space applications centre, isro ahmedabad-380015 · variation of target reflectance) which directly...
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SATELLITE BASED HYDROLOGICAL
APPLICATIONS AND MODELING
Space Applications Centre, ISROAhmedabad-380015
India as seen from RISAT-1 (blue colour shows
mapped water bodies)
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Lecture Notes on training
SATELLITE BASED HYDROLOGICAL
APPLICATIONS AND MODELING
Organized By
Earth-ecosystems Research and Training Division
VEDAS Research Group (EPSA)
Space Applications Centre (ISRO)
Ahmedabad-380058
(08-11 August, 2017)
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CONTENTS
1. BASICS OF REMOTE SENSING AND APPLICATIONS
R P Singh
2. REMOTE SENSING FOR HYDROLOGICAL APPLICATIONS
R P Singh and P K Gupta
3. SATELLITE ALTIMETRY OVER LAND
S Chander
4. HYDROLOGICAL MODELLING AND REMOTE SENSING
P K Gupta
5. EVAPOTRANSPIRATION: TOOLS AND TECHNIQUES
R Pradhan
6. WATER QUALITY MONITORING FROM SPACE
A Gujrati
7. DIGITAL IMAGE PROCESSING
V B Jha
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BASICS OF REMOTE SENSING AND APPLICATIONS
R. P. SINGH
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Remote sensing technique is being currently used for providing solution to many resource
management issues as well as carry out scientific investigations to explore newer dimensions in
field of global carbon cycle, water cycle, improved weather prediction and planetary studies.
Satellite observations in different electromagnetic regions allow detection of various geophysical
parameters of the earth and planetary environment. This article reviews basic concepts involved
in remote sensing as well discusses various earth observation applications in India
Key Words: Remote Sensing, Spectral Signatures, Synthetic Aperture Radar, Passive
Microwave Radiometer, Scatterometry.
1.0 Introduction
Remote Sensing usually refers to the technology
of acquiring information about the earth's surface
(land and ocean) and atmosphere using sensors
onboard airborne (aircraft, balloons) or space
borne (satellites, space shuttles) platforms. In
remote sensing, the sensors are not in direct
contact with the objects or events being observed.
The electromagnetic radiation is normally used as
an information carrier in remote sensing.
Electromagnetic radiation is a self-propagating
wave in space with electric and magnetic
components. These components oscillate at right
angles to each other and to the direction of
propagation. Every object reflects/scatters a
portion of the electromagnetic energy incident on
it depending upon its physical properties. In
addition, objects emit radiation depending on
their temperature and emissivity. If we study the
reflectance/emittance of any object at different
wavelengths, we get a reflectance/emittance
pattern which is characteristic of that object.
Visual perception of objects is the best example
of remote sensing. We see an object by the light
reflected from the object falling on the human
eye. Modern remote sensing is an extension of
this natural phenomenon. However, apart from
visible light, the electromagnetic radiation
extending from the ultraviolet to the far infrared
(IR) and the microwave regions are also used for
remote sensing of the earth resources.
Remote sensing employs passive and/or active
sensors. Passive sensors are those, which sense
natural radiations, either emitted or reflected
from the earth. On the other hand, sensors, which
produce their own electromagnetic radiation, are
called active sensors (LIDAR, SAR). Remote
sensing can also be broadly classified as optical,
thermal and microwave. In optical remote
sensing, sensors detect solar radiation in the
visible and near infrared wavelength regions,
reflected or scattered from the earth, forming
images resembling photographs taken by a
camera located up high in the space. Thermal
remote sensing deals with detection of emitted
thermal radiation (generally in 3- 15 um spectral
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range). This information is used to estimate,
temperature, humidity, cloud properties, thermal
inertia, surface mineral composition etc.
Microwave remote sensing is done by observing
passive emission or backscattered signal in 1-200
GHz spectral range.
Figure 1. Remote sensing in which solar radiation reflected from different surface features are observed by
satellite sensor.
2.0 Spectral Signature
Different land cover features, such as water, soil,
vegetation, cloud and snow reflect visible and
infrared light in different ways. The interpretation
of optical images requires the knowledge of the
spectral reflectance signatures of the various
materials (natural or man-made) covering the
surface of the earth. Any set of observable
characteristics (such as wavelength wise
variation of target reflectance) which directly or
indirectly leads to the identification of an object
and/or its condition is termed as signature.
Spatial, spectral and temporal variations are
important characteristics of target, which is used
for discrimination. Spectral signature of
vegetation is uniquely characterized by
absorption in the blue and red bands due to
chlorophyll. Generally leaf pigments in visible
region, cell structure in near infrared region
(NIR) and water content in short wave infra red
(SWIR) region are the dominant controlling
factor in leaf/canopy spectra. Infrared sensors
which measure the thermal infrared radiation
emitted from the earth help in estimation of land
or sea surface temperature. Figure 2. Wavelength
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wise distribution of reflectance (spectral
signature) of different earth features
superimposed by vertical lines showing different
bands of MOS-B sensor.
Satellite sensor measures radiance which is the
radiant flux per unit solid angle leaving an
extended source in a giving direction per unit
projected source area in that direction. Unit of the
radiance is watts per meter square per micron, per
steradian (Wm-²um-1sr-¹). Observed radiances are
converted into reflectance which is the fractional
part of the incident radiation that is reflected by
the surface. Spectral reflectance is the reflectance
measured within a specific wavelength interval.
The reflection from a surface, which follows
Snell’s Law of reflection (angle of incidence =
angle of reflection, both measured from the
surface normal) is called specular reflection.
Here, the direction of the outgoing or reflected
ray is completely determined by the incoming
direction. If the angular distribution of the
reflected ray varies with the surface property and
does not follow Snell’s law then such reflection
is said to be diffuse. The reflection from a
Lambertian surface (whose intensity varies as
cosine of the angle measured from the normal to
the surface) is diffuse in nature. Bidirectional
Reflectance Distribution Function (BRDF)
describes the directional dependence of reflected
optical radiation. It characterizes the radiance
reflected into a specific view direction as a result
of the radiant flux incident upon a surface.
In optical remote sensing of the earth, the optical
sensors are looking through a layer of atmosphere
lying in between the sensors and the Earth's
surface being observed. Hence, it is essential to
understand the effects of atmosphere on the
electromagnetic radiation traveling from the
Earth to the sensor through the atmosphere. The
atmospheric constituents cause wavelength
dependent absorption and scattering of radiation.
These effects degrade the quality of images.
Some of the atmospheric effects can be corrected
before the images are subjected to further
analysis and interpretation. Absorption in the
atmosphere mostly occur when the EM radiation
interact with the atmospheric atoms or molecules
so as to excite the molecule to a higher energy
level. In this process, the incident radiation
transfers all or part of its energy to molecule.
A consequence of atmospheric absorption is that
certain wavelength bands in the electromagnetic
spectrum are strongly absorbed and effectively
blocked by the atmosphere. The wavelength
regions in the electromagnetic spectrum usable
for remote sensing are determined by their ability
to penetrate the atmosphere. These regions are
known as atmospheric transmission windows.
Remote sensing systems are often designed to
operate within one or more of the atmospheric
windows. Atmospheric molecules are
responsible for selective absorption in different
wavelength.
Even in the regions of atmospheric windows, the
scattering by the atmospheric molecules and
aerosols produces spatial redistribution of energy.
The scattered / diffused radiance entering the
field of view of a remote sensor, other than that
from the target of interest, is called path radiance.
Scattering is a multiple reflection of
electromagnetic waves by particles or surfaces.
Energy is not lost to the medium but the radiation
is scattered out to other directions, thereby
reducing the amount of radiation in the original
direction. The sum total of absorption and
scattering is known as attenuation. Broadly there
are three type of scattering process in atmosphere.
