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									                               Weather monitoring and forecasting was one of the
                                first civilian (as opposed to military) applications of
                               satellite remote sensing, dating back to the first true
                               weather satellite, TIROS-1 (Television and Infrared
                                Observation Satellite - 1), launched in 1960 by the
                               United States. Several other weather satellites were
                                   launched over the next five years, in near-polar
                              orbits, providing repetitive coverage of global weather
                                      patterns. In 1966, NASA (the U.S. National
                              Aeronautics and Space Administration) launched the
                              geostationary Applications Technology Satellite (ATS-
                                    1) which provided hemispheric images of the
                                  Earth's surface and cloud cover every half hour.
For the first time, the development and movement of weather systems could be routinely
monitored. Today, several countries operate weather, or meteorological satellites to
monitor weather conditions around the globe. Generally speaking, these satellites use
sensors which have fairly coarse spatial resolution (when compared to systems for
observing land) and provide large areal coverage.
Their temporal resolutions are generally quite high, providing frequent observations of
the Earth's surface, atmospheric moisture, and cloud cover, which allows for near-
continuous monitoring of global weather conditions, and hence - forecasting. Here we
review a few of the representative satellites/sensors used for meteorological
                                           The GOES (Geostationary Operational
                                           Environmental Satellite) System is the
                                           follow-up to the ATS series. They were
                                           designed by NASA for the National
                                           Oceanic and Atmospheric
                                           Administration (NOAA) to provide the
                                           United States National Weather
                                           Service with frequent, small-scale
                                           imaging of the Earth's surface and
                                           cloud cover.

The GOES series of satellites have been used extensively by meteorologists for
weather monitoring and forecasting for over 20 years. These satellites are part of a
global network of meteorological satellites spaced at approximately 70° longitude
intervals around the Earth in order to provide near-global coverage. Two GOES
satellites, placed in geostationary orbits 36000 km above the equator, each view
approximately one-third of the Earth. One is situated at 75°W longitude and monitors
North and South America and most of the Atlantic Ocean. The other is situated at
135°W longitude and monitors North America and the Pacific Ocean basin.
Together they cover from 20°W to 165°E
longitude. This GOES image covers a portion
of the southeastern United States, and the
adjacent ocean areas where many severe
storms originate and develop. This image
shows Hurricane approaching the
southeastern United States and the Bahamas
in September of 1996.

                    Two generations of GOES satellites have been launched,
                       each measuring emitted and reflected radiation from
                     which atmospheric temperature, winds, moisture, and
                          cloud cover can be derived. The first generation of
                    satellites consisted of GOES-1 (launched 1975) through
                                                   GOES-7 (launched 1992).

   GOES-8 and the other second generation GOES satellites have
   separate imaging and sounding instruments. The imager has five
   channels sensing visible and infrared reflected and emitted solar
   radiation. The infrared capability allows for day and night imaging.
                            GOES Bands
a   Wavelength Range
                            Resolutio Application
n   (>mm)
                                      cloud, pollution, and haze detection; severe
1   0.52 - 0.72 (visible)   1 km
                                      storm identification
                                      identification of fog at night; discriminating water
    3.78 - 4.03                       clouds and snow or ice clouds during daytime;
2                           4 km
    (shortwave IR)                    detecting fires and volcanoes; night time
                                      determination of sea surface temperatures
    6.47 - 7.02                       estimating regions of mid-level moisture content
3   (upper level water      4 km      and advection; tracking mid-level atmospheric
    vapour)                           motion
    10.2 - 11.2                       identifying cloud-drift winds, severe storms, and
4                           4 km
    (longwave IR)                     heavy rainfall
    11.5 - 12.5
                                      identification of low-level moisture; determination
    (IR window
5                           4 km      of sea surface temperature; detection of dust and
    sensitive to water
                                      volcanic ash
                         NOAA AVHRR
 NOAA is also responsible for another series of satellites which are useful
for meteorological, as well as other, applications. These satellites, in sun-
synchronous, near-polar orbits (830-870 km above the Earth), are part of
 the Advanced TIROS series (originally dating back to 1960) and provide
complementary information to the geostationary meteorological satellites
  (such as GOES). Two satellites, each providing global coverage, work
 together to ensure that data for any region of the Earth is no more than
six hours old. One satellite crosses the equator in the early morning from
        north-to-south while the other crosses in the afternoon.

