Observation through Remote

Sensing

Dr. Muhammad Athar Haroon

PMD

Sentinel 2, 27. August 2016

Sentinel: 9 Dec,2017

Overview

Introduction to Remote Sensing

Principles of RS

Features of the RS Satellites

Applications of Remote Sensing

The art and science of obtaining information about an object or feature without

physically coming in contact with that object or feature

Remote sensing can be used to measure

Variations in acoustic wave distributions (Sonar)

Variations in electromagnetic energy distributions (eye)

Remotely collected data through various sensors may be

analyzed to obtain information about the objects or features

under investigation

Remote Sensing

http://geoportal.icimod.org

Remote Sensing of Electromagnetic Energy

Variation in electromagnetic energy can be measured using photographic or non-

photographic sensors

Remote sensing of Electromagnetic energy is used for earth observation

“Remote sensing is detecting and measuring electromagnetic energy emanating or

reflected from distant objects made of various materials, so that we can identify and

categorize these objects by class or type, substance and spatial distribution”

[American Society of Photogrammetry, 1975]

Surface parameters are inferred through the measurement and interpretation of the

electromagnetic energy / radiation from the Earth’s surface

Electromagnetic Energy

Electromagnetic energy or electromagnetic radiation (EMR)

Energy propagated in the form of an advancing interaction between electric and

magnetic fields (Sabbins, 1978)

Travels with the velocity of light

Visible light, ultraviolet rays, infrared, heat, radio waves and x-rays are different forms

Expressed either in terms of frequency (f) or wave length (λ) of radiation

Shorter wavelengths have higher energy content and longer wavelengths have

lower energy content

h = Planck's constant (6.626 x 10-34 Joules-sec)

c = Speed of light (3 x 108 m/sec)

f = Frequency expressed in Hertz

λ = wavelength in micro meters (µm)

E = h.c.f or h.c / λ

Electromagnetic Spectrum

EMR spectrum : Distribution of the continuum of energy plotted as a function of wavelength

(or frequency)

In remote sensing terminology, electromagnetic energy is generally expressed in terms of

wavelength, λ.

Electromagnetic Spectrum…

Ranges from gamma rays (very short) to radio waves (long wavelengths)

Gamma rays, X-rays and most of the UV rays

‒ Mostly absorbed by the earth’s atmosphere and hence not used in remote sensing

Most of the remote sensing systems operate in visible, infrared (IR) and microwave regions

Some systems use the long wave portion of the UV spectrum

Electromagnetic Spectrum…

Visible region

• Small region in the range 0.4 - 0.7 μm

• Blue : 0.4 – 0.5 μm

• Green: 0.5-0.6 μm

• Red: 0.6-0.7 μm.

• Ultraviolet (UV) region adjoins the blue end

• Infrared (IR) region adjoins the red end

Microwave region

• Longer wavelength intervals

• Ranges from 0.1 to 100 cm

• Includes all the intervals used by radar systems.

Infrared (IR) region

• Spanning between 0.7 and 100 μm

• 4 subintervals of interest for remote sensing

• Reflected IR (0.7 - 3.0 μm)

• Photographic IR (0.7 - 0.9 μm)

• Thermal IR at 3 - 5 μm

• Thermal IR at 8 - 14 μm

Remote Sensing of Electromagnetic Radiation

Selective wavelength bands are used in remote sensing

Electromagnetic energy interacts with the atmospheric gases and particles

- Scattering and Absorption

- Atmosphere absorbs / backscatters a fraction of the energy and transmits the remainder

Atmospheric windows : Wavelength regions through which most of the energy is transmitted through atmosphere

Remote Sensing of Electromagnetic Radiation…

Remote sensing data acquisition is limited through these atmospheric windows

Atmospheric windows in electromagnetic radiation (EMR) spectrum (Source: Short, 1999)

Atmosphere is mostly opaque for the areas marked in Blue color

Atmospheric windows

The most common sources of energy are

Incident solar energy

• Maximum energy in the visible region

Radiation from the Earth

Maximum energy in the thermal IR region

Two atmospheric windows

• at 3 to 5μm and at 8 to 14μm

Radar & Passive microwave systems operate through a window in the region 1 mm-1 m

Atmospheric window Wavelength

band (μm)

