Remote Sensing Review

Lecture 1

What is remote sensing

Remote Sensing: remote sensing is science of acquiring, processing, and interpreting

images and related data that are obtained from ground-based, air-or space-borne instruments that record the interaction between matter (target) and electromagnetic radiation.

Remote Sensing: using electromagnetic spectrum to image the land, ocean, and atmosphere.

Electromagnetic Spectrum

Source: http://oea.larc.nasa.gov/PAIS/DIAL.html

Ways of Energy TransferWays of Energy Transfer

Energy is the ability to do work. In the process of doing work, energy is often transferred from one body to another or from one place to another. The three basic ways in which energy can be transferred include conduction, convection, and radiation. • Most people are familiar with conduction which occurs when one body (molecule or atom) transfers its kinetic energy to another by colliding with it (physical contact). This is how a pan gets heated on a stove.

• In convection, the kinetic energy of bodies is transferred from one place to another by physically moving the bodies. A good example is the convectional heating of air in the atmosphere in the early afternoon (less dense air rises).

• The transfer of energy by electromagnetic radiation is of primary interest to remote sensing because it is the only form of energy transfer that can take place in a vacuum such as the region between the Sun and the Earth.

Energy is the ability to do work. In the process of doing work, energy is often transferred from one body to another or from one place to another. The three basic ways in which energy can be transferred include conduction, convection, and radiation. • Most people are familiar with conduction which occurs when one body (molecule or atom) transfers its kinetic energy to another by colliding with it (physical contact). This is how a pan gets heated on a stove.

• In convection, the kinetic energy of bodies is transferred from one place to another by physically moving the bodies. A good example is the convectional heating of air in the atmosphere in the early afternoon (less dense air rises).

• The transfer of energy by electromagnetic radiation is of primary interest to remote sensing because it is the only form of energy transfer that can take place in a vacuum such as the region between the Sun and the Earth.

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Wave model of EMR

Electromagnetic wave consists of an electrical field (E) which varies in magnitude in a direction perpendicular to the direction in which the radiation is traveling, and a magnetic field (M) oriented at right angles to the electrical field. Both these fields travel at the speed of light (c).

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Three characteristics of electromagnetic wave

Velocity is the speed of light, c=3 x 108 m/s wavelength (ג) is the length of one wave cycle, is

measured in metres (m) or some factor of metres such as centimetres (cm) 10-2 m micrometres (µm) 10-6 mnanometres (nm) 10-9 m

Frequency (v) refers to the number of cycles of a wave passing a fixed point per unit of time. Frequency is normally measured in hertz (Hz), equivalent to one cycle per second, and various multiples of hertz. unlike c and ג changing as propagated through media of different densities, v remains constant.Hertz (Hz) 1kilohertz (KHz) 103

megahertz (MHz) 106

gigahertz (GHz) 109

The amplitude of an electromagnetic wave is the height of the wave crest above the undisturbed position Travel time from the Sun to Earth is 8 minutes

Particle model of EMR

Sir Isaac Newton (1704) was the first person stated that the light had not only wavelike characteristics but also light was a stream of particles, traveling in straight lines.

Niels Bohr and Max Planck (20’s) proposed the quantum theory of EMR:

Energy content: Q (Joules) = hv (h is the Planck constant 6.626 x 10 –34 J s)

= c/v=hc/Q or Q=hc/ The longer the wavelength, the lower its energy content, which is important in remote

sensing because it suggests it is more difficult to detect longer wavelength energy

Newton’s experiment in 1966

Energy of quanta (photons)

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EMR details

m)•Red: 0.620 - 0.7 •Orange: 0.592 - 0.620•Yellow: 0.578 - 0.592

•Green: 0.500 - 0.578

•Blue: 0.446 - 0.500

•Violet: 0.4 - 0.446Bees and some other insects can see near UV.The Sun is the source of UV, but only > 0.3 m (near UV) can reach the Earth.

