SlideShare ist ein Scribd-Unternehmen logo
1 von 50
Rayleigh Scattering
and
Mie scattering:
Dr. P. K. Mani

Bidhan Chandra Krishi Viswavidyalaya
E-mail: pabitramani@gmail.com
Website: www.bckv.edu.in
Remote Sensing and its Applications in Soil Resource Mapping (ACSS-754)

Absorption.
Scattering
The atmosphere affects electromagnetic energy through absorption, scattering
and reflection.
How these processes affect radiation seen by the satellite depends on the path
length, the presence of particulates and absorbing gases, and wavelengths
involved.
transmitted
absorbed,
emitted and
scattered by
aerosols and
molecules

transmitted

absorbed
&scattered

emitted

reflected

transmitted
reflected

absorbed

emitted

Land

emitted

reflected

transmitted absorbed

Ocean

Figure-... Process of Atmospheric Radiation
Rayleigh Scattering: why the sky is blue
EM radiation from the sun interacts with the atmospheric constituents
and gets absorbed or scattered. Essentially two types of scattering
takes place:
Elastic scattering in which the energy of radiation is not changed due
to the scattering, and
inelastic scattering in which the energy of the scattered radiation is
changed.
3 types of elastic scattering
is recognized in atmospheric scattering
Rayleigh scattering
Mie scattering
Nonselective scattering
Radiation scattered from a particle depends on:
Size;
Shape;
Index of refraction;
Wavelength of radiation;
View geometry.
For Rayleight scattering, λ >> φ
•Scattering is diffuse (in all directions) and λ dependent or selective
• Scattering = 1/ λ4
For Mie scattering,
λ ≈φ
Where φ is particle size.
 Scattering properties of such aerosols as smoke, dust, haze in
the visible part of the spectrum and of cloud droplets in the IR
region can be explanined by Mie scattering,
While of air molecules in the visible part can be explained by
Rayleigh Scattering
Rayleigh Scattering:
In Rayleigh scattering the volume scattering coefficient σλ is given by :

σλ

[4π
=

2

NV

λ

4

2

] ⋅ [µ

2

[µ

2

]
]

− µ0

2 2

+ µ0

2 2

=

const

λ

4

N= no. of particles/cm2
…. V= vol. of scattering particles
λ = wavelength of radiation … µ= refractive index of the particles
µ0= refractive index of the medium
Because of Rayleigh scattering Multispectral remote sensing data
from the blue portion of the spectrum is of relatively limited
usefulness. In case of aerial photography, special filters are used to
filter out the scattered blue radiation due to haze present in the
atmosphere.
Mie scattering
a2

σ λ =10 π ∫ N (a ) K (a, µ)a da
5

2

a1

σλ = Mie scattering coefficient at wavelength λ
N(a) = no. of particles in interval of radius a and a + da
K(a, µ) = scattering coefficient(cross section ) as a function of
spherical particles of radius a and the refractive index of the
particles µ
Mie scattering usually manifests itself as a general deterioration of
multispectral images across the optical spectrum under conditions of
heavy atmospheric haze
Nonselective Scattering

 Particles are much larger than the wavelength λ >> l
All wavelength are scattered equally
Effects of scattering
 It causes haze in remotely sensed images
 It decreases the spatial detail on the images
 It also decreases the contrast of the images


Water droplets with diameters ranging from 5-100 µm scatter all wavelengths of
visible light with equal efficiency. As a consequence, clouds and fog appear
whitish because a mixture of all colours in approximately equal quantities produces
white light.

Non selective scattering usually results when the atmosphere is
heavily dust and moisture ladden and results in a severe attenuation
of the received data. However, the occurrence of this scattering
mechanism is frequently a clue to the existence of large particulate
matter in the atmosphere above the scene of interest, and sometimes
this in itself becomes useful data.
Atmospheric scattering process
Scattering
process
Rayleigh
Mie
Nonselective

Wavelength Particle size
dependence
μm
λ-4
<<0.1
λ0 to λ-4

0.1-10

λ0

>10

Kind of
particles
Air molecules
Smoke ,
fume, Haze
Dust , Fog,
Cloud

Nonselective scattering occurs when the particles are much larger
than the wavelength of the radiation. Water droplets and large dust
particles can cause this type of scattering. Nonselective scattering gets
its name from the fact that all wavelengths are scattered about equally.
This type of scattering causes fog and clouds to appear white to our
eyes because blue, green, and red light are all scattered in
approximately equal
Atmospheric Windows
 Atmospheric windows define wavelength ranges in which
the atmosphere is particularly transmissive of energy.
 Visible region of the electromagnetic spectrum resides
within an atmospheric window with wavelengths of about
0.3 to 0.9 µm
 Emitted energy from the earth's surface is sensed through
windows at 3 to 5 µm and 8 to 14 µm.
 Radar and passive microwave systems operate through a
window region of 1 mm to 1 m.
Selective transmission of EMR by Earth’s
atmosphere