1) Molecular or Rayleigh Scattering - This occurs
when the particles causing the scattering are
smaller in size than the wavelengths of radiation
in contact with them. This type of scattering is
therefore wavelength dependent. As the
wavelength decreases, the amount of scattering
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increases. It is the Rayleigh scattering that is
responsible for the sky appearing blue.
2) Particle or Mie Scattering - Mie scattering is
caused by pollen, dust, smoke, water droplets,
and other particles in the lower portion of the
atmosphere. It occurs when the size of particles
causing the scattering are similar or slightly
larger than the wavelengths of radiation in
contact with them. Turbid appearance of sky is
due to Mie scattering caused by suspended
aerosols.
3) Non-selective Scattering - It occurs in the
lower portion of the atmosphere when the
particles are much larger than the incident
radiation. This type of scattering is not
wavelength dependent. Scattering of optical light
in cloud is associated with non selective
scattering.
Figure 3 Atmospheric transmittance due to absorption of different atmospheric molecules.
3.0 Thermal Remote Sensing
Any object above absolute zero temperature
emits electromagnetic radiation. Thus the objects
we see around, including ourselves are thermal
radiators. An ideal substance is called blackbody
which absorbs the entire radiant energy incident
on it and emits radiant energy at the maximum
possible rate per unit area at each wavelength for
any given temperature. No actual substance is a
true blackbody, although some substances
approach its properties. The radiance being
emitted by a blackbody at given wavelength ( )
and Temperature T is given by Planck’s
Radiation Law.
Mλ = 2лhc2
λ5 [exp(hc/λkT)-1]
Where k Boltzmann’s constant, h is Planck’s
constant, c is velocity of light, and T is the
absolute temperature in Kelvin.
A black body is an ideal surface such that
1. It absorbs all incident radiation
regardless of the wavelength or direction of the
incident radiation;
2. For a given temperature and wavelength,
nobody can emit more energy than a black body;
3. Emission from a black body is
independent of direction, that is, the black body
is a diffuse emitter.
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The total emission within all the wavelengths
Mtotal can be found out by integrating the Planck’s
equation from λ = 0 to λ = ∞ and works out to be
Mtotal = σT4 Wm-2
Where σ is Stefan –Boltzman constant.
Another useful expression in thermal remote
sensing is Wien’s Displacement Law, which
gives the wavelength λmax at which the exitance is
maximum and is related to the temperature as
λmax T = constant
if λmax is expressed in micrometer and T in 0K,
then the constant is 2897.
The heat energy is converted to radiation at a
maximum rate as per Planck’s law. However, a
real surface does not emit at this maximum rate.
The emission from a real surface is characterized
with respect to a black body. In order to do so, a
term called emissivity is used which compares,
the ‘radiating capability’ of a surface to that of a
black body (an ideal radiator).
Figure 4. The Planck’s radiation distribution of blackbody at different temperatures
Emissivity () defined as the ratio of radiant
exitance of the material of interest (Mm) to the
radiant exitance of a black body (Mb) at the same
temperature.
= Mm / Mb
For a black body, = 1, for all wavelengths. For
a gray body < 1, and can vary with wavelength
and direction.
Brightness temperature (TB) of the surface is the
temperature of a blackbody surface which, when
placed in front of the receiver aperture, would
produce the same received flux within the
spectral band of receiver. Brightness temperature
(TB) is defined a
TB = T
where is emissivity of the target and the T is
absolute physical temperature.
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Figure 5. Observations of thermal emission on Martian surface showing (a) radiance in
W/cm2/um/sr, (b) Surface temperature in K and (c) surface Emissivity estimated from THEMIS
observations from band 3 (7.93 um).
4.0 Microwave Remote Sensing
Microwave remote sensing is highly useful as it
provides the observations of earth‘s surface
regardless of day/night and atmospheric
conditions. Microwave remote sensing makes use
frequency range from 1 to 300 GHz of the
spectrum. The electromagnetic waves in this
range are relatively less affected by the
atmosphere and hence provide useful data in
overcast or turbid environment. The active
sensors in microwave consist of transmitter and
receiver. Scatterometers, Synthetic Aperture
Radars (SAR) and altimeters are some of the
examples of active microwave sensors. The
transmitted energy is reflected and /or scattered
from the target. The signal with a propagation
delay is received and processed to deduce and
understand the target properties. Radar equation
expresses the fundamental relationship between
radar parameters, target characteristics and the
received signal. For monostatic radars, it is given
by
𝑃𝑟 =𝜆2
(4𝜋)3∫𝑃𝑡𝐺
2𝜎0
𝑅4𝑑𝐴
Where Pr is the average power returned to the
radar antenna from the extended target, Pt is the
power transmitted by radar, G is the gain of the
antenna, R is the distance of the antenna from the
target, λ is the wavelength of the radar, o is the
radar scattering coefficient of the target. The
integration is over the illuminated area A. The
backscattering coefficient is defined as the ratio
of the energy received by the sensor, over the
energy that the sensor would have received if the
surface scattered the energy incident upon it in
isotropic fashion. It is represented as Decibels
(dB).
In the active microwave remote sensing,
information about the object’s physical structure
and electrical property is retrieved by analyzing
the backscattering The microwave signature of
the object are governed by sensor parameters
(frequency, polarization, incidence angle) and
physical (surface roughness, feature orientation)
and electrical (dielectric constant) property of the
target. A given surface may appear very rough at
higher frequency compared to lower frequency.
Generally backscattering coefficient increases
with increasing frequency. In addition, the signal
penetration depth increases with wavelength in
microwave region. Use of multi- frequency
allows distinction between roughness types. The
backscattering also depends on the polarization of
(a) (b) (c)
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the incident wave. A vegetation canopy
consisting of short vertical scatter over a rough
surface can be considered as short vertical
dipoles. In such case, vertically polarized incident
wave interact strongly with canopy. The multiple
scattering and volume scattering from a complex
surface, such as forest cause depolarization. The
radar backscattering coefficient from a terrain is
strongly dependent on angle of incidence. The
angular dependency of backscattering coefficient
is primarily due to surface roughness. The surface
water extent during flood is detectable on radar
backscatter image due to high contrast between
smooth water and rough land surface.
SAR interferometry is an extremely powerful tool
for mapping the Earth’s land, ice and even the sea
surface topography. The basic idea is that the
position of a point on the Earth’s surface can be
reconstructed from the phase difference
(interferogram) between two complex-valued
SAR images achieved by coherently processing
the backscattered signals (phase) recorded by the
two antennas.
A Passive sensor consists of only a receiver.
Emitted radiation from manmade or natural
targets is received and processed by the
radiometer to infer the target properties. Raleigh-
Jeans law describe the spectral radiance Mλ(T)
from a black body in microwave at a given
temperature through classical arguments. At
microwave frequencies, Planck’s equation gets
approximated to Raleigh-Jeans law as
Mλ = 2лckT
λ4
where K is Boltzmann constant, is emissivity of
the body at absolute temperature T and
wavelength λ. Emission from a body in
microwave region at particular wavelength is
proportional to the brightness temperature.
Brightness temperature observed over a region
which is product of physical temperature and
surface emissivity is used to infer many
geophysical properties.
Figure 6. Spatial variability of different ecosystem as observed by IRS-LISS-III data
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Figure 7. Supervised classification of different land cover classes in parts of Madhya Pradesh, India.
5.0 Remote Sensing Applications
The output of a remote sensing system is
usually an image representing the scene being
observed. Image analysis and modeling is used
in order to extract useful information from the
image. Remote sensing images are normally in
the form of digital images. There are many
image analysis techniques (image
transformation, enhancements, pattern
recognition, fusion, merging etc) available for
analysis the data. Suitable techniques are
adopted for a given area and land cover
characteristics, depending on the requirements
of the specific problem.