Characteristics

Upper ultraviolet, Visible

and photographic IR

0.3-1 apprx. 95% transmission

Reflected infrared 1.3, 1.6, 2.2 Three narrow bands

Thermal infrared 3.0-5.0

8.0-14.0

Two broad bands

Microwave >5000 Atmosphere is mostly

transparent

Atmospheric windows

Reflected Energy in Remote Sensing

Energy reflected from the surface is recorded in remote sensing

Fraction of energy that is reflected / scattered is unique for each material

Used for distinguishing different features on an image

Within a feature class, energy reflected / emitted / absorbed depends on the wavelength

Features may be similar and hence indistinguishable using single spectral band

Their reflectance properties may be different in another spectral band

Use of multiple wavelength bands helps to further differentiate the features within one

class

Reflected energy from multiple wavelength bands are recorded in multi-spectral

remote sensing

Spectral Reflectance

Spectral Reflectance Rλ

Spectral Reflectance Curve

Graphical representation of the spectral response over different wavelengths of the

electromagnetic spectrum

Gives an insight into the spectral characteristics of different objects

Used for the selection of a particular wavelength band for remote sensing data

acquisition

Essential to interpret and analyze an image obtained in any one or multiple

wavelengths

Spectral Reflectance Curves

Average reflectance curves of healthy vegetation, dry barren soil and clear water bodies

Reflectance of individual features varies considerably above and below the average

The average curves demonstrate some fundamental points concerning spectral reflectance

Typical spectral reflectance curves for vegetation, soil and water (Lillesand et

al., 2004)

Use of Spectral Reflectance in Remote Sensing …

Example:

Generalized spectral reflectance curves for

deciduous and coniferous trees

Sensor selection to differentiate deciduous and

coniferous trees

• Curves overlap in the visible portion

• Both class will be seen in shades of green

Deciduous and coniferous trees cannot

be differentiated through visible

spectrum

• Spectral reflectance are quiet different in NIR

Deciduous and coniferous trees can be

differentiated through NIR spectrum

Spectral reflectance within one class is not

unique, and hence the ranges are shown

Maximum

reflectance in

green gives the

green colour

Panchromatic photograph using reflected

sunlight over the visible wavelength• Coniferous and deciduous trees are not differentiable

Black and white infrared photograph using

reflected sunlight over 0.7 to 0.9 mm wavelength• Deciduous trees show bright signature compared to

coniferous trees

(Source: Lillesand et al., 2004)

Passive/ Active Remote Sensing

A simple analogy:

Passive remote sensing is similar to taking a picture with an ordinary camera

Active remote sensing is analogous to taking a picture with camera having built-in flash

Passive Remote Sensing

Passive remote sensing: Source of energy is that naturally available

Solar energy

Energy emitted by the Earth etc.

Most of the remote sensing systems work in passive mode using solar energy

Solar energy reflected by the targets at specific bands are recorded using sensors

For ample signal strength received at the sensor, wavelengths capable of traversing

through the atmosphere without significant loss, are generally used

The Earth will also emit some radiation since its ambient temperature is about 300o K.

Passive sensors can also be used to measure the Earth’s radiance

Not very popular as the energy content is very low

Active Remote Sensing

Active remote sensing: Energy is generated and emitted from a sensing platform

towards the targets

Energy reflected back by the targets are recorded

Longer wavelength bands are used

Example: Active microwave remote sensing (radar)

Pulses of microwave signals are sent towards the target from the radar antenna

located on the air / space-borne platform

The energy reflected back (echoes) are recorded at the sensor

Remote Sensing Platforms

Ground level remote sensing

Very close to the ground (e.g., Hand held

camera)

Used to develop and calibrate sensors for

different features on the Earth’s surface

Aerial remote sensing

Low altitude aerial remote sensing

High altitude aerial remote sensing

Space-borne remote sensing

Space shuttles

Polar orbiting satellites

Geo-stationary satellites

Air-borne Remote sensing

Downward or sideward looking sensors mounted on aircrafts are used to obtain images

Very high spatial resolution images (20 cm or less) can be obtained

Drawbacks:

Less coverage area and high cost per unit area of ground coverage

Mainly intended for one-time operations, whereas space-borne missions offer

continuous monitoring of the earth features

LiDAR, analog aerial photography, thermal imagery and digital photography are commonly

used in airborne remote sensing

Space-borne Remote sensing

Sensors are mounted on space shuttles or satellites orbiting the Earth

Geostationary and Polar orbiting satellites

Example: Landsat satellites, Indian remote sensing (IRS) satellites, IKONOS, SPOT

satellites,

AQUA and TERRA (NASA), and INSAT satellite series

Advantages:

Large area coverage, less cost per unit area of coverage

Continuous or frequent coverage of an area of interest

Automatic/ semi-automatic computerized processing and analysis.

