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 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.
Jensen, 2000
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).
Jensen, 2000
Three characteristics of
electromagnetic wave



Velocity is the speed of light, c=3 x 108 m/s
wavelength (‫ )ג‬is 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 m
nanometres (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 ‫ ג‬changing as
propagated through media of different densities, v
remains constant.
Hertz (Hz)
1
kilohertz (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)
Jensen, 2000
EMR details
(mm)
•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
Bees and some other insects can see near UV.
The Sun is the source of UV, but only > 0.3 mm
(near UV) can reach the Earth.
•Violet: 0.4 - 0.446
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
Jensen, 2000
Jensen, 2000
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).
Various Paths of
Satellite Received Radiance
Total radiance
LS
at the sensor
Solar
irradiance
E
0
Lp
90Þ
T
LT
0
T
2
Diffus e s ky
irradiance
Remote
sens or
detector
Ed
1
1,3,5
4
v
60 miles
or
100km
Atmos phere
v
0
3
LI
5
Reflectance from
neigh boring area,
Reflectance from
study area,
r n
r
Jensen, 2000
Several concepts

Planck’s equation
- if a blackbody transforms heat into radiant energy, then the radiation
2hc 2
received at a sensor is given by Planck’s equation. B( , T ) 
5 (e hc / kT )  1)

Spectral Emissivity
Actual _ Emission / Absorption(Wm 2 mm1 )
e 
Blackbody _ Emission / Absorption(Wm 2 mm1 )

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 0.80.95, old snow 0.5-0.6, forest
0.1-0.2, Earth system 0.35
EMR
EMR
EMR
0.6
average shrub
0.5
average grass
average soil
Reflectance

0.4
0.3
0.2
0.1
0
250
500
750
1000
1250
1500
Wavelength (nm)
1750
2000
2250
2500
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
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




Low-inclination, non-Sun-synchronous orbits




global coverage, fixed crossing, repeat sampling
typical altitude 500-1,500 km
example: MODIS, Landsat
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)


Spectral resolution (D ) and coverage (min to max)


Narrow bands need bigger aperture, more detectors, longer
integration time
Radiometric resolution (S/N, NEDr, NEDT ) and coverage
(dynamic range)