Transmission through the atmosphere is very selective.
Very high for wavelengths 0.3-1 µm and >1cm,
moderately good for 1-20 µm and 0.1-1 cm, and
very poor for <0.3 µm and 20-100 µm. This defines the
“ATMOSPHERIC WINDOWS”.
Those wavelength ranges in which radiation can pass through
the atmosphere with relatively little attenuation.
atmospheric windows.
C. Interaction with Target

What the remote sensor is really measuring is how the energy
interacts with the target.
There are three (3) forms
of interaction that can take place
when energy strikes, or is incident
(I) upon the surface. These are:
Absorption (A);
Transmission (T);
Reflection (R).

Specular reflection

Diffuse reflection.
Leaves: chlorophyll strongly
absorbs radiation in the R and B but
reflects (G)green wavelengths.
Internal structure of healthy leaves
act as excellent diffuse reflectors of
near-infrared (NIR) wavelengths. In
fact, measuring and monitoring the
NIR reflectance is one way that can
determine healthiness of vegetation
Water: Longer λ visible and near
infrared radiation is absorbed more
by water than shorter visible
wavelengths. Thus water typically
looks blue or blue-green due to
stronger reflectance at these
shorter wavelengths,
Spectral Reflectance Signature
When solar radiation hits a target surface, it may be transmitted,
absorbed or reflected. Different materials reflect and absorb
differently at different wavelengths.
The reflectance spectrum of a material is a plot of the fraction
of radiation reflected as a function of the incident wavelength and
serves as a unique signature for the material.
In principle, a material can be identified from its spectral reflectance
signature if the sensing system has sufficient spectral resolution to
distinguish its spectrum from those of other materials. This premise
provides the basis for multispectral remote sensing.
Spectral reflectance: the reflectance of electromagnetic energy at
specified wavelength intervals
Spectral signatures are the specific combination of emitted, reflected
or absorbed electromagnetic radiation (EM) at varying wavelengths
which can uniquely identify an object.
The spectral signature of an object is a function of the incidental EM
wavelength and material interaction with that section of the
electromagnetic spectrum.

Spectral Signature: Quantitative measurement of the properties of an
object at one or several wavelength intervals
For example, at some wavelengths, sand reflects more energy than green
vegetation but at other wavelengths it absorbs more (reflects less) than
does the vegetation.
In principle, we can recognize various kinds of surface materials and
distinguish them from each other by these differences in reflectance.
Of course, there must be some suitable method for measuring these
differences as a function of wavelength and intensity (as a fraction of the
amount of irradiating radiation).
Using reflectance differences, we can distinguish the four common surface
materials (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty
water), shown in the next figure. Please note the positions of points for each
When we use more than two wavelengths, the plots in multidimensional space tend to show more separation among the materials.
This improved ability to distinguish materials due to extra
wavelengths is the basis for multispectral remote sensing
I-11: Referring to the above spectral plots, which region of the spectrum
(stated in wavelength interval) shows the greatest reflectance for a)
grasslands; b) pinewoods; c) red sand; d) silty water. At 0.6
By measuring the energy that is reflected (or emitted) by targets on
the Earth's surface over a variety of different wavelengths, we can
build up a spectral response for that object.
Vegetation has a unique spectral signature that enables it to be
distinguished readily from other types of land cover in an
optical/near-infrared image.
The reflectance is low in both the blue and red regions of
the spectrum, due to absorption by chlorophyll for
photosynthesis. It has a peak at the green region.
In the near infrared (NIR) region, the reflectance is much
higher than that in the visible band due to the cellular structure
in the leaves.
 Hence, vegetation can be identified by the high NIR but
generally low visible reflectance.
The reflectance of clear water is generally low. However,
the reflectance is maximum at the blue end of the spectrum and
decreases as wavelength increases.
Hence, water
appears dark bluish to the visible eye.
Turbid water has some sediment suspension that increases the
reflectance in the red end of the spectrum and would be brownish
in appearance.
 The reflectance of bare soil generally depends on its
composition. In the example shown, the reflectance increases
monotonically with increasing wavelength. Hence, it should appear
yellowish-red to the eye.
The shape of the reflectance spectrum can be used for
identification of vegetation type.
For example, the reflectance spectra of dry grass and green
grass in the previous figures can be distinguished although
they exhibit the generally characteristics of high NIR but low
visible reflectance.
• Dry grass has higher reflectance in the visible
region but lower reflectance in the NIR region.
For the same vegetation type, the reflectance spectrum also
depends on other factors such as the
• leaf moisture content
• health of the plants.
These properties enable vegetation condition to be
monitored using remotely sensed images.
Vegetation generally has low reflectance and low transmittance in
the visible part of the spectrum. This is mainly due to plant pigments
absorbing visible light. Chlorophyll pigments absorb violet-blue and
red light for photosynthetic energy. Green light is not absorbed for
photosynthesis and therefore most plants appear green.
In the autumn, some plant leaves turn from green to a brilliant yellow.
This change in foliage color is caused by the normal autumn
breakdown of chlorophyll (which usually is the dominant pigment
during the summer). After the breakdown of chlorophyll, other
pigments such as carotenes and xanthophylls become dominant and
therefore the foliage color changes from green to yellow.
Carotene and xanthophyll pigments absorb blue light and reflect
green and red light.
Spectral Signatures
• Reflectance is
wavelength
dependent
• Signatures
represent
average
reflectance
values
• Signatures are
spatially and
temporally
variable
The vertical axis shows the
percentage of incident sunlight
that is reflected by the
materials. The horizontal axis
shows wavelengths of energy
for the visible spectral region
0.4 to 7.0 µm. and the
reflected portion 0.7 to 3.0
µm. of the infrared IR. region.
Reflected IR energy consists
largely of solar energy
reflected from the earth at
wavelengths longer than the
sensitivity range of the eye.
The thermal portion of the IR
region 3.0to 1000 µm. consists
of radiant, or heat, energy….
Spectral bands recorded by remote sensing systems.
Spectral reflectance curves are for vegetation and sedimentary rocks.
Fig. 5A
shows reflectance spectra
of alunite and the three
common hydrothermal
clay minerals illite,
kaolinite, and
montmorillonite.
These minerals have
distinctive absorption
features (reflectance
minima) at wavelengths
within the bandpass of
TM band 7 which is
shown with a stippled
pattern in Fig. 5A.
Recognition of hydrothermal clays and alunite
from TM data, Goldfield mining district.
Recognition of hydrothermal iron minerals from TM data, Goldfield
mining district.
Laboratory spectra of alteration
minerals in the 2.0 to 2.5
µm band. Spectra are offset
vertically.
Note positions and bandwidths
of the spectral bands recorded
by AVIRIS and TM band 7.