Drawback: Lower resolution

An Ideal Remote Sensing System

Basic components of an ideal remote sensing system

A uniform energy source

A non-interfering atmosphere

A series of unique energy/matter interactions at the Earth's surface

A super sensor

A real-time data handling system

Multiple data users

An Ideal Remote Sensing System…

Basic components of an ideal remote sensing system…

i. A uniform energy source : Provides constant, high level of output over all wavelengths

ii. A non-interfering atmosphere: Does not modify the energy transmitted through it

iii. A series of unique energy/matter interactions at the Earth's surface: Generates reflected / emitted

signals that are

Selective with respect to wavelength and

Unique to each object or earth surface feature type

An Ideal Remote Sensing System…

Basic components of an ideal remote sensing system…

iv. A super sensor : Simple, accurate, economical and highly sensitive to all wavelengths

Yields data on the absolute brightness (or radiance) from a scene as a function of wavelength.

v. A real-time data handling system: Generates radiance-wavelength response and

processes into an interpretable format in real time

vi. Multiple data users : Possess knowledge in remote sensing techniques and in their

respective disciplines. Use the collected information in their respective disciplines

Features of the Remote Sensing Satellites

Introduction:

Remote sensing satellite programs

MODIS program

Landsat program

SPOT mission

Sentinel program

Very high resolution satellites

IKONOS

QuickBird

Geo-stationary satellites

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard

the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1)

satellites.

Terra's orbit around the Earth is timed so that it passes from north to south across the equator

in the morning

Aqua passes south to north over the equator in the afternoon

Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days,

acquiring data in 36 spectral bands.

These data will improve our understanding of global dynamics and processes occurring on

the land, in the oceans, and in the lower atmosphere.

MODIS Program

Bands Wave Length (nm) Remark1 620 - 670 Land/Cloud/Aerosols Boundaries

2 841 - 876

3 459 - 479 Land/Cloud/Aerosols Properties

4 545 - 565

5 1230 - 1250

6 1628 - 1652

7 2105 - 2155

8 405 - 420 Ocean Colour/ Phytoplankton/ Biogeochemistry

9 438 - 448

10 483 - 493

11 526 - 536

12 546 - 556

13 662 - 672

14 673 - 683

15 743 - 753

16 862 - 877

17 890 - 920 Atmospheric Water Vapour

18 931 - 941

19 915 - 965

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Bands Wave Length (μm) Remark20 620 - 670 Surface / Cloud Temperature

21 841 - 876

22459 - 479

23 545 - 565

24 1230 - 1250 Atmospheric Temperature

25 1628 - 1652

26 2105 - 2155 Cirrus Clouds Water Vapour

27405 - 420

28 438 - 448

29 483 - 493 Cloud Properties

30 526 - 536 Ozone

31 546 - 556 Surface / Cloud Temperature

32 662 - 672

33 673 - 683 Cloud Top Altitude

34 743 - 753

35 862 - 877

36890 - 920

Chara

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Landsat Program

Longest running program for acquiring satellite imageries of the Earth

Landsat-1 was launched in July 1972

A collaborative effort of NASA and the US Department of the Interior

The program was earlier called Earth Resources Technology Satellites

(ERTSs) and was renamed as Landsat in 1975

The mission consists of 8 satellites launched successively

The recent one in the series Landsat-8, which is also called Landsat Data

Continuity Mission (LDCM) was launched in February, 2013

Time Line of the Landsat Satellite Program

(http://landsat.usgs.gov/about_landsat7.php)

Landsat Program…

Sensors used

•Return Beam Vidicom (RBV)

•Multispectral Scanner (MSS)