    The primary sensor on board the NOAA satellites, used
    for both meteorology and small-scale Earth observation
    is the Advanced Very High Resolution Radiometer
    (AVHRR). The AVHRR sensor detects radiation in the
    visible, near and mid infrared, and thermal infrared
    portions of the electromagnetic spectrum, over a swath
    width of 3000 km.
                     NOAA AVHRR Bands

       Wavelength      Spatial
Band                                Application
       Range (mm)      Resolution
       0.58 - 0.68
1                      1.1 km       cloud, snow, and ice monitoring
       0.725 - 1.1                  water, vegetation, and agriculture
2                      1.1 km
       (near IR)                    surveys
       3.55 -3.93                   sea surface temperature,
3                      1.1 km
       (mid IR)                     volcanoes, and forest fire activity
       10.3 - 11.3                  sea surface temperature, soil
4                      1.1 km
       (thermal IR)                 moisture
       11.5 - 12.5                  sea surface temperature, soil
5                      1.1 km
       (thermal IR)                 moisture
                 Other Weather Satellites

The United States operates the DMSP (Defense Meteorological Satellite
Program) series of satellites which are also used for weather monitoring.
These are near-polar orbiting satellites whose Operational Linescan
System (OLS) sensor provides twice daily coverage with a swath width of
3000 km at a spatial resolution of 2.7 km.

     There are several other meteorological satellites in
     orbit, launched and operated by other countries, or
     groups of countries. These include Japan, with the
     GMS satellite series, and the consortium of
     European communities, with the METEOSAT
     satellites. Both are geostationary satellites situated
     above the equator over Japan and Europe,
                 Land Observation Satellites/Sensors

                                Landsat-1, was launched by NASA
                                in 1972. Initially referred to as
                                ERTS-1, (Earth Resources
                                Technology Satellite), Landsat was
                                designed as an experiment to test
                                the feasibility of collecting multi-
                                spectral Earth observation data

All Landsat satellites are placed in near-polar, sun-synchronous orbits. The
first three satellites (Landsats 1-3) are at altitudes around 900 km and have
revisit periods of 18 days while the later satellites are at around 700 km and
have revisit periods of 16 days.
A number of sensors have been on board the Landsat series of satellites,
including the Return Beam Vidicon (RBV) camera systems, the
MultiSpectral Scanner (MSS) systems, and the Thematic Mapper (TM).
The most popular instrument in the early days of Landsat was the MultiSpectral
Scanner (MSS) and later the Thematic Mapper (TM). Each of these sensors
collected data over a swath width of 185 km, with a full scene being defined as
185 km x 185 km.

                             MSS Bands

    Channel                        Wavelength Range (mm)
    Landsat 1,2,3 Landsat 4,5
    MSS 4            MSS 1         0.5 - 0.6 (green)
    MSS 5            MSS 2         0.6 - 0.7 (red)
    MSS 6            MSS 3         0.7 - 0.8 (near infrared)
    MSS 7            MSS 4         0.8 - 1.1 (near infrared)
                                          TM Bands

Cha    Wavelength
nnel   Range (mm)
                           soil/vegetation discrimination; coastal mapping;
TM 1 0.45 - 0.52 (blue)
                           cultural/urban feature identification
       0.52 - 0.60         green vegetation mapping (measures reflectance
TM 2
       (green)             peak); cultural/urban feature identification
                           vegetated vs. non-vegetated and plant species
TM 3 0.63 - 0.69 (red)     discrimination (plant chlorophyll absorption);
                           cultural/urban feature identification
       0.76 - 0.90 (near   identification of plant/vegetation types, health, and
TM 4
       IR)                 biomass content; soil moisture
       1.55 - 1.75 (short sensitive to moisture in soil and vegetation;
TM 5
       wave IR)           discriminating snow and cloud-covered areas
                           vegetation stress and soil moisture discrimination
     10.4 - 12.5
TM 6                       related to thermal radiation; thermal mapping (urban,
     (thermal IR)
       2.08 - 2.35 (short discrimination of mineral and rock types; sensitive to
TM 7
       wave IR)           vegetation moisture content
           (Système Pour l'Observation de la Terre) is a series of Earth
SPOT      observation imaging satellites designed and launched by CNES (Centre
          National d'Études Spatiales) of France, with support from Sweden and

            The Indian Remote Sensing (IRS) satellite series, combines
          features from both the Landsat MSS/TM sensors and the SPOT
                                   HRV sensor.

