
used to map the Coconut area.
• Google maps satellite layer was used as a base map for identification of other
non-crop classes (i.e., forest, water, etc.).
• Two crop loss scenarios, namely minimum damage and maximum damage, were
identified for both the crops.
• They have used the time series of satellite based optical and SAR observation.
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18. Recent works
• Title: Response of winter wheat to spring frost from a remote sensing
perspective: Damage estimation and influential factors
• In this study[4], they proposed a remote sensing-based index (SFDI)
that can quantify the continuous impacts of spring frost on winter
wheat over a large area rapidly and effectively.
• Compared with the existing methods, the new index was easy to
implement, and the end date can be set flexibly according to the
application purpose.
• They have used MODIS nadir BRDF-adjusted daily reflectance data
(MCD43A4) from 2001 to 2018 from the NASA EARTHDATA website
(https://earthdata.nasa.gov/).
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19. References
[1] Suryakant Sawant, et al. , “Near Real Time Crop Loss Estimation
using Remote Sensing Observations”, IEEE, 2019.
[2] Victor Hugo Rohden Prudente, et al., “Limitations of cloud cover for
optical remote sensing of agricultural areas across South America”,
Remote Sensing Applications: Society and Environment, 2020.
[3] https://www.nasa.gov/mission_pages/cubesats/overview.
[4] Shuai Wang et al., “Response of winter wheat to spring frost from a
remote sensing perspective: Damage estimation and influential
factors”, ISPRS Journal of Photogrammetry and Remote Sensing, 2020.
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20. Thank You
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