Weitere ähnliche Inhalte

Was ist angesagt?

Multispectral remote sensing
Multispectral remote sensingMultispectral remote sensing
Multispectral remote sensing
Dharmendera Meena
 
Energy interaction with earth surface features
Energy interaction with earth surface featuresEnergy interaction with earth surface features
Energy interaction with earth surface features
suchismita11
 
Emr intraction with atmosphere
Emr intraction with atmosphereEmr intraction with atmosphere
Emr intraction with atmosphere
Rahat Hasan
 
Interaction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surfaceInteraction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surface
Sumant Diwakar
 
. Atmospheric window and reflectance curve
. Atmospheric window and  reflectance curve. Atmospheric window and  reflectance curve
. Atmospheric window and reflectance curve
marutiChilame
 

Was ist angesagt? (20)

Spectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetationSpectral reflectance curve of dead stressed vegetation
Spectral reflectance curve of dead stressed vegetation
 
Optical remote sensing
Optical remote sensingOptical remote sensing
Optical remote sensing
 
Multispectral remote sensing
Multispectral remote sensingMultispectral remote sensing
Multispectral remote sensing
 
Remote Sensing Platforms and Sensors
Remote Sensing Platforms and SensorsRemote Sensing Platforms and Sensors
Remote Sensing Platforms and Sensors
 
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
 ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES  ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
ENERGY INTERACTIONS WITH EARTH SURFACE FEATURES
 
Thermal Remote Sensing
Thermal Remote SensingThermal Remote Sensing
Thermal Remote Sensing
 
Energy interaction with earth surface features
Energy interaction with earth surface featuresEnergy interaction with earth surface features
Energy interaction with earth surface features
 
Movement of EMR in the Atmosphere and Atmospheric window
Movement of EMR in the Atmosphere and Atmospheric windowMovement of EMR in the Atmosphere and Atmospheric window
Movement of EMR in the Atmosphere and Atmospheric window
 
Fundamentals of Remote Sensing
Fundamentals of Remote SensingFundamentals of Remote Sensing
Fundamentals of Remote Sensing
 
Emr intraction with atmosphere
Emr intraction with atmosphereEmr intraction with atmosphere
Emr intraction with atmosphere
 
Image interpretation keys & image resolution
Image interpretation keys & image resolutionImage interpretation keys & image resolution
Image interpretation keys & image resolution
 
Basics of Remote Sensing
Basics of Remote SensingBasics of Remote Sensing
Basics of Remote Sensing
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
Interaction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surfaceInteraction of EMR with atmosphere and earth surface
Interaction of EMR with atmosphere and earth surface
 