Identification of terrain categories is done by
digital processing of data acquired by
multispectral scanners. Classification is a process
of assigning individual pixels of an image to
categories, generally on the basis of spectral
seperability analysis. Classification is generally
carried out by supervised or unsupervised
technique. Supervised Classification is digital-
information extraction technique in which the
operator provides training-site information that
the computer uses to assign pixel to categories. It
generates the decision rule and assigns the classes
accordingly, Unsupervised classification is
digital information extraction technique in which
the computer assigns pixels to categories through
clustering techniques without a priori field
information of classes.
Remote sensing help mapping, monitoring and
management of various resources like
agriculture, forestry, geology, wetlands, ocean
etc. It further enables monitoring of environment
and thereby helping in conservation. In the last
four decades it has grown as a major tool for
collecting information on almost every aspect on
the earth. Some of the important projects carried
out in the country include Groundwater Prospects
Mapping under Drinking Water Mission,
Forecasting Agricultural output using Space,
Agrometeorology and Land based observations
(FASAL), Forest Cover/Type Mapping, Wetland
Mapping, Biodiversity Characterization, Snow &
Glacier Studies, Land Use/Cover mapping,
Coastal Studies, Coral and Mangroves Studies,
Wasteland Mapping etc. The information
generated by large number of projects is used by
various departments, industries and others for
different purposes like development planning,
monitoring, conservation etc.
Forest Crop WL/Fallow Water SandForest Crop WL/Fallow Water Sand
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REMOTE SENSING FOR HYDROLOGICAL APPLICATIONS
R P SINGH and P K GUPTA
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Although 70 percent of the Earth’s surface is covered with water, the amount of fresh water
available on land surfaces is a tiny fraction of the total. Less than 1 percent is fresh water, present
in the form of groundwater, soil moisture and, river/lakes on the land surface for domestic,
agriculture, aquatic and other purposes. Water demand is growing by twice of the population
growth. One third of the population would be under water stress by 2025 and 2/3 of population
would be under the water scarcity by 2050. Therefore, there is a need for global, satellite-based
observations of terrestrial surface waters to diagnose where is water stored in the Earth’s land
surfaces, and how does this storage vary in space and time? Satellite remote sensing provides a
means of observing repetitive and continuous coverage of the Earth surface and atmosphere with
high spatial resolutions. Many earth observation satellites are in use; polar satellites which turn
around the poles, and geostationary satellites which have a fixed position with respect to the earth
surface. A series of satellites are being used for observing hydrological state variables for the
assessment and management of water resources. Such variables are rainfall from
microwave/thermal data, river/reservoir/lakes water levels from altimeter and scatterometer,
surface soil moisture from active/passive microwave data, flood inundation areas from
microwave, wetland areas using optical data, irrigated areas from optical data, groundwater
prospects using optical/microwave data etc. Different hydrological variables namely rainfall,
runoff, wetlands, groundwater and soil moisture has been discussed.
Key Words: Satellite Remote Sensing, Hydrology, Rainfall, Runoff, Soil Moisture, Irrigation
class, Water level, Groundwater, Microwave, Altimeter, Scatterometry.
1.0 Introduction
The average annual rainfall including snowfall in
India is 4000 Billion Cubic Meters (Rakesh
Kumar 2005) but availability of per capita fresh
water is major concern as the population continue
to increase in future (R K Mall 2006). Water
security and management requires information
ranging from regular inventory of surface water
bodies to assessment of rainfall, soil moisture,
evapotranspiration, ground water and snow melt
runoff (V.V. Rao 2013). Hydrological drought
and flood forecast during extreme weather events
become important for planning and water
resource management.
Modeling different components of water cycle
requires measurement of water which can exist in
all the three phases of matter i.e. solid, liquid and
gas (R.O. Green 2006). Satellite provides an
important platform from where measurements
can be done in any part of electromagnetic
spectrum suitable to detect different phases of
water over a large region not feasible through
sparse network of surface-based instruments.
Instruments on Earth Observation systems have
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been extensively used to measure hydrologic and
hydraulic variables such as water spread area,
elevation of water surface (h), its slope (∂h/∂x)
and temporal changes (∂h/∂t) (E A Douglas,
2007). Seasonal monitoring of reservoir spread,
conjunctive water uses and water management
practices associated to transplanting operations
helps in irrigation scheduling (P K Gupta 2009
and 2010). Satellite observations are regularly
used to generate snow cover map which help in
snow melt runoff estimation (S.K. Jain 2010).
Satellite data has been used to study the changes
in the extent of Himalayan glaciers inventory and
their monitoring in terms of whether they are
retreating or being stable over the time is another
important contribution of space technology
towards understanding the climate change signals
(I.M. Bahuguna 2014). To provide safe drinking
water to hundred thousands of villages, ground
water prospect maps were generated using
satellite data conjunctively with ground
information showing probable regions where
wells can be drilled (R.K. Jaiswal 2003).
Groundwater prospects maps at 1:50,000 scale
using IRS-1C/ 1D LISS-III data for entire country
was generated under the project titled "Rajiv
Gandhi National Drinking Water Mission".
Remote sensing data along with Geospatial
technology have helped planning of water
resources by the respective water management
boards.
Hydrological remote sensing is carried out using
measurements from various Indian satellite
platforms such as SARAL-Altika Mission
(R.M.Gairola 2015) (Inland Water level),
RISAT-1 SAR Mission (T. Misra 2013) (Surface
water spread, Soil Moisture), Resourcesat-1/2
Missions (M.R. Pandya 2007) (Snow cover,
Wetlands, Land use Land cover, Water quality),
Cartosat Missions (DEM), Kalpana, Megha
Tropiques and INSAT-3D Missions (Rainfall,
Solar Radiation etc.) (R. R. Navalgund 2010).
Global Missions such as Landsat Program,
Sentinel Program, Jason program,
SRTM/ASTER topography missions, MODIS
instruments on Earth Observation Terra and Aqua
Missions, GRACE Mission (S. Swenson 2008),
Soil Moisture and Ocean Salinity (SMOS) (Y.H.
Kerr 2001) Mission, Soil Moisture Active
Passive (SMAP) (D. Entekhabi 2010)Mission
and Tropical Rainfall Measuring Mission
(TRMM) etc. also provide valuable datasets to
model the water fluxes over India. Scientific
rationale of section of various hydrological
parameters using satellite data is discussed in
subsequent sections.
2.0 Physical Basis for Remote Sensing in
Hydrology
Remote sensing generally refers to the
technology of observations of earth using sensors
placed on aircraft or satellites platforms. These
sensors employ active as well passive sensing
system. Active systems have their own source of
illumination (Radar, Scatterometer, Altimeter)
whereas passive systems sense natural radiations,
either reflected or emitted from the earth. In
active remote sensing, generally instruments
(Radar, Scatterometer) measure back scattered
signals from target however Altimeters use the
time delay in propagation of the incident signal to
infer the topography (elevation, water level) of
the surface. Short pulse of electromagnetic wave
is transmitted and received by Radar from space
platform and range between the satellite and earth
surface is measured by precise orbit computation
and correcting atmospheric and geophysical
signals.
In the active microwave remote sensing,
information about the object’s physical structure
and electrical property is retrieved by analyzing
the backscattering signal. Microwave remote
sensing provides the observations of earth‘s
hydrological variables regardless of day/night
and atmospheric conditions. Water being a polar
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molecule has very high sensitivity in microwave
wavelengths due to orientation polarization
property. The electromagnetic radiation is used as
an information carrier in remote sensing.