Optical diffraction sets minimum aperture size
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
Bit no.
7 6 5 4 3 2 1 0
0
0 0 0 0 0 0 0 0
256
1 1 1 1 1 1 1 1
8 bits as an example
bits Max. num
(2bits)
1
2
3
6
8
11
12
2
4
8
64
256
2048
4096
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
107
8
5
Jensen, 2000
3
2
I
106
8
5
30
II
10
8
7
6
5
4
3
III
2
1
0.8
0.7
0.6
0.5
IV
0.4
3
2
105
8
5
Temporal Resolution in minutes
Various
application
demands as
driving
forces for
the
resolution
improvemen
ts of remote
sensing
20
Nominal Spatial Resolution
(ground resolved distance, meters)
1m
0.3 0.5
100
80
70
60
50
40
3
2
104
8
5
3
15 y
10 y
5y
4y
3y
2y
1y
10
8
5
0.2
L2
S1
100 m
Jensen, 2000
LI
L3
E1
B1
D E1, E2
T2
U2
U3
T1, U1
L4
C1
SPOT HR V 1,2,3,4
Pan 10 x10
MSS 20 x20
SPOT 5 HR G (2001; not shown)
Pan 2.5 x 2.5; 5 x 5
MSS 10 x 10; SWIR 20 x20
180 d
C2
55 d
44 d
30 d
26 d
22 d
16 d
JERS-1
MSS 18 x 24
L-band 18 x 18
ERS-1,2
C-band 30 x 30
SPIN -2
KVR-1000 2 x 2
TK-350 10 x 10
9d
10,000 min
5d
4d
3d
2d
1d
1,000 min
12 hr
IRS-1 AB
LISS-1 72.5 x 72.5
LISS-2 36.25 x 36.25
IRS-1C D
Pan 5.8 x 5.8
LISS-3 23.5 x 23.5; MIR 70 x 70
Wi FS 188 x 188
LAND SAT 4,5
MSS 79 x 79
TM 30 x 30
LAND SAT 7 ETM + (1999)
Pan 15 x15; M SS 30 x 30
TIR 60 x 60
Quickbird (2000)
0.82 x 0.82
3.28 x 3.28
ASTER (1999)
EOS AM -1
VNIR 15 x 15 m
SWIR 30 x 30 m
TIR 90 x 90 m
D E3
D E4
D E5
D E2
R AD ARSAT
C-band
11-9, 9
25 x28
48-30 x 28
32-25 x 28
50 x50
22-19 x 28
63-28 x 28
100 x100
M5
EOSAT/Space Imaging
IKONOS (1999)
Pan 1 x 1
MSS 4 x 4
IRS-P5 ( 1999)
Pan 2.5 x 2.5
1 hr
I
II
III
IV
Land Cover Class Level
ORB IMAGE
OrbView 2
SeaW iFS
1.13 x 1.13 km
AVHR R
LAC 1.1 x 1.1 km
GAC 4 x4 km
METEOSAT
VISIR 2.5 x2.5 km
TIR 5 x 5 km
T4
GOES
VIS 1 X 1 km
TIR 8 x 8 km
Aerial Photography
< 0.25 x 0.25 m ( 0.82 x0.82 ft.)
1 x1 m (3.28 x 3.28 ft.)
min
M1
M2
N WS WSR -88D
D oppler R adar
1 x1 km
4 x4 km
T3
3
2
0.1
SPOT 4
Vegetation
1 x1 km
MODIS (1999)
EOS AM -1
Land 0.25 x0.25 km
Land 0.50 x0.50 km
Ocean 1 x 1 km
Atmo 1 x 1 km
TIR 1 x 1 km
ORB IMAGE
OrbView 3 (1999)
Pan 1 x 1
MSS 4 x 4
OrbView 4 (2000)
Pan 1 x 1
MSS 4 x 4
H yperspectral 8 x 8 m
100 min
3
2
0.3
20 30
S2
3
2
102
8
5
10
S3
D1
D2
2
103
8
5
5
1m
2 3
5
10 15 20 30
0.2 0.3 0.5 .8 1.0
2 3
5
10
2 3
1 km
1000 m
100 m
5
102
2 3
Nominal Spatial Resolution in meters
5
103
M3
M4
5 km 10 km
2 34 5
8 104
From Terra, Aqua to NPP to JPSS
Terra (1999)
Aqua (2002)
NPP (2011, Oct)
Coriolis (2003)
WindSat
METOP (2006)
IASI/AMSU/MHS & AVHRR
AIRS, AMSU & MODIS
CrIS/ATMS
VIIRS
OMPS
JPSS/ (2016, 2019)
CrIS/ATMS, VIIRS, CMIS,
OMPS & ERBS
Use of Advanced Sounder Data for Improved
Weather Forecasting & Numerical Weather Prediction
NOAA Real-Time Data Delivery Timeline
Ground Station Scenario
NWS/NCEP
GSFC/DAO
ECMWF
C3S
IDPS
NOAA
Real-time
User
UKMO
FNMOC
Meteo-France
BMRC-Australia
Joint Center for Satellite Data Assimilation
Met Serv Canada
NWP
Forecasts
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)
29
NPP sensors
NPP Satellite Scheduled for
Launch
Nadir facing antennas
– T&C
– HRD
– SMD
Launched:
October 28,
2011
VIIRS
CrIS
ATMS
OMPS
http://jointmission.gsfc.nasa.gov/
31
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 georeferencing 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.leicageosystems.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.definiensimaging.com/ecognition/pro/40.htm
See5 and decision tree