Microwave remote sensing
Microwave remote sensingMicrowave remote sensing
Microwave remote sensing
 
Spectral signatures
Spectral signaturesSpectral signatures
Spectral signatures
 
. Atmospheric window and reflectance curve
. Atmospheric window and  reflectance curve. Atmospheric window and  reflectance curve
. Atmospheric window and reflectance curve
 
Role of electromagnetic Radiation in Remote Sensing
Role of electromagnetic Radiation in  Remote SensingRole of electromagnetic Radiation in  Remote Sensing
Role of electromagnetic Radiation in Remote Sensing
 
Remote Sensing Platforms and Its types
Remote Sensing Platforms and Its typesRemote Sensing Platforms and Its types
Remote Sensing Platforms and Its types
 
Spectral Signature
Spectral SignatureSpectral Signature
Spectral Signature
 

Andere mochten auch

Research Proposal
Research Proposal Research Proposal
Research Proposal
Nor Ain
 
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
Rudolf Husar
 
Polly use of remote sensing products for local water
Polly use of remote sensing products for local waterPolly use of remote sensing products for local water
Polly use of remote sensing products for local water
GeCo in the Rockies
 

Andere mochten auch (20)

Light Scattering: Fundamentals (Old Version)
Light Scattering: Fundamentals (Old Version)Light Scattering: Fundamentals (Old Version)
Light Scattering: Fundamentals (Old Version)
 
Advanced Laser Diffraction Theory
Advanced Laser Diffraction TheoryAdvanced Laser Diffraction Theory
Advanced Laser Diffraction Theory
 
Remote Sensing
Remote SensingRemote Sensing
Remote Sensing
 
Research Proposal
Research Proposal Research Proposal
Research Proposal
 
Prediction methods of Rayleigh scattering losses
Prediction methods of Rayleigh scattering lossesPrediction methods of Rayleigh scattering losses
Prediction methods of Rayleigh scattering losses
 
Introduction to laser diffraction
Introduction to laser diffractionIntroduction to laser diffraction
Introduction to laser diffraction
 
Optical fiber cable final
Optical fiber cable finalOptical fiber cable final
Optical fiber cable final
 
Optical Fiber
Optical FiberOptical Fiber
Optical Fiber
 
Thermal remote sensing
Thermal remote sensing   Thermal remote sensing
Thermal remote sensing
 
Remote sensing
Remote sensingRemote sensing
Remote sensing
 
Haze removal for a single remote sensing image based on deformed haze imaging...
Haze removal for a single remote sensing image based on deformed haze imaging...Haze removal for a single remote sensing image based on deformed haze imaging...
Haze removal for a single remote sensing image based on deformed haze imaging...
 
How to Select the Best Refractive Index for Particle Size Analysis
How to Select the Best Refractive Index for Particle Size AnalysisHow to Select the Best Refractive Index for Particle Size Analysis
How to Select the Best Refractive Index for Particle Size Analysis
 
Atmoshpheric effect on remote sensing data
Atmoshpheric effect on remote sensing dataAtmoshpheric effect on remote sensing data
Atmoshpheric effect on remote sensing data
 
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
2004-10-14 AIR-257: Satellite Detection of Aerosols Concepts and Theory
 
Physics presentation(step index and graded index)
Physics presentation(step index and graded index)Physics presentation(step index and graded index)
Physics presentation(step index and graded index)
 
.remote sensing.Ece 402 unit-2
.remote sensing.Ece 402 unit-2.remote sensing.Ece 402 unit-2
.remote sensing.Ece 402 unit-2
 
Polly use of remote sensing products for local water
Polly use of remote sensing products for local waterPolly use of remote sensing products for local water
Polly use of remote sensing products for local water
 
Archaeological applications of multi/hyper-spectral data: challenges and pote...
Archaeological applications of multi/hyper-spectral data: challenges and pote...Archaeological applications of multi/hyper-spectral data: challenges and pote...
Archaeological applications of multi/hyper-spectral data: challenges and pote...
 
TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecos...
TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecos...TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecos...
TERN eMAST : Observations and terrestrial ecosystem models : Terrestrial Ecos...
 