           The first is the MEIS-II (Multispectral
MEIS-II    Electro-optical Imaging Scanner) sensor
           developed for the Canada Centre for Remote

          The Compact Airborne Spectrographic
CASI      Imager, is a leader in airborne imaging,
          being the first commercial imaging
      Marine Observation Satellites/Sensors
The Nimbus-7 satellite, launched in 1978, carried the first sensor,
the Coastal Zone Colour Scanner (CZCS), specifically intended for
monitoring the Earth's oceans and water bodies.

  The primary objective of this sensor was to observe ocean colour
  and temperature, particularly in coastal zones, with sufficient
  spatial and spectral resolution to detect pollutants in the upper
  levels of the ocean and to determine the nature of materials
  suspended in the water column.

      The first Marine Observation Satellite (MOS-1) was launched
      by Japan in February, 1987 and was followed by its
      successor, MOS-1b, in February of 1990. These satellites
MOS   carry three different sensors: a four-channel Multispectral
      Electronic Self-Scanning Radiometer (MESSR), a four-channel
      Visible and Thermal Infrared Radiometer (VTIR), and a two-
      channel Microwave Scanning Radiometer (MSR), in the
      microwave portion of the spectrum.
                 They are used for very specific detection and
 SeaWiFS         monitoring of various ocean phenomena including:
(Sea-viewing     ocean primary production and phytoplankton
Wide-Field-of    processes, ocean influences on climate processes
View Sensor)     (heat storage and aerosol formation), and monitoring
                 of the cycles of carbon, sulfur, and nitrogen.

  FLIR          Typically positioned on aircraft or helicopters, and imaging
  Forward       the area ahead of the platform, FLIR systems provide
  Looking       relatively high spatial resolution imaging that can be used
  InfraRed      for military applications, search and rescue operations,
  systems       law enforcement, and forest fire monitoring.

                An active imaging technology very similar to
  Lidar         RADAR. Lidar is also used in atmospheric
  LIght         studies to examine the particle content of
  Detection     various layers of the Earth´s atmosphere and
  And           acquire air density readings and monitor air
  Ranging       currents.
            RADAR systems are active sensors which provide
            their own source of electromagnetic energy.

RADAR       Because RADAR provides its own energy source,
RAdio       images can be acquired day or night.
            Also, microwave energy is able to penetrate
            through clouds and most rain, making it an all-
            weather sensor.
Data Reception, Transmission, and Processing
        There are three main options for transmitting data acquired by
         satellites to the surface. The data can be directly transmitted to
        Earth if a Ground Receiving Station (GRS) is in the line of sight of
                                     the satellite

                                  (A). the data can be recorded
                                    on board the satellite directly

                                 (B) for transmission to a GRS at a later
                                 time. Data can also be relayed to the GRS
                                 through the Tracking and Data Relay
                                 Satellite System (TDRSS)

(C), which consists of a series of communications satellites in
geosynchronous orbit. The data are transmitted from one satellite to
another until they reach the appropriate GRS.
For many sensors it is possible to provide
customers with quick-turnaround
imagery when they need data as quickly
as possible after it is collected.

Near real-time processing systems are
used to produce low resolution imagery
in hard copy or soft copy (digital) format
within hours of data acquisition.

Such imagery can then be faxed or
transmitted digitally to end users.
  Microwave sensing encompasses both active
  and passive forms of remote sensing

the microwave portion of the spectrum
covers the range from approximately 1cm to
1m in wavelength

         Longer wavelength microwave radiation
         can penetrate through cloud cover, haze,
         dust, and all but the heaviest rainfall
A passive microwave sensor detects the naturally
emitted microwave energy within its field of view.