EMR details (2)

Source of EMR All objects above absolute zero emit electromagnetic energy, including water, soil, rock,

vegetation, and the surface of the Sun. The Sun represents the initial source of most of the electromagnetic energy remote sensing systems (except radar and sonar)

Total radiation emitted M (Wm–2) = σT4 (Stefan-Boltzmann Law), where T is in degrees K and σ is the “Stefan-Boltzmann” constant, 5.67×10–8 K–4Wm–2

-- Energy at Sun enormous, 7.3×107 Wm–2, reduced to 459 Wm–2 by Earth-Sun distance Wavelength λmax of peak radiation, in μm = 2897/T (Wien’s Displacement Law) Examples:

-- Peak of Sun’s radiation λmax = 2897/6000 = 0.48 μm

-- Peak of Earth’s radiation λmax = 2897/300 = 9.7 μm

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Paths and Interactions

If the energy being remotely sensed comes from the Sun, the energy:

• is radiated by atomic particles at the source (the Sun),

• propagates through the vacuum of space at the speed of light,

• interacts with the Earth's atmosphere (3A),

• interacts with the Earth's surface (3B),

• interacts with the Earth's atmosphere once again (3C),

• finally reaches the remote sensor where it interacts with various optical systems, filters, emulsions, or detectors (3D).

Solar irradiance

Reflectance from study area,

Various Paths of Satellite Received Radiance

Diffuse sky irradiance

Total radiance at the sensor

L L

L

Reflectance from neighboring area,

1

2

3

Remote sensor

detector

Atmosphere

5

4 1,3,5

E

L

90Þ

0T

v T

0

0

v

p T

S

I

nr r

Ed

Solar irradiance

Reflectance from study area,

Various Paths of Satellite Received Radiance

Diffuse sky irradiance

Total radiance at the sensor

L L

L

Reflectance from neighboring area,

1

2

3

Remote sensor

detector

Atmosphere

5

4 1,3,5

E

L

90Þ

0T

v T

0

0

v

p T

S

I

nr r

Ed

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60 milesor100km

Several concepts Planck’s equation

- if a blackbody transforms heat into radiant energy, then the radiation received at a sensor is given by Planck’s equation.

Spectral Emissivity

)1(

2),(

)/5

2

kThce

hcTB

)(/_

)(/_12

12

mWmAbsorptionEmissionBlackbody

mWmAbsorptionEmissionActuale

Spectral reflectivity is the percentage of EMR reflected by the object in a each wavelength or spectral bands

Albedo is ratio of the amount of EMR reflected by a surface to the amount of incident radiation on the surface. Fresh Snow has high albedo of 0.8-0.95, old snow 0.5-0.6, forest 0.1-0.2, Earth system 0.35

EMR EMR

EMR

0

0.1

0.2

0.3

0.4

0.5

0.6

250 500 750 1000 1250 1500 1750 2000 2250 2500

Wavelength (nm)

Ref

lect

ance

average shrub

average grass

average soil

Some others

Pixel FOV and IFOV Solid angle Radiance Cross track and along track Whiskbroom and Push broom Dwell time Nadir and off-nadir

Remote sensing platforms Remote sensing platforms

Ground and Aircraft Based

Ground repeat or continuous sampling regional or local coverage example: NEXRAD for precipitation

Aircraft repeat sampling , any sampling interval regional or local coverage examples: AVIRIS for minerals exploration

LIDAR for ozone and aerosols

Space Based

Sun-synchronous polar orbits global coverage, fixed crossing, repeat sampling typical altitude 500-1,500 km example: MODIS, Landsat

Low-inclination, non-Sun-synchronous orbits tropics and mid-latitudes coverage, varying sampling typical altitude 200-2,000 km example: TRMM

Geostationary orbits regional coverage, continuous sampling over equator only, altitude 35,000 km example: GOES

Types of remote sensing

Passive: source of energy is either the Sun, Earth, or atmosphere Sun

- wavelengths: 0.4-5 µm

Earth or its atmosphere

- wavelengths: 3 µm -30 cm

Active: source of energy is part of the remote sensor system Radar

- wavelengths: mm-m Lidar

- wavelengths: UV, Visible, and near infrared

Measurement scales constrained by physics and technology

Spatial resolution (IFOV/GSD) and coverage (FOV) Optical diffraction sets minimum aperture size

Spectral resolution ( ) and coverage (min to max) Narrow bands need bigger aperture, more detectors, longer

integration time

Radiometric resolution (S/N, NE, NET ) and coverage (dynamic range) Aperture size, detector size, number of detectors, and integration time

Temporal resolution (site revisit) and coverage (global repeat) Pointing agility, period for full coverage

Basics of Bit

Computer store everything in 0 or 1

bits Max. num

1 2

2 4

3 8

6 64

8 256

11 2048

12 4096

(2bits)

7 6 5 4 3 2 1 0

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

Bit no.