SCATTERING
SCATTERINGSCATTERING
SCATTERING
 

Ähnlich wie Raleigh and Mie scattering in remote sensing,

Interaction between electromagnetic radiation and matter
Interaction between electromagnetic radiation and matterInteraction between electromagnetic radiation and matter
Interaction between electromagnetic radiation and matter
Abdullah Khan
 
Solar irradiation & spectral signature
Solar irradiation & spectral signatureSolar irradiation & spectral signature
Solar irradiation & spectral signature
Sumant Diwakar
 

Ähnlich wie Raleigh and Mie scattering in remote sensing, (20)

Interaction between electromagnetic radiation and matter
Interaction between electromagnetic radiation and matterInteraction between electromagnetic radiation and matter
Interaction between electromagnetic radiation and matter
 
remote sensing-converted.pptx
remote sensing-converted.pptxremote sensing-converted.pptx
remote sensing-converted.pptx
 
Emr and atmosphere
Emr and atmosphereEmr and atmosphere
Emr and atmosphere
 
2 Intro RS.pdf
2 Intro RS.pdf2 Intro RS.pdf
2 Intro RS.pdf
 
Surveying ii ajith sir class2
Surveying ii ajith sir class2Surveying ii ajith sir class2
Surveying ii ajith sir class2
 
L3 emr
L3 emrL3 emr
L3 emr
 
Unit iv remote sensing
Unit iv remote sensingUnit iv remote sensing
Unit iv remote sensing
 
Introduction to Remote Sensing- by Wankie Richman
Introduction to Remote Sensing- by Wankie RichmanIntroduction to Remote Sensing- by Wankie Richman
Introduction to Remote Sensing- by Wankie Richman
 
Remote Sensing fundamental.pptx
Remote Sensing fundamental.pptxRemote Sensing fundamental.pptx
Remote Sensing fundamental.pptx
 
Solar irradiation & spectral signature
Solar irradiation & spectral signatureSolar irradiation & spectral signature
Solar irradiation & spectral signature
 
Remote sensing and aerial photography
Remote sensing and aerial photographyRemote sensing and aerial photography
Remote sensing and aerial photography
 
Rs and gis lect 3-6.pdf
Rs and gis lect 3-6.pdf Rs and gis lect 3-6.pdf
Rs and gis lect 3-6.pdf
 
detail information of advance total station and remote sensing
detail information of advance total station and remote sensingdetail information of advance total station and remote sensing
detail information of advance total station and remote sensing
 
Basic of Remote Sensing
Basic of Remote SensingBasic of Remote Sensing
Basic of Remote Sensing
 
Introduction to remote sensing pt 1
Introduction to remote sensing pt 1Introduction to remote sensing pt 1
Introduction to remote sensing pt 1
 
Concept of Remote sensing
Concept of Remote sensingConcept of Remote sensing
Concept of Remote sensing
 
Remote Sensing
Remote SensingRemote Sensing
Remote Sensing
 
chapter2: Remote sensing
chapter2: Remote sensingchapter2: Remote sensing
chapter2: Remote sensing
 
lec1.pptx
lec1.pptxlec1.pptx
lec1.pptx
 
Electromagnetic spectrum and its interaction with atmosphere &amp; matter
Electromagnetic spectrum and its  interaction with atmosphere &amp; matterElectromagnetic spectrum and its  interaction with atmosphere &amp; matter
Electromagnetic spectrum and its interaction with atmosphere &amp; matter
 

Mehr von P.K. Mani

Mehr von P.K. Mani (20)

Crust core and mantle
Crust core and mantleCrust core and mantle
Crust core and mantle
 
Origin of universe
Origin of universe Origin of universe
Origin of universe
 
Fundamentals of soil science
Fundamentals of soil scienceFundamentals of soil science
Fundamentals of soil science
 
Physical chemistry of soil for PG students
Physical chemistry of soil for PG studentsPhysical chemistry of soil for PG students
Physical chemistry of soil for PG students
 
EFFECT OF COATED NITROGENOUS FERTILIZERS ON CARBON FRACTIONS IN RICE BASED CR...
EFFECT OF COATED NITROGENOUS FERTILIZERS ON CARBON FRACTIONS IN RICE BASED CR...EFFECT OF COATED NITROGENOUS FERTILIZERS ON CARBON FRACTIONS IN RICE BASED CR...
EFFECT OF COATED NITROGENOUS FERTILIZERS ON CARBON FRACTIONS IN RICE BASED CR...
 
Nano Technology for UG students of Agriculture
Nano Technology for UG students of AgricultureNano Technology for UG students of Agriculture
Nano Technology for UG students of Agriculture
 
Sewage and sludge as waste material
 Sewage and sludge as waste material Sewage and sludge as waste material
Sewage and sludge as waste material
 
Agril. Waste management
Agril. Waste managementAgril. Waste management
Agril. Waste management
 
Geomorphology at a glance: Major landforms
Geomorphology at a glance: Major landformsGeomorphology at a glance: Major landforms
Geomorphology at a glance: Major landforms
 
Introduction to Geomorphology
Introduction to Geomorphology Introduction to Geomorphology
Introduction to Geomorphology
 
Geomorphology and Geochemistry
Geomorphology  and GeochemistryGeomorphology  and Geochemistry
Geomorphology and Geochemistry
 
COMPARATIVE ADVANTAGE OF SRI OVER TRANSPLANTED RICE IN TERMS OF YIELD A...
COMPARATIVE  ADVANTAGE  OF SRI  OVER TRANSPLANTED  RICE  IN TERMS OF YIELD  A...COMPARATIVE  ADVANTAGE  OF SRI  OVER TRANSPLANTED  RICE  IN TERMS OF YIELD  A...
COMPARATIVE ADVANTAGE OF SRI OVER TRANSPLANTED RICE IN TERMS OF YIELD A...
 