Instruments operate in optical (Cameras, Spectro-
radiometer), infrared (Thermal radiometers) and
microwave (Radar, Altimeter, Scatterometer etc.)
wavelength ranges. Microwave remote sensing
provides the observations of earth‘s hydrological
cycle regardless of day/night and atmospheric
conditions. Water being a polar molecule has
very high sensitivity in microwave wavelengths
due to orientation polarization property. The
microwave signature of the object is governed by
sensor parameters (frequency, polarization,
incidence angle) and physical (surface roughness,
feature orientation) and electrical (dielectric
constant) property of the target (H.S. Srivastava
2009). The surface water extent is detectable on
radar backscatter image due to high contrast
between smooth water and rough land surface.
Radar operated in interferometry mode helps in
mapping the surface topography. Radar
interferometry works on the principle that the
position of a point on the Earth’s surface can be
reconstructed from the phase difference
(interferogram) between two complex-valued
SAR images achieved by coherently processing
the backscattered signals (phase) recorded by the
two antennas. Shuttle Radar Terrain Mapper
(SRTM) used interferometric technique to derive
global digital elevation model (DEM). DEM
information when coupled with other remote
sensing data and terrain models provide valuable
inputs in hydrology.
A Passive sensor consists of only a receiver.
Emitted radiation from manmade or natural
targets is received and processed by the
radiometer to infer the target properties. Any
object above absolute zero temperature emits
electromagnetic radiation. Thus the objects we
see around, including ourselves are thermal
radiators. An ideal substance is called blackbody
which absorbs the entire radiant energy incident
on it and emits radiant energy at the maximum
possible rate per unit area at each wavelength for
any given temperature. Brightness temperature
observed over a region which is product of
physical temperature and surface emissivity is
used to infer many geophysical properties.
Brightness temperature are lower when measured
over moist surface as compared to brightness
temperature observed over dry surface.
Modeling of multi frequency vertical as well as
horizontal polarization brightness temperature
(from SSMI, AMSR-E type of radiometers) with
respect to varying soil moisture content and other
surface characteristics is carried out to retrieve
the surface soil moisture (R.P.Singh 2005).
3. Techniques for Estimation of Hydrological
variables
3.1 Rainfall
Rainfall can be estimated using combination of
optical and thermal infrared data and also using
the microwave data.
3.1.1 Optical-infrared approach
Existing techniques have concentrated on using
visible and thermal infrared channels for cloud-
top reflectance and temperature. In the visible
band, it is possible to determine the type of cloud
cover based on the textural characteristics of the
cloud within the satellite image. The physical
basis for this method is that high cloud top
brightness in the visible bands means that there is
a greater probability of rain due to large cloud
thickness. Similarly, low cloud top temperature in
the thermal band implies high cloud tops and also
implies a correspondingly large cloud thickness.
Therefore, precipitating clouds can be
distinguished from others on the basis of their
brightness or infrared temperature characteristics.
A strong quantitative relationship exists between
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rainfall amount and calibrated cold cloud
duration derived from thermal infrared data.
Precipitation intensity is directly proportional to
the area of the upper surface of the cloud at
temperature of less than –15 0C. Decreases in
brightness temperature is directly correlated to
cumulative rainfall amounts.
Satellite data: Kalpana-1, INSAT-3A, MODIS,
ASTER, NOAA etc.
3.1.2 Microwave approach
Passive microwave remote sensing and active
microwave remote sensing are being used to
estimate rainfall. This is largely because clouds
are relatively transparent at microwave
wavelengths but hydrometeors are not, resulting
in a relatively direct link between hydrometeors
and attenuation of upwelling microwave
radiation. Radar measurements of power of
electromagnetic waves backscattered by
raindrops are directly related to a physical
quantity called reflectivity. Estimation of rainfall
amounts involves using reflectivity via a
reflectivity-rainfall relationship. How is the
relationship selected, there are two approaches. In
the first approach, which we will term the drop
size distribution approach, relations are derived
from raindrop size distribution observations
made at the surface. The second approach is
similar in relying on statistical estimation
procedures to relate measured values of radar
reflectivity to rainfall rate.
Satellite data: SSM/I, TMI, Tropical Rainfall
Measurement Mission Precipitation Radar
Satellite Products: NOAA CPC, JAXA, TRMM,
METEOSAT, INSAT etc.
3.2 Surface Runoff
What is the global distribution of runoff water
delivered to the oceans and what is its inter-
seasonal and inter-annual variability? Runoff is
an integrated effect of rainfall, topography, soil
and land cover conditions. Efforts are being made
to derive the runoff directly using remote sensing
technique such as combined use of altimeters and
microwave data. Presently remote sensing
technology has been used to derive very crucial
inputs to runoff modeling. Remote sensing
derived precipitation, topography, LULC and
land surface characteristics are used for the
computation of runoff using simple rainfall-
runoff simulation approach;
3.2.1 Altimetry
River water levels estimated using altimeter data
(SARAL-ALTIKA, Jason-2) can be used for the
validation of the model results. Water level is
important hydrological quantity required to
budget the fresh water availability. Satellite
altimetry is important active remote sensing
technique for systematic monitoring of water
levels of reservoirs, lakes and rivers. Satellite
altimetry technique was originally started for
assessment of ocean topography but recent
instruments on JASON, SARAL Altika, Jason-3,
Sentinel-3 and future missions such as Surface
Water and Ocean Topography (SWOT) mission
are designed to study the inland water bodies also.
Radars onboard satellite emit pulses towards
nadir and receive the echo by water surface. The
half time span for pulse reflected back to mission
corresponds to distance ( 𝜌 ) between satellite and
earth surface. The height H of the reflecting water
body with reference to geodetic reference is given
as
𝐻 = 𝑎s − 𝜌 + 𝐶iono + 𝐶dry + 𝐶wst + 𝐶st + 𝐶pt (4)
Where as is the satellite altitude with reference to
reference ellipsoid. Other terms reference to
corrections related with delayed propagation
through the atmosphere (Cdry and Cwet), the
interaction with ionosphere (Ciono) and solid earth
tides (Cst and Cpt ).
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Radar altimeter onboard ISRO/CNES SARAL –
AltiKa mission provides important information
of water level for rivers and large reservoirs at 35-
day repeat interval (P.K.Gupta 2015) (A.K.
Dubey 2015) (F. Papa 2012). Observations of
water level in Ukai Reservoir from 2013 to 2016
shows lowest water level condition in 2016
indicating hydrological drought situation.
Analysis showed that amount of water volume
availability was found to be less by19.8% in the
current year (April 2016) as compared to last year
(April2015) whereas it is 88.5% less in
comparison to 2014 for the same time frame over
the Ukai Reservoir. A flood wave of 5.93 m,
which is highest since inception of SARAL-
Altika (Feb 2013) between two passes (4th May to
9th June 2016) over Brahmaputra was estimated.
Fig. Water Level Retrieval using Satellite
Altimetry over Ukai reservoir, India
Satellite altimetry (A. K. Dubey 2014) has been
used to study the river stage and its discharge
using rating curve relationship. Papa et al. (2010)
(F.Papa 2010) estimated monthly discharges
from Ganga and Brahmaputra rivers using
TOPEX-Poseidon (T/P), ERS-2 and ENVISAT
satellite altimetry data. Biancamaria et al. (2011)
(S. Biancamaria 2011) also studied water levels
at upstream locations in India using T/P altimetry
data to forecast the water levels of Ganges and
Brahmaputra rivers. Frappart et al. (2005) (F.
Frappart 2005) have determined spatio temporal
variations of water volume in Negro River basin
using area variations from SAR data and changes
in water level from T/P altimetry data.
3.2.2 Scatterometery
A Scatterometer is an active microwave
instrument (radar) actively transmit
electromagnetic pulses to the Earth's surface and
measure the backscatter response/power of the
return pulse scattered back to the antenna.