0

256

8 bits as an example

The size of a cell we call image resolution, depending on…Such as 1 m, 30 m, 1 km, or 4 km

Digital Image Data Formats

Each band of image is stored as a matrix (array) format;

To efficiently handle the multi-bands (and hyperspectral) imagery in an image processing software, BSQ (band sequential), BIL (band interleaved by line), BIP (band interleaved by pixel) are common image data format (see an example in p103 of the text book) .

Procedures of image processing

Preprocessing Radiometric correction is concerned with improving the accuracy of surface

spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. Atmospheric and topographic corrections

Geometric correction is concerned with placing the above measurements or derivative products in their proper locations.

Information enhancement Point operations change the value of each individual pixel independent of all

other pixels Local operations change the value of individual pixels in the context of the

values of neighboring pixels. They are image reduction, image magnification, transect extraction, contrast

adjustments (linear and non-linear), band ratioing, spatial filtering, fourier transformations, principle components analysis, and texture transformations

Information extraction Post-classification Information output

Image or enhanced image itself, thematic map, vector map, spatial database, summary statistics and graphs

Remote Sensing Applications

Land: rocks, minerals, faults, land use and land cover, vegetation, DEM, snow and ice, urban growth, environmental studies, …

Ocean: ocean color, sea surface temperature, ocean winds, …

Atmosphere: temperature, precipitation, clouds, ozone, aerosols, …

Applications driving remote sensing 100

50

30

20

10

80

8

5

3

2

10.8

0.5

0.3

0.2

0.4

0.60.7

Nom

inal

Sp

atia

l Res

olu

tion

(g

rou

nd

res

olve

d d

ista

nce

, met

ers)

Land Cover Class Level

I II III IV

4

6 7

40

60 70

0.1

I

II

III

IV

100

50

30

20

10

80

8

5

3

2

10.8

0.5

0.3

0.2

0.4

0.60.7

Nom

inal

Sp

atia

l Res

olu

tion

(g

rou

nd

res

olve

d d

ista

nce

, met

ers)

Land Cover Class Level

I II III IV

4

6 7

40

60 70

0.1

I

II

III

IV

2 y3 y4 y

Quickbird (2000) 0.82 x 0.82 3.28 x 3.28

0.2 1.0 2 3 5 10 2 3 5 102 103 104 2 3 5 2 3 5

10

102

103

104

2 3

5

8

2 3

5

2

3

5

2

3

5

2

3

5

9 d

1 d

1 hr

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

12 hr

8105

8

8

8

.80.5

3 d

2 d

4 d

532

2

3

58

106

1 y

23

5

8107

5 y

10 y

15 20

4 8

26 d

44 d 55 d

METEOSAT VISIR 2.5 x 2.5 km

TIR 5 x 5 km

AVHRR LAC 1.1 x 1.1 km

GAC 4 x 4 km

JERS-1 MSS 18 x 24

L-band 18 x 18

Aerial Photography < 0.25 x 0.25 m (0.82 x 0.82 ft.)

1 x 1 m (3.28 x 3.28 ft.)