ASSESSMENT OF DIFFERENT N MANAGEMENT STRATEGIES IN LOWLAND RICE CULTIVATION
ASSESSMENT OF DIFFERENT N MANAGEMENT STRATEGIES IN LOWLAND RICE CULTIVATIONASSESSMENT OF DIFFERENT N MANAGEMENT STRATEGIES IN LOWLAND RICE CULTIVATION
ASSESSMENT OF DIFFERENT N MANAGEMENT STRATEGIES IN LOWLAND RICE CULTIVATION
 
BEHAVIOUR OF HEAVY METALS IN SEWAGE-SLUDGE AMENDED SOIL
BEHAVIOUR OF HEAVY METALS IN SEWAGE-SLUDGE AMENDED SOILBEHAVIOUR OF HEAVY METALS IN SEWAGE-SLUDGE AMENDED SOIL
BEHAVIOUR OF HEAVY METALS IN SEWAGE-SLUDGE AMENDED SOIL
 
Quiz contest for UG level
Quiz  contest for UG levelQuiz  contest for UG level
Quiz contest for UG level
 
Quiz contest for UG level
Quiz  contest for UG levelQuiz  contest for UG level
Quiz contest for UG level
 
Bioavailabilty and crop uptake of heavy metals from Sewage sludge
Bioavailabilty and crop uptake of heavy metals from Sewage sludge Bioavailabilty and crop uptake of heavy metals from Sewage sludge
Bioavailabilty and crop uptake of heavy metals from Sewage sludge
 
Maintenance of Soil Health
Maintenance of Soil HealthMaintenance of Soil Health
Maintenance of Soil Health
 
Effect of phosphorus build up on the availabiilty of Zinc in soil in a rice b...
Effect of phosphorus build up on the availabiilty of Zinc in soil in a rice b...Effect of phosphorus build up on the availabiilty of Zinc in soil in a rice b...
Effect of phosphorus build up on the availabiilty of Zinc in soil in a rice b...
 
Effect of minimum tillage and Mulching on nutrient Transformation in rice bas...
Effect of minimum tillage and Mulching on nutrient Transformation in rice bas...Effect of minimum tillage and Mulching on nutrient Transformation in rice bas...
Effect of minimum tillage and Mulching on nutrient Transformation in rice bas...
 

Kürzlich hochgeladen

An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdf
SanaAli374401
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
PECB
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
negromaestrong
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch LetterGardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
MateoGardella
 

Kürzlich hochgeladen (20)

Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdf
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch LetterGardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
 
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
psychiatric nursing HISTORY COLLECTION .docx
psychiatric  nursing HISTORY  COLLECTION  .docxpsychiatric  nursing HISTORY  COLLECTION  .docx
psychiatric nursing HISTORY COLLECTION .docx
 