•Thematic Mapper

•Enhanced Thematic Mapper (ETM)

•Enhanced Thematic Mapper Plus (ETM+)

Satellite orbit

•Sun-synchronous, near polar orbits at different altitudes for each mission

Sensors used in the Landsat Missions 1-7

Spectral sensitivity and spatial resolution of the sensors used in the Landsat missions

Sensors

RBV MSS TM ETM ETM+

Band

Wavelength

(μm) Band

Wavelength

(μm) Band

Wavelength

(μm) Band

Wavelength

(μm) Band

Wavelength

(μm)

1 0.475-0.575 4 0.5-0.6 1 0.45-0.52 TM B1-B7 Same as TM ETM B1-B8 Same as ETM

2 0.580-0.680 5 0.6-0.7 2 0.52-0.60

3 0.690-0.830 6 0.7-0.8 3 0.63-0.69 8 0.5-0.90

4 0.505-0.750 7 0.8-1.1 4 0.76-0.90

8 10.4-12.6 5 1.55-1.75

6 10.4-12.5

7 2.08-2.35

Sensors used in the Landsat -8 Mission Two sensors

Operational Land Imager (OLI) : 9 bands including 1 panchromatic band

Thermal Infrared Scanner (TIRS): 2 thermal bands

Operational Land Imager (OLI) Thermal Infrared Scanner (TIRS)

Band Wavelength (μm) Remark Band Wavelength (μm) Remark

1 0.43-0.45 Coastal aerosol detection 1 10.60-11.19 Thermal infrared

2 0.45-0.51 Blue 2 11.50-12.51 Thermal infrared

3 0.53-0.59 Green

4 0.64-0.67 Red

5 0.85-0.88 Near infrared

6 1.57-1.65 Short wave infrared

7 2.11-2.29 Short wave infrared

8 0.50-0.68 Panchromatic

9 1.36-1.38 Cirrus cloud detection

Spectral bands of the OLI and TIPS sensors of the Landsat-8 mission

SPOT Satellite Program

SPOT (Systeme Pour l’Observation de la Terre)

Designed by the Centre National d’Etudes Spatiales (CNES), France

Commercially oriented Earth observation program

The first satellite of the mission, SPOT-1 was launched in February, 1986.

First Earth observation satellite that used a linear array of sensors and the

pushbroom scanning techniques

First system to have point able optics, enabling side-to-side off-nadir viewing

capabilities

Time Line of the SPOT Missions

(Source: http://smsc.cnes.fr/SPOT/index.htm)

Sensors Used in the SPOT Missions

SPOT 1, 2 and 3

Two identical High Resolution Visible (HRV) imaging systems

Operational either in the panchromatic mode or in the MSS mode

Along-track, push-broom scanning method

Each HRV contained four CCD sub-arrays

Off-nadir viewing capability enables stereoscopic imaging

SPOT 4

Two identical High Resolution Visible and Infrared (HRVIR) sensors and a Vegetation instrument (VI)

SPOT-5

Two high resolution geometric (HRG) instruments, a single high resolution stereoscopic (HRS)

instrument, and a (VI )

SPOT-4 SPOT-5

HRVIR VI HRG HRS and VI

Bands Wavelength (μm) Bands Wavelength (μm) Bands

Wavelength

(μm) Bands Wavelength (μm)

1 0.53-0.59 1 0.43-0.47 PAN 0.48-0.71 PAN 0.49-0.69

2 0.61-0.68 2 0.61-0.68 1 0.50-0.59 0 0.45-0.52

3 0.79-0.89 3 0.79-0.89 2 0.61-0.68 2 0.61-0.58

4 1.58-1.75 4 1.58-1.75 3 0.78-0.89 3 0.78-0.89

4 1.58-1.75 4 1.58-1.75

Characteristics of the Sensors Used in SPOT 4 and 5

Characteristics of the Sensor Used in SPOT-6

SPOT-6

Employs two New AstroSat Optical Modular Instruments (NAOMI)

NAOMI operates in 5 spectral bands, including one panchromatic band

Band Wavelength (μm) Remark

PAN 0.45-0.745 Panchromatic

1 0.450-0.525 Blue

2 0.530-0.590 Green

3 0.625-0.695 Red

4 0.760-0.890 Near infrared

Sentinel Program

Sentinel-2 is an Earth observation mission developed by ESA as part of the Copernicus

Programme to perform terrestrial observations. It consists of two identical satellites built

by Airbus Ds,Sentinel-2A and Sentinel 2-B.