This emitted energy is related to the temperature and
moisture properties of the emitting object or surface.

Passive microwave sensors are typically radiometers or scanners

                       The microwave energy recorded by a
                       passive sensor can be emitted by the
                       atmosphere (1), reflected from the surface
                       (2), emitted from the surface (3), or
                       transmitted from the subsurface (4).
Applications of          meteorologists can use
                         passive microwaves to
passive microwave
                          measure atmospheric
remote sensing                   profiles and to
include meteorology,      determine water and
hydrology, and             ozone content in the
oceanography.                      atmosphere

Hydrologists use         applications include
passive microwaves to       mapping sea ice,
measure soil moisture           currents, and
since microwave             surface winds as
emission is influenced   well as detection of
by moisture content.               pollutants
Active microwave sensors provide their own source of
microwave radiation to illuminate the target.

                                      Active microwave sensors are
                                   generally divided into two distinct
                                    categories: imaging and non-

      The most common form of imaging active
           microwave sensors is RADAR.
          The radar’s sensor transmits a microwave
          (radio) signal towards the target and
          detects the backscattered portion of the
     The strength of the backscattered signal is
     measured to discriminate between different
     targets and the time delay between the
     transmitted and reflected signals determines the
     distance (or range) to the target.

    Non-imaging microwave sensors include
    altimeters and scatterometers.

Radar altimeters transmit short microwave pulses and
measure the round trip time delay to targets to determine
their distance from the sensor.
      Scatterometers are also generally non-
      imaging sensors and are used to make
      precise quantitative measurements of
      the amount of energy backscattered
      from targets.
Scatterometry measurements over
ocean surfaces can be used to estimate
wind speeds based on the sea surface

Ground-based scatterometers are
used extensively to accurately
measure the backscatter from various
targets in order to characterize
different materials and surface types.
This is analogous to the concept of
spectral reflectance curves in the
optical spectrum.
As with passive microwave sensing, a major
advantage of radar is the capability of the
radiation to penetrate through cloud cover and
most weather conditions.

  Because radar is an active sensor, it
  can also be used to image the surface
  at any time, day or night.

These are the two primary advantages of
  radar: all-weather and day or night
    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 antenna receives a portion of
                                the transmitted energy reflected
                               (or backscattered) from various
                                   objects within the illuminated
                                                         beam (C)
the polarization of the radiation is
also important. Polarization refers to
the orientation of the electric field

                              Most radars are designed to
                              transmit microwave radiation
                              either horizontally polarized (H)
                              or vertically polarized (V).

                                  HH - for horizontal transmit
                                    and horizontal receive,
                                 VV - for vertical transmit and
                                        vertical receive,
                                  HV - for horizontal transmit
                                   and vertical receive, and
                                 VH - for vertical transmit and
                                       horizontal receive.
Airborne and Spaceborne Radar Systems
Convair-580 C/X SAR system            AirSAR system


Sea Ice and Terrain
Assessment (STAR)                                 ERS-1

In order to take advantage of and make good use of remote
sensing data, we must be able to extract meaningful information
from the imagery. This brings us to the topic of discussion in this
chapter - interpretation and analysis

                               Interpretation and analysis
                               of remote sensing imagery
                               involves the identification
                               and/or measurement of
                               various targets in an image
                               in order to extract useful
                               information about them.
                              Targets in remote sensing images may be
                              any feature or object which can be
                              observed in an image, and have the
                              following characteristics:
 Targets may be a point, line, or area feature. This
 means that they can have any form, from a bus in a
 parking lot or plane on a runway, to a bridge or
 roadway, to a large expanse of water or a field.

The target must be distinguishable; it must contrast
with other features around it in the image.
 Much interpretation and identification of targets in
 remote sensing imagery is performed manually or
 visually, i.e. by a human interpreter.

In many cases this is done using imagery displayed in
a pictorial or photograph-type format, independent of
what type of sensor was used to collect the data and
how the data were collected.

 In this case we refer to the data as being in analog format.