Raleigh and Mie scattering in remote sensing,

  • 1. Rayleigh Scattering and Mie scattering: Dr. P. K. Mani Bidhan Chandra Krishi Viswavidyalaya E-mail: pabitramani@gmail.com Website: www.bckv.edu.in
  • 2. Remote Sensing and its Applications in Soil Resource Mapping (ACSS-754) Absorption. Scattering
  • 3. The atmosphere affects electromagnetic energy through absorption, scattering and reflection. How these processes affect radiation seen by the satellite depends on the path length, the presence of particulates and absorbing gases, and wavelengths involved. transmitted absorbed, emitted and scattered by aerosols and molecules transmitted absorbed &scattered emitted reflected transmitted reflected absorbed emitted Land emitted reflected transmitted absorbed Ocean Figure-... Process of Atmospheric Radiation
  • 4. Rayleigh Scattering: why the sky is blue
  • 5. EM radiation from the sun interacts with the atmospheric constituents and gets absorbed or scattered. Essentially two types of scattering takes place: Elastic scattering in which the energy of radiation is not changed due to the scattering, and inelastic scattering in which the energy of the scattered radiation is changed. 3 types of elastic scattering is recognized in atmospheric scattering Rayleigh scattering Mie scattering Nonselective scattering
  • 6. Radiation scattered from a particle depends on: Size; Shape; Index of refraction; Wavelength of radiation; View geometry. For Rayleight scattering, λ >> φ •Scattering is diffuse (in all directions) and λ dependent or selective • Scattering = 1/ λ4 For Mie scattering, λ ≈φ Where φ is particle size.  Scattering properties of such aerosols as smoke, dust, haze in the visible part of the spectrum and of cloud droplets in the IR region can be explanined by Mie scattering, While of air molecules in the visible part can be explained by Rayleigh Scattering
  • 7. Rayleigh Scattering: In Rayleigh scattering the volume scattering coefficient σλ is given by : σλ [4π = 2 NV λ 4 2 ] ⋅ [µ 2 [µ 2 ] ] − µ0 2 2 + µ0 2 2 = const λ 4 N= no. of particles/cm2 …. V= vol. of scattering particles λ = wavelength of radiation … µ= refractive index of the particles µ0= refractive index of the medium Because of Rayleigh scattering Multispectral remote sensing data from the blue portion of the spectrum is of relatively limited usefulness. In case of aerial photography, special filters are used to filter out the scattered blue radiation due to haze present in the atmosphere.
  • 8. Mie scattering a2 σ λ =10 π ∫ N (a ) K (a, µ)a da 5 2 a1 σλ = Mie scattering coefficient at wavelength λ N(a) = no. of particles in interval of radius a and a + da K(a, µ) = scattering coefficient(cross section ) as a function of spherical particles of radius a and the refractive index of the particles µ Mie scattering usually manifests itself as a general deterioration of multispectral images across the optical spectrum under conditions of heavy atmospheric haze
  • 9. Nonselective Scattering  Particles are much larger than the wavelength λ >> l All wavelength are scattered equally Effects of scattering  It causes haze in remotely sensed images  It decreases the spatial detail on the images  It also decreases the contrast of the images  Water droplets with diameters ranging from 5-100 µm scatter all wavelengths of visible light with equal efficiency. As a consequence, clouds and fog appear whitish because a mixture of all colours in approximately equal quantities produces white light. Non selective scattering usually results when the atmosphere is heavily dust and moisture ladden and results in a severe attenuation of the received data. However, the occurrence of this scattering mechanism is frequently a clue to the existence of large particulate matter in the atmosphere above the scene of interest, and sometimes this in itself becomes useful data.
  • 10. Atmospheric scattering process Scattering process Rayleigh Mie Nonselective Wavelength Particle size dependence μm λ-4 <<0.1 λ0 to λ-4 0.1-10 λ0 >10 Kind of particles Air molecules Smoke , fume, Haze Dust , Fog, Cloud Nonselective scattering occurs when the particles are much larger than the wavelength of the radiation. Water droplets and large dust particles can cause this type of scattering. Nonselective scattering gets its name from the fact that all wavelengths are scattered about equally. This type of scattering causes fog and clouds to appear white to our eyes because blue, green, and red light are all scattered in approximately equal
  • 11. Atmospheric Windows  Atmospheric windows define wavelength ranges in which the atmosphere is particularly transmissive of energy.  Visible region of the electromagnetic spectrum resides within an atmospheric window with wavelengths of about 0.3 to 0.9 µm  Emitted energy from the earth's surface is sensed through windows at 3 to 5 µm and 8 to 14 µm.  Radar and passive microwave systems operate through a window region of 1 mm to 1 m.
  • 12. Selective transmission of EMR by Earth’s atmosphere Transmission through the atmosphere is very selective. Very high for wavelengths 0.3-1 µm and >1cm, moderately good for 1-20 µm and 0.1-1 cm, and very poor for <0.3 µm and 20-100 µm. This defines the “ATMOSPHERIC WINDOWS”.
  • 13.
  • 14. Those wavelength ranges in which radiation can pass through the atmosphere with relatively little attenuation. atmospheric windows.
  • 15. C. Interaction with Target What the remote sensor is really measuring is how the energy interacts with the target.
  • 16. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface. These are: Absorption (A); Transmission (T); Reflection (R). Specular reflection Diffuse reflection.
  • 17. Leaves: chlorophyll strongly absorbs radiation in the R and B but reflects (G)green wavelengths. Internal structure of healthy leaves act as excellent diffuse reflectors of near-infrared (NIR) wavelengths. In fact, measuring and monitoring the NIR reflectance is one way that can determine healthiness of vegetation Water: Longer λ visible and near infrared radiation is absorbed more by water than shorter visible wavelengths. Thus water typically looks blue or blue-green due to stronger reflectance at these shorter wavelengths,