Scatterometers average the detected returns from
a sequence of pulses, a process known as
postdetection integration often achieving ± 0.10
to 0.15 dB accuracy. calibrated to less than a few
tenths of a decibel; ample to capture inter-
seasonal diff. with 1 to 2 dB changes.
Satellite based remote sensing of river
hydrodynamics is important application in
hydrology. Basin level rainfall and associated soil
wetness influences the fluctuations in river water
levels through process of surface runoff in the
downstream (Wagner 1999). Fig. present water
level at gauging site Dhubri (downstream of
river) using OSCAT/SCATSAT-1 (high
resolution SIR datasets) data for the year 2013.
Fig. Scatterometer (blue line, every 15 days) and
altimeter (red line, every 35 days) estimated
water levels for Dhubri gauging site for the year
2013.
88
90
92
94
96
98
100
102
104
0 30 60 90 120 150 180
Wa
ter
leve
l fr
om
msl
, m
Julian Days
Multi-year water level fluctuation in Ukai reservoir using
SARAL-Altika
2013
2014
2015
2016
25
26
27
28
29
30
31
32
33
22-M
ay
26
-Ju
n
31
-Ju
l
04
-Se
p
09
-Oct
13-N
ov
18
-De
c
22
-Ja
n
Wat
er
leve
ls, m
Altimeter pass dates
Altimeter_scatterometer Altimeter_linear fit
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3.3 Wetlands
Wetland identification, mapping and analyses are
often done by means of remote sensing. Remote
sensing of wetlands may be studied using a range
of sensors such as panchromatic or colour-
infrared aerial sensors, broadband multi-spectral
to narrowband hyperspectral besides microwave
sensors. The mapping and inventory is the first
step in the conservation and management of
wetlands. Several studies appeared from time-to-
time using fine resolution (5 to 100 m) multi-
spectral satellite data like Landsat-MSS (Multi
Spectral Satellites), Thematic Mapper, Enhanced
TM; SPOT; Linear Imaging Self Scanning (LISS-
I, LISS-II, LISS-III) on IRS series of satellites;
AVNIR on ADEOS satellite for mapping and
classification of wetlands. Data from Advanced
Very High Resolution Radiometer (AVHRR) on-
board NOAA is currently available operational
representative coarse resolution system besides
Moderate Resolution Imaging Spectro-
radiometer (MODIS) on-board Terra and Aqua
satellites was used for global wetland mapping
and estimation of aerial extent. The importance
of this has been recognized over the last forty
years and various governmental agencies have
laid emphasis on mapping the spatial
extents/distribution of wetlands to feed towards
the functional characterization like hydrologic,
biogeochemical and maintenance of habitat and
food webs. Space applications Centre has done
National level wetland mapping comprising of
natural and manmade water bodies covering
inland and coastal areas using RESOURESAT-1
LISS-III data at 1:50000 scales. A total of 201503
large wetlands (> 2.25 ha) and 555557 small
wetlands (< 2.25 ha) covering an area of 15.26
Mha have been mapped and classified.
Synthetic Aperture Radar (SAR) which is an
active sensor onboard Microwave Satellites
(RISAT-1; Mishra et al., 2013), ERS, Radarsat
and Envisat) has many characteristics that make
it useful in water spread (land-water demarcation)
mapping and monitoring activities over time
because of its ability to image even during severe
weather conditions and day/night acquisition
capability (Lee K S, 2003; Brisco et al., 2008).
But, estimation of flood extent varies with the
type of polarization since the intensity of the
radar-backscattered signal depends upon the
wavelength, incident angle and polarization of
the signal and the geometric
(structure/composition) and electrical (dielectric
constant) properties of the ground features it
impends upon. Land-water demarcation using
HH polarization data has better capability as
compared to HV polarization. Fig. shows water
spread area of Kamleshwar Dam in Gir forest
region of Gujarat India using multi-date RISAT-
1 MRS datasets for 2015.
Fig. Water spread using multi-date RISAT-1
dataset over Kamleshwar Dam in Gir forest
regions of Gujarat. Different colors show water
spread change from April to October 2015.
3.4 Groundwater
Groundwater potential zones and its short term
depletion/surplus areas can be identified using the
remote sensing technology.
3.4.1 Groundwater potential zone
April
July
September
October
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The occurrence and movement of groundwater in
an area is mainly controlled by secondary
porosity caused by fracturing of the underlying
rocks. Lithology, geomorphology, fractures,
lineament, soil, landuse, drainage and slope all
play an important role in groundwater
replenishment. High relief, steep slopes and high
drainage density impart higher runoff causing
less infiltration, while low relief, gentle slope and
low drainage density result in low runoff and
comparatively high infiltration. Surface water
bodies, such as rivers, ponds and canals, can act
as recharge zones enhancing the groundwater
potential in the vicinity. Therefore, identification
delineation and quantization of surface
hydrological features are vital for groundwater
potential zones investigation.
Remote sensing technology has been widely used
for groundwater resource management. Satellite
data (LISS-III, AWiFS, RISAT, CARTO_DEM)
are useful for extracting various hydro-geological
thematic maps required for groundwater
assessment. These thematic maps are assigned
suitable weights and different rankings to the
individual classes within each thematic map. The
integrated thematic maps can be used to compute
the Groundwater Potential Index (GWPI). Very
poor GWPI zones show low water yields (0.5- 1.5
lps) while excellent GWPI zones show high water
yields (5–7 lps). The results can be subsequently
cross-checked with resistivity survey. Space
applications centre has carried out hydro-
geological studies in varied geological setups
under the Rajiv Gandhi drinking water mission.
3.4.2 Groundwater from gravity anomaly
Satellite-based observational technique allows us
to directly monitor regional changes in stored
water. It allows us to produce an up-to-date
quantitative estimate of the temporal and spatial
variability of groundwater in the region, which is
a first step towards management of sustainable
water resources for the populated places on the
globe. The Gravity Recovery and Climate
Experiment (GRACE) satellite mission,
measures temporal variations in the gravity field
due to mass fluctuations which GRACE record
remarkably from the space. Withdrawals for
irrigation and other uses are depleting the
groundwater reserves of Rajasthan, Punjab and
Haryana at a rate of 4.0 ± 1.0 cm yr-1 equivalent
height of water, or 17.7 ± 4.5 km3 yr-1, recorded
by GRACE (Tiwari 2009).
3.5 Soil moisture
Hydrological variable soil moisture at the
regional scales is very dynamic and changes
conditions rapidly. Satellite based sensors offer
the advantages of large area mapping and long-
term repetitive coverage. Active and passive
remote sensing techniques are used for the
estimation of soil moisture.
3.5.1 Active remote sensing
Near surface (0-5 cm) soil moisture can be
estimated at microwave frequencies. The most
important feature is the large contrast in
emissivity between water and land. This is due to
the large dielectric constant of water (80) while
that of most dry minerals or soils is <5 and for
mixture of soil water is 30-40 which constitute a
base for observing emissivity and estimating soil
moisture through remote sensing at microwave
frequencies. It has been found that the longer
wavelengths are better for increased sampling
depth and reduced effects of noise factors such as
vegetation and surface roughness. The range of
emissivity variation to be expected is from 0.95
for dry soils to less than 0.6 for smooth wet soils.
The main factors which affect the accuracy are
vegetation cover (25%) and roughness (15%).
Example RADARSAT (Radio Detection and
Ranging Satellite), Radar Imaging satellite
(RISAT) etc.