The Sentinel-2 mission has the following capabilities:

Multi-spectral data with 13 bands in the visible, near infrared and short wave infrared part of

the spectrum

Systematic global coverage of land surfaces from 56° S to 84° N, coastal waters, and all of

the Mediterranean Sea

Revisiting every 5 days under the same viewing angles. At high latitudes, Sentinel-2 swath

overlap and some regions will be observed twice or more every 5 days, but with different viewing

angles.

Spatial resolution of 10 m, 20 m and 60 m

Free and open data policy (https://scihub.copernicus.eu/)

Characteristics of the Sensors Used in Sentinel-2Band Wavelength (μm) Resolution Remark

1 0.443 60 Aerosol

2 0.490 10 Blue

3 0.560 10 Green

4 0.665 10 Red

5 0.705 20 Vegetation

6 0.740 20 Vegetation

7 0.783 20 Vegetation

8 0.842 10 NIR

8A 0.865 20 Vegetation

9 0.945 60 Water Vapour

10 1.375 60 SWIR

11 1.610 20 SWIR

12 2.190 20 SWIR

Very High Resolution Systems

IKONOS

Commercial high resolution system operated by GeoEye.

The satellite was launched in September 1999

Employs linear array technology and collects data in four multispectral bands and one

panchromatic band

IKONOS was the first successful commercial satellite to collect sub-meter resolution

images

•1 m in panchromatic mode

•4 m in the MSS mode

Imagery from the panchromatic and multispectral sensors can be merged to create 0.82-

meter color imagery (pan-sharpened).

IKONOS Images

IKONOS (0.8m) image of the Tadco Farms,

Saudi ArabiaIKONOS image of the Denver Broncos

Stadium, Denver, Colorado, USA

Details of the IKONOS Satellite

Satellite IKONOS

Launch date Sep, 2009

Orbit Sun-synchronous

Eq. crossing 10:30am

Altitude 682 km

Inclination 98.1 deg

Repeat cycle 11 days (more frequent imaging due to the off-nadir

viewing capabilities up to 45 deg)

Sensor PAN and MSS

Wavelength bands (μm) PAN 0.45-0.90

MSS: 0.45-0.52

0.52-0.60

0.63-0.69

0.76-0.90

Spatial resolution PAN : 0.81m

MSS: 4m

Radiometric resolution 11 bits

Very High Resolution Systems…

QuickBird

Commercial high resolution remote sensing system

Operated by Digital Globe, Inc

Launched in October 2001

Relatively low orbit, at an altitude 450 km.

Payloads: Panchromatic camera and a four-band multispectral scanner

QuickBird sensors are composed of linear arrays detectors

Spatial resolution

• 0.61 m in the panchromatic mode

• 2.4 m in the multispectral mode

Details of the QuickBird satellite

Satellite QuickBird

Launch date Oct, 2011

Orbit Sun-synchronous

Eq. crossing 10:00 am

Altitude 450 km

Inclination 98 deg

Revisit period Average revisit time is 1-3.5days depending upon the latitude

and the image collection angle

Sensor PAN and MSS

Wavelength bands (μm) PAN 0.405-1.053

MSS: 0.43-0.545

0.466-0.620

0.590-0.710

0.715-0.918

Spatial resolution PAN : 0.61 m

MSS: 2.4 m

Radiometric resolution 11 bits

QuickBird Image

QuickBird (61cm) true colour image for a small region in Nigeria

(Source : www.satimagingcorp.com )

Applications of RS…

Some of the applications of RS data can be:

Mapping (Topography, Land use/ land cover, Infrastructure)

Resource

Agriculture (crops monitoring and prediction)

Forestry

Water resources

Urban and regional planning

Environments assessment

Military surveillance

Water Resource Applications

To monitor :