  • 18.
  • 19. Spectral Reflectance Signature When solar radiation hits a target surface, it may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Spectral reflectance: the reflectance of electromagnetic energy at specified wavelength intervals
  • 20. Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic radiation (EM) at varying wavelengths which can uniquely identify an object. The spectral signature of an object is a function of the incidental EM wavelength and material interaction with that section of the electromagnetic spectrum. Spectral Signature: Quantitative measurement of the properties of an object at one or several wavelength intervals
  • 21. For example, at some wavelengths, sand reflects more energy than green vegetation but at other wavelengths it absorbs more (reflects less) than does the vegetation. In principle, we can recognize various kinds of surface materials and distinguish them from each other by these differences in reflectance. Of course, there must be some suitable method for measuring these differences as a function of wavelength and intensity (as a fraction of the amount of irradiating radiation). Using reflectance differences, we can distinguish the four common surface materials (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty water), shown in the next figure. Please note the positions of points for each
  • 22. When we use more than two wavelengths, the plots in multidimensional space tend to show more separation among the materials. This improved ability to distinguish materials due to extra wavelengths is the basis for multispectral remote sensing I-11: Referring to the above spectral plots, which region of the spectrum (stated in wavelength interval) shows the greatest reflectance for a) grasslands; b) pinewoods; c) red sand; d) silty water. At 0.6
  • 23. By measuring the energy that is reflected (or emitted) by targets on the Earth's surface over a variety of different wavelengths, we can build up a spectral response for that object.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32. Vegetation has a unique spectral signature that enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. The reflectance is low in both the blue and red regions of the spectrum, due to absorption by chlorophyll for photosynthesis. It has a peak at the green region. In the near infrared (NIR) region, the reflectance is much higher than that in the visible band due to the cellular structure in the leaves.  Hence, vegetation can be identified by the high NIR but generally low visible reflectance.
  • 33. The reflectance of clear water is generally low. However, the reflectance is maximum at the blue end of the spectrum and decreases as wavelength increases. Hence, water appears dark bluish to the visible eye. Turbid water has some sediment suspension that increases the reflectance in the red end of the spectrum and would be brownish in appearance.  The reflectance of bare soil generally depends on its composition. In the example shown, the reflectance increases monotonically with increasing wavelength. Hence, it should appear yellowish-red to the eye.
  • 34.
  • 35. The shape of the reflectance spectrum can be used for identification of vegetation type. For example, the reflectance spectra of dry grass and green grass in the previous figures can be distinguished although they exhibit the generally characteristics of high NIR but low visible reflectance. • Dry grass has higher reflectance in the visible region but lower reflectance in the NIR region. For the same vegetation type, the reflectance spectrum also depends on other factors such as the • leaf moisture content • health of the plants. These properties enable vegetation condition to be monitored using remotely sensed images.
  • 36.
  • 37. Vegetation generally has low reflectance and low transmittance in the visible part of the spectrum. This is mainly due to plant pigments absorbing visible light. Chlorophyll pigments absorb violet-blue and red light for photosynthetic energy. Green light is not absorbed for photosynthesis and therefore most plants appear green. In the autumn, some plant leaves turn from green to a brilliant yellow. This change in foliage color is caused by the normal autumn breakdown of chlorophyll (which usually is the dominant pigment during the summer). After the breakdown of chlorophyll, other pigments such as carotenes and xanthophylls become dominant and therefore the foliage color changes from green to yellow. Carotene and xanthophyll pigments absorb blue light and reflect green and red light.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46. Spectral Signatures • Reflectance is wavelength dependent • Signatures represent average reflectance values • Signatures are spatially and temporally variable
  • 47. The vertical axis shows the percentage of incident sunlight that is reflected by the materials. The horizontal axis shows wavelengths of energy for the visible spectral region 0.4 to 7.0 µm. and the reflected portion 0.7 to 3.0 µm. of the infrared IR. region. Reflected IR energy consists largely of solar energy reflected from the earth at wavelengths longer than the sensitivity range of the eye. The thermal portion of the IR region 3.0to 1000 µm. consists of radiant, or heat, energy…. Spectral bands recorded by remote sensing systems. Spectral reflectance curves are for vegetation and sedimentary rocks.
  • 48. Fig. 5A shows reflectance spectra of alunite and the three common hydrothermal clay minerals illite, kaolinite, and montmorillonite. These minerals have distinctive absorption features (reflectance minima) at wavelengths within the bandpass of TM band 7 which is shown with a stippled pattern in Fig. 5A. Recognition of hydrothermal clays and alunite from TM data, Goldfield mining district.
  • 49. Recognition of hydrothermal iron minerals from TM data, Goldfield mining district.
  • 50. Laboratory spectra of alteration minerals in the 2.0 to 2.5 µm band. Spectra are offset vertically. Note positions and bandwidths of the spectral bands recorded by AVIRIS and TM band 7.

Hinweis der Redaktion

  1. What the remote sensor is really measuring is how the energy interacts with the target.