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3.5.2 Passive remote sensing
Passive microwave methods measure the natural
thermal emission of the land surface using very
sensitive detectors, the intensity of this emission
is generally expressed as a brightness
temperature. The brightness temperature of the
surface is related to its emissivity, physical
temperature and contributions from the
intervening atmosphere. Microwave emission is
mainly influenced by the dielectric and roughness
properties of the surface. As the fractional
amount of water increases, the emissivity
decreases and the slope of the emissivity between
two or more frequencies can be used to determine
the surface wetness condition. Examples Special
Sensor Microwave/Imager (SSM/I), Advanced
Microwave Scanning Radiometer (AMSR), Soil
Moisture and Ocean Salinity (SMOS) etc.
A statistical approach as well as a forward model
based inversion technique is mainly used in the
estimation of soil moisture. Statistical techniques
rely on regression analysis between measured
backscattering coefficient/brightness temperature
and surface soil moisture. The slope and intercept
of the regression line are dependent on land cover
variables, which can be estimated from ancillary
data. In the forward model based inversion
technique, a model is used to simulate remotely
sensed output signal (backscattering coeff.,
brightness temperature) on the basis of input land
surface characteristics (e.g. soil moisture, soil
texture, roughness, vegetation water content).
Inversion methods based on iterative
minimization between forward model simulation
and observation are used to estimate the soil
moisture. The statistical approaches are simpler
to use in comparison to the physical approaches
but require a region specific coefficient as
microwave scattering/emissions are determined
by the soil (texture, roughness) and vegetation
characteristics. Based on the sensor involved in
the soil moisture estimation, remote sensing
techniques can be categorized in broad three
categories (1) Radar Based Technique, (2)
Scatterometer based Techniques and (3) Passive
Microwave Radiometer based Techniques.
3.5.2.1 Shifting Irrigation Practices
Over exploitation of ground water in the recent
past is well known fact in the Punjab and Haryana
region and has been reported by several studies
using the satellite based gravity anomaly from
GRACE mission and also using observed data.
This decline in groundwater has enforced
“Punajb Sub-soil water act in 2009” by the
Punjab Govt. This water act has changed
irrigation practices in the region. Passive
microwave radiometer (AMSR-E) soil moisture
data from 15 March to 30 September was
analyzed during 2002 to 2011 period.
It has been found that there is gradual shift in the
early soil wetness pattern and associated change
in the irrigation practices. Shifting is delayed by
14 ± 1 days in central Punjab from 167 Julian day
to 181 Julian day from pre to post “water act”
period, respectively. Fig. presents the shifting
irrigation practices over Punjab and Haryana
regions. in Julian days for pre and post water act
time periods.
Fig. presents the shifting irrigation practices over
Punjab and Haryana regions. in Julian days for
pre and post water act time periods.
Legend
state_bnd_geo
ndvi_max2008_subset.tif
Value
High : 0.9
Low : 0.5
sm_maxdiff10_subset.tif
Value
High : 300
Low : 50
day_maxdiff10subset.tif
Value
High : 30
Low : 0
195
175
155
135
Julian Days
Pre “water Act”
Post “water Act”
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3.6 Conjunctive water uses
Improving the water use efficiency in irrigated
systems integrating satellite inputs is the thrust
area of research. Hence, conjunctive use of water
from different sources i.e., precipitation, canal
water, groundwater, surface water etc., is gaining
attention. Thenkabail et al. (2005) used near
continuous time series remote sensing moderate
resolution imaging spectrometer (MODIS) data
to map the irrigated areas of the Indo-Gangetic
region. Global irrigation mapping effort was
undertaken by Siebert et al. 2007. Multi-date high
temporal IRS-1D data have been used to identify
the various sources of irrigation such as surface
water, groundwater, wetland etc. by analyzing the
space time spectral curves over the Damodar
command area in West Bengal (P K Gupta 2009).
Fig. Conjunctive water use (irrigation
class/source) pattern (GW: groundwater, SW:
surface water) in Damodar command area using
multi-date IRS-1D data.
3.7 River morphologic features
Understanding of river and floodplain interaction
is very limited. Generally, physical scaled models
are used to investigate the interaction. These
physical models are unable to present the
morphological change and unsteady effect
accurately. This river dynamics (change in the
river morphological features) can be identified
using the microwave remote sensing techniques.
River models integrating the remote sensing
inputs will provide us depth of water and
duration. A decision rule based classifier was
developed to delineate the river morphological
features using the multi-date RISAT-1 MRS HH
polarized (gives better discrimination as
compared to VV polarization) data during July to
September 2012.
Fig. River morphological features delineated
using multi-date RISAT-1 MRS HH data over
Brahmaputra river reach nearby Guwahati.
4.0 Conclusions and Future Directions
Present trend in remote sensing of hydrology is to
develop methodologies for retrieval of various
hydro-meteorological parameters from satellite
data and assimilate the information in physically
based distributed hydrological models.
Integrating remote sensing derived inputs such as
DEM, rainfall, crop growth parameters (Leaf area
index), land use land cover, wetlands, soil
moisture, snow cover area, vegetation indices,
river network, cross sections, river water level
fluctuations, insolation, Albedo etc. into semi-
distributed and distributed hydrological models is
a challenge. As volume of input data and
computational requirements are enormous, use of
these models has not yet reached the operational
status in many developing countries. Future need
in water management is to develop and assess the
use of remote sensing methodologies, in
combination with in situ data and hydrological
Command boundary
Groundwater
Canal water
Wetland
Non crop
Water
Canal network
Turbulent Water
Calm WaterIsland Water
FloodplainInundation
Island Vegetation
Sand Bar
Others
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models. In the present scenario hydrological
research is oriented towards addressing the
particular process instead there is need to use
hydrological system approach to study the
integrated effect of hydrology and eco-system
processes for updating predicted hydrologic state
variables. Study scale needs to be up scaled from
basin to global nature to account for the
atmospheric phenomenon and hydro-
teleconnections. Development and testing of
remote sensing algorithms and generation of
environmental forcing fields needed to drive the
hydrologic model for various emerging
applications such as eco-hydrology, monitoring
of reservoirs/lakes storage from satellite altimetry
etc. which can lead to generate operational
hydrological products.
In future, there is requirements to improve the
assessment of water level, soil moisture,
bathymetry, Rainfall and evapotranspiration.
Present capability of repetivity (10-35 days) for
water level retrieval needs to be improved to
daily. Availability of soil moisture at 10 km need
to be improved to 1 km with capability of soil
moisture profiling. It is required to have LIDAR
measurements for river bathymetry and submeter
level DEM with vertical accuracy of 0.5m for
flood inundation studies. Present capability to
measure rainfall need to be improved to 1km
spatial resolution. Hydrological modeling need
simultaneous measurement of LST, NDVI,
Albedo and soil moisture to know ET at high
spatial resolution (100m). An integrated satellite
system dedicated for Hydrological application on
Indian Mission is needed with simultaneous
measurements from wide SAR, Nadir Altimeter,
Passive Radiometer with active rain radar.
5.0 References
1. A. K. Dubey, P. K. Gupta, S. Dutta, and B.
Kumar. 2014. "Evaluation of Satellite
altimetry derived river stage variation for the
brided Brahmaputa river." International
Journal of Remote Sensing 23: 7815-7827.
2. A.K. Dubey, P.K. Gupta, S. Dutta, and R.P.
Singh. 2015. "Water level retrieval using
SARAL/Altika observations in braided
Brahmaputra river." Marine Geodesy 38 (1):
549-567.
doi:10.1080/01490419.2015.1008156.
3. D. Entekhabi, E.G. Njoku, P.E. ONeill, K. H.
Kellogg, W.T. Crow, W.N. Edelstein, J.K.
Entin, S.D. Goodman, T.J. Jakson, J. Johson,
J. Kimball, J. R. Piepmeier R.D. Koster, N.
Martin, K.C. McDonald, M. Moghaddam, S.