                         remote sensing images can also
                         be represented in a computer as
                         arrays of pixels, with each pixel
                         corresponding to a digital
                         number, representing the
                         brightness level of that pixel in
                         the image
                  In this case, the data are in a digital format.
                  Visual interpretation may also be performed
                  by examining digital imagery displayed on a
                  computer screen. Both analogue and digital
                  imagery can be displayed as black and white
                  (also called monochrome) images, or as
                  colour images by combining different
                  channels or bands representing different
When remote sensing data are available in digital format,
digital processing and analysis may be performed using a
computer. Digital processing may be used to enhance data
as a prelude to visual interpretation. Digital processing and
analysis may also be carried out to automatically identify
targets and extract information completely without
manual intervention by a human interpreter. However,
rarely is digital processing and analysis carried out as a
complete replacement for manual interpretation. Often, it
is done to supplement and assist the human analyst.
Manual interpretation and analysis dates back to the early
beginnings of remote sensing for air photo interpretation.
Digital processing and analysis is more recent with the advent
of digital recording of remote sensing data and the
development of computers. Both manual and digital
techniques for interpretation of remote sensing data have
their respective advantages and disadvantages. Generally,
manual interpretation requires little, if any, specialized
equipment, while digital analysis requires specialized, and
often expensive, equipment. Manual interpretation is often
limited to analyzing only a single channel of data or a single
image at a time due to the difficulty in performing visual
interpretation with multiple images.
     The computer environment is more
     amenable to handling complex images of
     several or many channels or from several
     dates. In this sense, digital analysis is useful
     for simultaneous analysis of many spectral
     bands and can process large data sets much
     faster than a human interpreter.
 Manual interpretation is a subjective process,
 meaning that the results will vary with
 different interpreters. Digital analysis is based
 on the manipulation of digital numbers in a
 computer and is thus more objective, generally
 resulting in more consistent results. However,
 determining the validity and accuracy of the
 results from digital processing can be difficult.
It is important to reiterate that visual and digital
analyses of remote sensing imagery are not
mutually exclusive. Both methods have their
merits. In most cases, a mix of both methods is
usually employed when analyzing imagery. In
fact, the ultimate decision of the utility and
relevance of the information extracted at the
end of the analysis process, still must be made
Recognizing targets is the key to interpretation and
information extraction. Observing the differences between
targets and their backgrounds involves comparing different
targets based on any, or all, of the visual elements of tone,
shape, size, pattern, texture, shadow, and association.

                            Tone         refers to the
                            relative brightness or colour of
                            objects in an image. Generally,
                            tone is the fundamental
                            element for distinguishing
                            between different targets or
                            features. Variations in tone also
                            allows the elements of shape,
                            texture, and pattern of objects
                            to be distinguished.
Shape refers to the
general form, structure, or
outline of individual objects.
Shape can be a very distinctive
clue for interpretation.
Straight edge shapes typically
represent urban or agricultural
(field) targets, while natural
features, such as forest edges,
are generally more irregular in
shape, except where man has
created a road or clear cuts.
Farm or crop land irrigated by
rotating sprinkler systems
would appear as circular
Size of objects in an image
is a function of scale. It is
important to assess the size of a
target relative to other objects
in a scene, as well as the
absolute size, to aid in the
interpretation of that target. A
quick approximation of target
size can direct interpretation to
an appropriate result more
quickly. For example, if an
interpreter had to distinguish
zones of land use, and had
identified an area with a number
of buildings in it, large buildings
such as factories or warehouses
would suggest commercial
property, whereas small
buildings would indicate
residential use.
Pattern refers to the
spatial arrangement of visibly
discernible objects. Typically an
orderly repetition of similar
tones and textures will produce
a distinctive and ultimately
recognizable pattern. Orchards
with evenly spaced trees, and
urban streets with regularly
spaced houses are good
examples of pattern.
Texture refers to the
arrangement and frequency of tonal
variation in particular areas of an
image. Rough textures would consist
of a mottled tone where the grey
levels change abruptly in a small
area, whereas smooth textures would
have very little tonal variation.
Smooth textures are most often the
result of uniform, even surfaces, such
as fields, asphalt, or grasslands. A
target with a rough surface and
irregular structure, such as a forest
canopy, results in a rough textured
appearance. Texture is one of the
most important elements for
distinguishing features in radar
Shadow is also helpful in
interpretation as it may provide an
idea of the profile and relative
height of a target or targets which
may make identification easier.
However, shadows can also reduce
or eliminate interpretation in their
area of influence, since targets
within shadows are much less (or
not at all) discernible from their
surroundings. Shadow is also useful
for enhancing or identifying
topography and landforms,
particularly in radar imagery.
Association takes into account the
relationship between other recognizable
objects or features in proximity to the
target of interest. The identification of
features that one would expect to
associate with other features may
provide information to facilitate
identification. In the example,
commercial properties may be
associated with proximity to major
transportation routes, whereas
residential areas would be associated
with schools, playgrounds, and sports
fields. In our example, a lake is
associated with boats, a marina, and
adjacent recreational land.
Digital Image Processing
    In today's world of advanced technology where most
    remote sensing data are recorded in digital format, virtually
    all image interpretation and analysis involves some element
    of digital processing. Digital image processing may involve
    numerous procedures including formatting and correcting of
    the data, digital enhancement to facilitate better visual
    interpretation, or even automated classification of targets
    and features entirely by computer. In order to process
    remote sensing imagery digitally, the data must be recorded
    and available in a digital form suitable for storage on a
    computer tape or disk. Obviously, the other requirement for
    digital image processing is a computer system, sometimes
    referred to as an image analysis system, with the
    appropriate hardware and software to process the data.
    Several commercially available software systems have been
    developed specifically for remote sensing image processing
    and analysis.
For discussion purposes, most of the common image
processing functions available in image analysis systems
can be categorized into the following four categories:

                Image Enhancement
               Image Transformation
           Image Classification and Analysis

 Preprocessing functions involve those operations that are
 normally required prior to the main data analysis and
 extraction of information, and are generally grouped as
 radiometric or geometric corrections.
Radiometric corrections include correcting the data for
sensor irregularities and unwanted sensor or
atmospheric noise, and converting the data so they
accurately represent the reflected or emitted radiation
measured by the sensor.

Geometric corrections include correcting for geometric
distortions due to sensor-Earth geometry variations, and
conversion of the data to real world coordinates (e.g.
latitude and longitude) on the Earth's surface.
The objective of the second group
of image processing functions
grouped under the term of image
enhancement, is solely to improve
the appearance of the imagery to
assist in visual interpretation and
analysis. Examples of enhancement
functions include contrast
stretching to increase the tonal
distinction between various
features in a scene, and spatial
filtering to enhance (or suppress)
specific spatial patterns in an
Image transformations are operations similar in concept to
those for image enhancement. However, unlike image
enhancement operations which are normally applied only to a
single channel of data at a time, image transformations usually
involve combined processing of data from multiple spectral
bands. Arithmetic operations (i.e. subtraction, addition,
multiplication, division) are performed to combine and
transform the original bands into "new" images which better
display or highlight certain features in the scene. We will look at
some of these operations including various methods of
spectral or band ratioing, and a procedure called principal
components analysis which is used to more efficiently
represent the information in multichannel imagery.
Image classification and analysis
operations are used to digitally identify
and classify pixels in the data.
Classification is usually performed on
multi-channel data sets (A) and this
process assigns each pixel in an image to
a particular class or theme (B) based on
statistical characteristics of the pixel
brightness values. There are a variety of
approaches taken to perform digital
classification. We will briefly describe the
two generic approaches which are used
most often, namely supervised and
unsupervised classification.
         Some project titles
      Pre-processing in image analysis of satellite pictures (melih)
Image Enhancement and Image Transformations in remote sensing (umut)
      Image Classification, Data Integration and Analysis (çiğdem)
   Multispectral Scanning and Thermal Imaging of satellites (german)
Radiometric, spectral and temporal resolutions in remote sensing( hidayet)
          Remote sensing applications for Agriculture (zafer)
          Remote sensing applications for Hydrology (tuğrul)
            Remote sensing applications for Geology (emre)
    Remote sensing applications for Oceans and Coastal(mehmet ali)
         Advanced radar applications in Meteorology(mahmut
         Remote sensing applications in Meteorology (BURAK)
         Remote sensing applications in Meteorology (SEYDA)
     Remote sensing applications in Meteorology (ebru)

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