RADARSAT C-band 11-9, 9 25 x 28

48-30 x 28 32-25 x 28

50 x 50 22-19 x 28 63-28 x 28 100 x 100

EOSAT/Space Imaging IKONOS (1999)

Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE

OrbView 3 (1999) Pan 1 x 1 MSS 4 x 4

OrbView 4 (2000) Pan 1 x 1 MSS 4 x 4

Hyperspectral 8 x 8 m

22 d 16 d

IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1CD

Pan 5.8 x 5.8 LISS-3 23.5 x 23.5; MIR 70 x 70

WiFS 188 x 188

10,000 min

100 min

5 d

30 d

180 d

T3

C1

15 y

DE1, E2

T4

0.3

LANDSAT 4,5 MSS 79 x 79 TM 30 x 30

LANDSAT 7 ETM+ (1999) Pan 15 x 15; MSS 30 x 30

TIR 60 x 60

E1

L4

M5

T1, U1

LI

SPOT HRV 1,2,3,4 Pan 10 x10

MSS 20 x 20 SPOT 5 HRG (2001; not shown)

Pan 2.5 x 2.5; 5 x 5 MSS 10 x 10; SWIR 20 x 20

1 m0.3 5 100 m10 20

Nominal Spatial Resolution in meters

30

SPIN-2 KVR-1000 2 x 2 TK-350 10 x 10

C2

DE2

DE3 DE4 DE5

L3

0.5

S2

L2S1

D1 D2

S3

1,000 min

min

Tem

pora

l R

esol

utio

n in

min

utes

SPOT 4 Vegetation

1 x 1 km

T2U2U3

B1

M1M2

ERS-1,2 C-band 30 x 30

GOES VIS 1 X 1 km TIR 8 x 8 km

NWS WSR-88D Doppler Radar

1 x 1 km 4 x 4 km

ORBIMAGE OrbView 2 SeaWiFS

1.13 x 1.13 km

ASTER (1999) EOS AM-1

VNIR 15 x 15 m SWIR 30 x 30 m TIR 90 x 90 m

MODIS (1999) EOS AM-1

Land 0.25 x 0.25 km Land 0.50 x 0.50 km

Ocean 1 x 1 km Atmo 1 x 1 km TIR 1 x 1 km

M3M4

2 y3 y4 y

Quickbird (2000) 0.82 x 0.82 3.28 x 3.28

0.2 1.0 2 3 5 10 2 3 5 102 103 104 2 3 5 2 3 5

10

102

103

104

2 3

5

8

2 3

5

2

3

5

2

3

5

2

3

5

9 d

1 d

1 hr

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

12 hr

8105

8

8

8

.80.5

3 d

2 d

4 d

532

2

3

58

106

1 y

23

5

8107

5 y

10 y

15 20

4 8

26 d

44 d 55 d

METEOSAT VISIR 2.5 x 2.5 km

TIR 5 x 5 km

AVHRR LAC 1.1 x 1.1 km

GAC 4 x 4 km

JERS-1 MSS 18 x 24

L-band 18 x 18

Aerial Photography < 0.25 x 0.25 m (0.82 x 0.82 ft.)

1 x 1 m (3.28 x 3.28 ft.)

RADARSAT C-band 11-9, 9 25 x 28

48-30 x 28 32-25 x 28

50 x 50 22-19 x 28 63-28 x 28 100 x 100

EOSAT/Space Imaging IKONOS (1999)

Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE

OrbView 3 (1999) Pan 1 x 1 MSS 4 x 4

OrbView 4 (2000) Pan 1 x 1 MSS 4 x 4

Hyperspectral 8 x 8 m

22 d 16 d

IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1CD

Pan 5.8 x 5.8 LISS-3 23.5 x 23.5; MIR 70 x 70

WiFS 188 x 188

10,000 min

100 min

5 d

30 d

180 d

T3

C1

15 y

DE1, E2

T4

0.3

LANDSAT 4,5 MSS 79 x 79 TM 30 x 30

LANDSAT 7 ETM+ (1999) Pan 15 x 15; MSS 30 x 30

TIR 60 x 60

E1

L4

M5

T1, U1

LI

SPOT HRV 1,2,3,4 Pan 10 x10

MSS 20 x 20 SPOT 5 HRG (2001; not shown)