Moran, R. Reichle, J.C. Shi and Team. 2010.
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4. E. A. Douglas, E. Rodriguez and D. P.
Lettenmaier. 2007. "Measuring surface water
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24.
5. F. Frappart, F. SeyLER, J.M. Martinez, J.G.
Leon, and A. Cazenave. 2005. "Flood Plain
water storage in the Negro river basin
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6. F. Papa, S.K. Bala, R.K. Pandey, F. Durand,
V.V. Gopalkrishna, A. Rahman, and W.B.
Rossow. 2012. "Ganga- Brahmaputra river
discharge from Jason-2 radar altimetry: An
update to the long term satellite derived
estimation of continental fresh watr forcing
flux into Bay of Bengal." Journal of Geophys.
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7. F.Papa, F. Durand, W. B. Rossow, A.
Rahman, and S.K. Bala. 2010. "Satellite
altimeterderived monthly discharge of
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Ganga-Brahmaputra river and its seasonal to
interannual variations from 1993 to 2008."
Journal of Geophysical Research 115
(C12013). doi:10.1029/2009JC006075.
8. H.S. Srivastava, P. Patel, Y. Sharma and R.
R. Navalgund. 2009. "Large area soil
moisture estimation using multi incidence
angle RADARSAT-1 SAR data." IEEE
transaction on Geosciences and Remote
Sensing 47: 2528-2535.
9. I.M. Bahuguna, B.P. Rathore, R. Brahmbhatt,
M. Sharma, S. Dhar, S.S. Randhawa, K.
Kumar, S. Romshoo, R.D. Shah, R.K.
Ganjoo and Ajai. 2014. "Are the Himalayan
glaciers retreating ?" Current Science 106
(7): 1008-1013.
10. M. R. Pandya, D. B. Shah, H.J. Trivedi, N.P.
Darji, R. Ramakrishnan, S. Panigrahy, J. S.
Parihar, A.S. Kirankumar. 2014. "Retrieval
of land surface temperature from the
Kalpana-1 VHRR data using a single-channel
algorithm and its validation over western
India." ISPRS Journal of Photogrammetry
and Remote Sensing 94: 160–168.
11. P.K. Gupta, S.Chauhan, and M.P. Oza. 2016.
"Modelling Surface Runoff and Trends
Analysis over India." Journal of Earth
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12. P.K.Gupta, A.K. Dubey, N. Goswami, R.P.
Singh, P. Chauhan. 2015. "Use of
Saral/Altika observations for modeling river
flow." Marine Geodesy 38 (1): 614-625.
doi:10.1080/01490419.2015.1008157.
13. P. K. Gupta, S. R. Oza and S. Panigrahy
2010. Monitoring Transplanting Operation of
Rice crop using Passive Microwave
Radiometer Data. International Journal of
Bio-Systems Engineering 108 (2011): 28-35.
14. P. K. Gupta, S. Dutta and S. Panigrahy
(2009). Mapping of Conjunctive Water use
Productivity Pattern in an Irrigation
Command using Temporal IRS WiFS Data.
Journal of Water Resources Management 24
(1): 157-171 .
15. P. S. Thenkabail, S. Mitchell, H. Turral.
2005. "Ganges and Indus river basin
landuse/land cover (LULC) and irrigated
area mapping using continuous streams of
MODIS data". Remote Sens Environ 95:317–
341. doi:10.1016/j.rse.2004.12.018.
16. R. P. Singh, D.R. Mishra, A.K. Sahoo, and S.
Dey. 2005. "Spatial and Temporal variability
of soil moisture over India using IRS P4
MSMR data." International Journal of
Remote Sensing 26: 2241-2247.
17. R. R. Navalgund, R.P. Singh. 2010. "The
evolution of the earth observation system in
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18. R.K. Jaiswal, S.Mukherjee, J. Krishnamurthy
and R. Saxena. 2003. "Role of Remote
sensing and GIS techniques for generation of
ground water prospect zones towards rural
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Jouranl of Remote Sensing 24 (5): 993-1008.
19. R.K. Mall, A Gupta, R. Singh, R. S. Singh
and L. S. Rathore. 2006. "Water resources
and climate change: An Indian perspective."
Current Science 90 (12): 1610-1626.
20. R.M.Gairola. 2015. "SARTAL/ALTIKA
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Achievements." NNRMS (B) Bulletin 39: 1-
11.
21. R.O. Green, T.H. Painter, D.A. Roberts and
J. Dozier. 2006. "Measuring the expressed
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abundance of the three phases of water with
an imaging spectrometer over melting snow."
water Resources 42 (W10402).
doi:10.1029/2005WR004509.
22. R.P.Singh, S.R. Oza, K.N. Chaudhari, and
V.K. Dadhwal. 2005. "Spoatial and
Temporal Patterns of surface soil Moisture
over India estimated using surface wetnes
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23. Rakesh Kumar, R.D. Singh and K.D. Singh.
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24. S. Swenson, J. Famiglietti, M. Rodell, J.
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Mesonet soil moisture data." Water
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25. S.K. Jain, A. Goswami and A.K. Saraf. 2010.
"Snow melt runoff modelling in Himalyan
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26. S.R. Oza, R.P. Singh, V.K. Dadhwal and P.S.
Desai. 2006. "Large area soil moisture
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passive microwave data." Journal od Indian
Society of Remote Sensing 34: 343-350.
27. S. Siebert, P. Döll, J. Hoogeveen, J.M.
Faures, K. Frenken, S. Feick. 2005.
"Development and validation of the global
map of irrigation areas". Hydrol Earth Syst
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28. T. Misra, S.S. Rana, N.M. Desai, D.B. Dave,
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30. V.V. Rao, J.R. Sharma and V.K. Dadhwal.
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31. Y.H. Kerr, P. Waldteufel, J.P.Wigneron, J.M.
Martinuzzi, J. Font, and M. Berger. 2001.
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32. Wagner, W, Lemoine, G, & Rott, H (1999) A
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SATELLITE ALTIMETRY OVER LAND
S. CHANDER
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Radar altimetry over Ocean is a mature science with centimeter level accuracy. But over inland
water bodies there is still scope of improvement by analyzing the raw altimeter measurement
“waveform”. Complete retrieval algorithm is needs to be modified taking into consideration the
land contamination within the altimeter foot-print. The methodology can be broadly divided into
three major categories on waveform classification, waveform retracking and dedicated inland
range corrections algorithms. The 40 Hz waveforms can be classified based on the pattern
matching, maximum likelihood estimation, linear discriminant analysis (LDA) and Bayesian
classifier. Waveforms can be retracked using Brown, Ice-2, Threshold, and Offset Centre of
Gravity methods. Range corrections can be modified by ECMWF operational, ERA reanalysis
pressure fields and global ionosphere maps. With modified methodology it is possible to estimate
inland water level measurement within 10 cm level accuracy. We have tested these results with
the gauge measurements and GPS measured water levels over the validation sites. This water
level product is now being disseminated over 50 major Indian inland water bodies through
VEDAS site.
Key Words: Altimetry, Waveform Retracking, Hydrology, Geophysical range corrections,
ionospheric total electron content, SARAL/AltiKa radar altimeter, SWOT.