Quality

Quantity

Geographic distribution

Flood extent and progress, rescue, relief

Flood damage estimates

Water pollution detection

Locate the discharge and extent of its plume

Sediment pollution

Oil spills

Recreation

VEGITATION INDEX

Collection of timely info on agricultural aspects is always important

In situ collection is time consuming and expensive often impossible

An alternative is measurement of vegetative amount and condition based on analysis

of RS data

Can help to assess canopy characteristic such as productivity and vegetative ground

cover

To help crop yield forecast

VEGITATION INDICES-MODIS

Vegetation Index (VI):

VI = IR/ Red

Normalized Difference Vegetation Index (NDVI):

NDVI = (Band 2-Band 1) / (Band 2+Band 1)

NDVI closer to 1 shows better vegetation

Monthly NDVI images with 1-Km resolution (MOD13A3, collection v005)

downloaded from National Aeronautics and Space Administration’s (NASA)

(http://ladsweb.nascom.nasa.gov)

Precipitation

Remote sensing has been used to assess

the occurrence and intensity of rainfall

Basic concept: Differentiation of precipitating

clouds from the non-precipitating clouds

Cloud brightness estimated using remote

sensing is used to identify precipitating

clouds

Both optical and microwave remote sensing

techniques have been used

Some of the important satellite rainfall products

Program

(Organization)Spectral bands used Characteristics and source of data

TRMM

(NASA and JAXA)

VIS, IR

Passive & active

microwave

Sub-daily

0.25o (~27 km) spatial resolution

(ftp://trmmopen.gsfc.nasa.gov/pub/merged)

GPM

VIS, IR

Passive & active

microwave

Sub-daily

0.1° spatial resolution

(https://pmm.nasa.gov/data-access/downloads/gpm

PERSIANN

(CHRS)IR

0.25o spatial resolution

Temporal resolution: 30 min. aggregated to 6 hrs.

(http://chrs.web.uci.edu/persiann/)

CMORPH

(NOAA)Microwave

0.08 deg (8 km) spatial and 30 min. temporal resolution

(http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_descrip

tion.html)

Acronyms

CHRS : Center for Hydrometeorology and Remote Sensing,

CMORPH : (CPC) MORPHing technique

NASA : National Aeronautics and Space Administration, USA

PERSIANN: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network

TRMM : Tropical Rainfall Measuring

GPM : Global Precipitation Measurement

WMO : World Meteorological Organization

Land Use / Land Cover

Land use / land cover identification and mapping

Based on the difference in the spectral signature of land

covers in different bands

Using remote sensing techniques, fine spatial resolution

and frequent temporal sampling can be achieved

Remote sensing helps to study the dynamics of land use

/ land cover pattern, and its impact on the hydrologic

processes

Differentiation of closely resembling land cover classes is

possible (e.g., crop classification) with Hyperspectral

remote sensing techniques

(http://glcf.umd.edu/data/lc/)MCD12Q1

Evapotranspiration

Evapotranspiration (ET): Water and energy flux between the land surface and the

lower atmosphere

ET is controlled by

Feedback mechanism between the atmosphere and the land surface

Soil and vegetation characteristics

Hydro-meteorological conditions

Evapotranspiration …

Remote sensing of ET

Direct estimation of ET through remote sensing is difficult

Indirect approaches are used

Remote sensing is used to measure

Surface conditions like albedo, soil moisture

Vegetation characteristics NDVI and leaf area index (LAI)

Surface temperature

Data obtained from remote sensing are used in different models to

simulate the actual ET

Evapotranspiration …

Remote sensing of ET

Optical remote sensing using the VIS and NIR bands have been commonly used

MODIS Global Terrestrial Evapotranspiration Project (MOD16)

As a part of the NASA / EOS project to estimate global terrestrial ET from Earth’s land

surface by using satellite remote sensing data

Provides global ET data sets at regular grids of 1 sq.km for the land surfaces

At 8-day, monthly and annual intervals for the period 2000-2012 images (MOD16A2, collection

v005) could be obtained from Numerical Terradynamic Simulation Group (NTSG) at the University of

Montana (http://www.ntsg.umt.edu/).