Pan 2.5 x 2.5; 5 x 5 MSS 10 x 10; SWIR 20 x 20

1 m0.3 5 100 m10 20

Nominal Spatial Resolution in meters

30

SPIN-2 KVR-1000 2 x 2 TK-350 10 x 10

C2

DE2

DE3 DE4 DE5

L3

0.5

S2

L2S1

D1 D2

S3

1,000 min

min

Tem

pora

l R

esol

utio

n in

min

utes

SPOT 4 Vegetation

1 x 1 km

T2U2U3

B1

M1M2

ERS-1,2 C-band 30 x 30

GOES VIS 1 X 1 km TIR 8 x 8 km

NWS WSR-88D Doppler Radar

1 x 1 km 4 x 4 km

ORBIMAGE OrbView 2 SeaWiFS

1.13 x 1.13 km

ASTER (1999) EOS AM-1

VNIR 15 x 15 m SWIR 30 x 30 m TIR 90 x 90 m

MODIS (1999) EOS AM-1

Land 0.25 x 0.25 km Land 0.50 x 0.50 km

Ocean 1 x 1 km Atmo 1 x 1 km TIR 1 x 1 km

M3M4

Various application demands as driving forces for the resolution improvements of remote sensing

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From Terra, Aqua to NPP to JPSS

NPP (2011, Oct)CrIS/ATMS

VIIRSOMPS

Terra (1999)Aqua (2002)

AIRS, AMSU & MODIS

METOP (2006)IASI/AMSU/MHS & AVHRR

JPSS/ (2016, 2019)CrIS/ATMS, VIIRS, CMIS,

OMPS & ERBS

Coriolis (2003)WindSat

NWS/NCEP

GSFC/DAO

ECMWF

UKMO

FNMOC

Meteo-France

BMRC-Australia

Met Serv Canada

NWS/NCEP

GSFC/DAO

ECMWF

UKMO

FNMOC

Meteo-France

BMRC-Australia

Met Serv Canada

NWPForecasts

NWPForecasts

NOAA Real-Time Data Delivery TimelineGround Station Scenario

NOAAReal-time

UserC3SC3S IDPSIDPS

Joint Center for Satellite Data Assimilation

Use of Advanced Sounder Data for ImprovedUse of Advanced Sounder Data for ImprovedWeather Forecasting & Numerical Weather PredictionWeather Forecasting & Numerical Weather Prediction

29

NPP Goals

The NPP mission has two major goals:

To provide a continuation of the EOS record of climate-quality observations after EOS Terra, Aqua, and Aura (i.e., it will extend key Earth system data records and/or climate data records of equal or better quality and uncertainty in comparison to those of the Terra, Aqua, and Aura sensors), and

To provide risk reduction for JPSS instruments, algorithms, ground data processing, archive, and distribution prior to the launch of the first JPSS spacecraft (but note that there are now plans to use NPP data operationally)

NPP sensors

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NPP Satellite Scheduled for Launch

Nadir facing antennas– T&C

– HRD

– SMD

VIIRS

CrIS

ATMS

OMPS

Launched: October 28, 2011

http://jointmission.gsfc.nasa.gov/

Data Products Level 1 products

VIIRS, CrIS, ATMS and OMPS Sensor Data Records (SDRs) are full resolution sensor data that are time referenced, Earth located, and calibrated by applying the ancillary information, including radiometric and geometric calibration coefficients and geo-referencing parameters such as platform ephemeris. These data are processed to sensor units (e.g., radiances). Calibration, ephemeris, and other ancillary data necessary to convert the sensor data back to sensor raw data (counts) are included.

Level 2 (EDR/CDRs) products EDR emphasis will be on generating products

with a more rapid data delivery that necessarily involves high-speed availability of ancillary data and high-performance execution of the sensor contractors' state-of-the-art science algorithms for civilian and military applications.

CDR, the requirement of timeliness can be relaxed, thereby allowing for the implementation of complex algorithms using diverse ancillary data. As understanding of sensor calibration issues and radiative transfer from the Earth and Atmosphere improves, algorithms can be improved, and products can be generated via reprocessing

EDR: environmental data records

Major image processing software

ENVI/IDL: http://www.rsinc.com/ ERDAS Imagine:

http://www.gis.leica-geosystems.com/Products/Imagine/ PCI Geomatics: http://www.pci.on.ca/ ER Mapper: http://www.ermapper.com/ INTEGRAPH: http://imgs.intergraph.com/gimage/ IDRIS: Ecognition:

http://www.definiens-imaging.com/ecognition/pro/40.htm See5 and decision tree