Soil Moisture

Remote sensing techniques are advantageous over the conventional in-situ

methods

Capable to capture spatial variation over a large spatial extent

Frequent sampling of an area is possible depending upon the revisit time of the

satellite

Soil Moisture …

Satellite / Sensors used for retrieving soil moisture data

Passive microwave sensors

• SMMR, AMSR-E and SSM/I

Active microwave sensors

• Advanced SCATterometer (ASCAT) aboard the EUMETSAT MetOp satellite

Thermal sensors

• Data from the thermal bands of the MODIS sensor onboard Terra satellite have been used for

retrieving soil moisture data

Hyper-spectral remote sensing techniques

• Uses reflectivity in the VIS and the NIR bands

• Changes in the spectral reflectance curves due to the presence of soil moisture are identified

• Multiple narrow bands help to extract most appropriate bands for the soil moisture estimation

Global average monthly soil moisture in May extracted from the integrated soil moisture

data base of the European Space Agency- Climate Change Initiative (ESA-CCI).

(Source: http://www.esa-soilmoisture-cci.org/)

Soil Moisture …

satellite-based climate data

https://www.ncdc.noaa.gov/cdr

http://cci.esa.int/

• All data are available at no charge

• Mostly in netcdf-format following the

CF-standard

Image Processing Software

ERDAS Imagine

ENVI

ArcGIS

QGIS

GRASS GIS

ERDAS IMAGINE allows an unlimited number of layers/ bands ofdata to be used for one classification.

Usual practice is to reduce dimensionality of the data as muchas possible as unnecessary data tend to consume disk spacethereby slowing down the processing.

ERDAS Imagine

ERDAS Imagine- Supervised Classification

To perform supervised classification using ERDAS IMAGINE, open the imagein a viewer.

Select training signatures using the AOI tool [ AOI>Tools]

Select Signature Editor using the Classifier button. Select signatures representing each land cover classin the viewer. Use the Create Polygon AOI button from the AOI tools. After selecting a polygonal area,double click when finished. Three or more signatures need to be collected for each land cover typeclassified. Once this procedure is complete, save the signature file.

Use the ‘Classifier’ button from menu and go for ‘Supervised Classification’

Select the satellite imagery and enter in the ‘Input Raster File’. Similarly, load the file created using thesignature editor in the box showing ‘Input Signature File’. Enter a new file name for the classifiedimage. Press OK.

This procedure can be followed for performing supervised classifications like maximum likelihood,minimum distance to means, etc

ERDAS Imagine

Thematic map obtained after performing supervised classification in ERDAS IMAGINE using methods of

mahalanobis (b)minimum distance to means and (c) maximum likelihood classification (d) Signature

file used

(a) (b) (c)

(d)

ENVI

ENVI uses a generalized raster data format to use nearly anyimage file including those which contain their own embeddedheader information.

Generalized raster information is stored in either bandsequential (BSQ), band interleaved by pixel (BIP) or bandinterleaved by line (BIL) format.

ENVI…

Main, scroll and zoom windows of ENVIshowing image displayed[ Source: ENVI version 3 Tutorial]

ENVI- Image Mosaicking

Mosaicking refers to combining multiple images into a single composite image.

The software allows creating and displaying mosaics without the creation of largefiles.

Most of the mosaics require contrast stretching and histogram matching in order tominimize the image differences in the resulting output mosaic.

ArcGIS…

ArcGIS Desktop is scalable to meet the needs of many types of users. It is available atthree functional levels.

ArcView: It is the desktop version of ArcGIS which is the most popular of the GISsoftware programs

ArcEditor: This includes all the functionalities of ArcGIS which include the ability toedit features in a multiuser geodatabase

ArcInfo: This is Esri’s professional GIS software which includes functions of ArcGIS andArcEditor

ArcGIS…

MODIS Global Tool

Global sub-setting and Visualization Tool

https://modis.ornl.gov/data.html

https://modis.ornl.gov/

To work with MODIS Vegetation Index time series data using the ORNL DAAC site for globalsubset data extractions

Extract the 2007 annual time series profiles of 4 distinct land cover types. This 1-year datasetconsists of 23 MODIS NDVI and EVI data, at 16-day composite intervals, and 250 m spatialresolution.

This data will be used to generate temporal (seasonal and phenology) profiles of the landsurface.

The goals of this exercise will be to:• Understand some basic concepts in time series analysis with remote sensing• Derive seasonal profiles of some land cover types• Derive some phenology metrics for various land cover types

OBJECTIVES for this lab

https://daac.ornl.gov/

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