1.0 Introduction
Satellite altimetry was begun with the
ocean/gravitational science community needs in
the 1960's. The principle of radar altimeters is
deceptively straightforward. The altimeter
transmits a short pulse of electromagnetic
radiation with known power towards the earth's
surface. The pulse interacts with the surface and
part of the incident radiation is reflected back to
the altimeter. This return radar power received by
the altimeter is recorded through time, producing
an altimetric "waveform". By analyzing
amplitude and shape of return waveform, various
characteristics of the surface such as backscatter
coefficient, surface roughness, wave height and
wind speed over oceans, etc. can be retrieved. In
Figure 1 explains the basic principle of the
altimeter. As shown in the figure the radar pulse
need to be corrected due to atmospheric and
ionospheric effects. Onboard radiometer is used
to correct the signal due to highly spatial and
temporal variable water vapor. But the foot-print
of the radiometer is an order of larger than the
altimeter foot-print, so is more prone to land
contamination in the coastal and inland water
bodies. Meteorological model derived water
vapor information can be used than to estimate
the wet tropospheric correction. All the altimeter
measurements are provided with respect to single
reference ellipsoid, i.e. WGS84. This reference
can be converted to mean sea level and geoid
once the high resolution information is available
over the reference site.
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Fig.1: Altimeter Principle
Altimetric measurements have a wide range of
applications in Oceanography, Geodesy,
Geophysics, Glaciology, and Continental
Hydrology. Satellite altimetry for inland water
applications has evolved from investigation of
water height retrieval to its monitoring since last
two decades. Monitoring water level of inland
water bodies has become essential for studying
the effects of global climate change and
increasing population pressure on the fresh water
resources. The traditional monitoring of river
gauges is limited to few locations. But, non-
availability of in-situ measurements at desired
locations imposes serious constraints on basin
scale hydrology study. Besides this, in-situ
discharge measurement is expensive and time
consuming. Satellite Altimetry has the potential
to monitor inland water bodies continuously and
consistently over a long period of time. Thus
altimetry can support subsequent hydrologic-
hydraulic modeling for planning and
management of water resources at regional
scales. Altimetry derived reservoir/ river levels
can subsequently be used to deal with key inland
water resources problems such as flood, rating
curve generation for remote locations, reservoir
operations, and calibration of river/lake models.
2.0 Limitations over Land
Altimeter waveform can have different
characteristic shapes according to reflecting
surfaces like Open Ocean, ice caps, inland water,
and continental surfaces. Waveforms over open
water show a characteristic shape, often referred
to as "ocean-like" or "Brown-like", featuring a
sharp rise up to a maximum value, followed by a
gentle sloppy plateau. But over inland water
bodies and land waveform may have different
shapes with more than one peak due to more than
one reflecting surface within the altimeter foot-
print. More details about the waveform shapes
and classification can be found elsewhere. If
these waveform are not classified can lead to
height inaccuracy of the order of few meters. One
such example of SARAL altimeter multi-peak
waveform is shown in figure 2 over one of the
Inland water body.
Fig.2: Multi-peak waveforms
Altimeter can only measure within a narrow
range window vertically, called "analysis
window". As the satellite-to-Earth distance
changes with the satellite motion along the orbit
(due to Earth surface topography), the position of
the analysis window must be adjusted to ensure
that the altimeter samples at the time when the
pulse hits the surface. This is done, by "on-board
tracker", a predictive device that minimizes the
risk of the altimeter losing track of the surface.
The tracker on-board the altimeter tries to align
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the midpoint of the leading edge of the waveform
at the nominal tracking gate that is fixed for a
given altimeter. But generally it is unable to
accurately predict the range and thus, the
midpoint of the leading edge is not always
aligned at the nominal tracking gate. Over ocean
this correction rarely rises more than few
centimeter. In order to achieve very high
accuracy in range the waveforms acquired are
down linked to earth where they are retracked
manually to improve the range estimates.
Over land the situation is more complicated, and
if along track slope is higher than few meter the
altimeter can lost its track. Thus for land
altimetry, the waveform must be post processed
to exactly estimate the range. This is called
"waveform retracking". Various model (based on
physics) and empirical retrackers have been
developed for characterizing the waveforms from
open-ocean, coastal and continental ice. The
formation of the theoretical shape of an echo over
the surface was given by Brown & Hayne [1].
Beta-5 retracker [2] is a five parameter functional
model designed to derive geophysical parameters
for Brown like waveforms obtained over the
ocean and large water bodies by trying to fit the
acquired waveform to the model curve. Offset
Centre of Gravity Retracker (OCOG) is normally
used for rectangular type waveforms where no
model can be fitted [3]. It attempts to find the
Centre of Gravity, Amplitude and Width of the
waveform. The threshold retracker was first
developed by Davis [4]. The amplitude of the
waveform is determined as the OCOG retracker.
Then the leading edge is determined by
computing the gate corresponding to threshold of
the amplitude after taking care of the thermal
noise component. Different threshold levels were
suggested based on the scattering mechanism, i.e.
50% threshold level for waveforms dominated by
surface scattering and 25% threshold for volume
scattering.
3.0 Sub-waveform based waveform retracker
A modified sub-waveform based retracker is
developed and implemented to see the
improvement in retrieved range. Since most
categories of waveforms namely brown, specular
and rectangular can be accurately retracked by
existing empirical and physically based
retrackers, it remains to be seen whether
multipeak waveforms can be retracked correctly
using the subwaveform based retracker.
Subwaveform based retracking technique like the
improved threshold exist but it requires some
reference data of water level height to identify the
appropriate subwaveform and determine the
correct range. This new retracker attempts to find
the first leading edge in the waveform and retrack
the subwaveform using the 50% threshold
method without trying to explain the full shape of
the waveform. The basic assumption is that the
first leading edge is from the surface closest to the
satellite and for flat surfaces it is from the nadir.
Figure 3 shows the improvement in range
estimation using this modified retracker with
comparison to previous retrackers. The range can
be corrected by order of 5 m, as in the case of
multipeak waveform [8].
Fig.3: Comparison between retracking algorithms
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4.0 Geophysical Range Corrections
The range estimated after waveform retracking
needs to be corrected for atmospheric effects and
sea-atmospheric interaction. The presence of dry
gasses, water vapor in the troposphere and free
electron in the ionosphere reduces the speed of
the radar pulse causing the observed range to
become longer and the sea surface height to be
too low. These atmospheric range correction
algorithms were modified for inland water
bodies.
The Dry Tropospheric correction (DTC) effect
can be derived from the sea level pressure data set
mainly from model re-analysis or forecasts. The
DTC has strong height dependence and since
satellite altimetry is primarily designed for ocean
applications, for which no such dependence
exists, altimeter products fail to provide the DTC
appropriate for inland water studies. The
correction was computed from Sea Level
Pressure (SLP) grids, using equation [5]:
𝐷𝑇𝐶 =0.0022768 𝑃0
1 − 0.0026 𝐶𝑜𝑠 2∅ − 0.28 × 10−6 ℎ𝑠
Here 𝑃0 is the sea level pressure, ∅ is the latitude
and ℎ𝑠 is the surface height.
The path delay due to the presence of water vapor
in the atmosphere, the wet tropospheric
correction (WTC), is one of the major error
sources in satellite altimetry. On-board
microwave radiometers corrections are hampered
by the contamination of the surrounding lands.
Alternatively, the vertically integrated water
vapor required for the wet tropospheric range
correction could be obtained from meteorological
model analyses. The WTC was calculated from
global grids of two single-level parameters
provided by global atmospheric models from the
following expression [6]:
𝑊𝑇𝐶 = − (0.101995 +1725.55
50.44+0.789 𝑇0)
𝑇𝐶𝑊𝑉
100
Here 𝑇0 is the near-surface air temperature (two-
meter temperature) and 𝑇𝐶𝑊𝑉 is the total column
water vapor.
Atmospheric refraction from free electrons and
ions in the upper atmosphere is related to the
dielectric properties of the ionosphere. This
columnar electron density can be approximated
by the Total Electron Content (TEC), i.e. the
integrated electron density. Therefore, the
ionospheric delay can be calculated using
following equation [7]:
𝐼𝐶 = −40.3 𝑇𝐸𝐶
𝑓𝐾𝑎2
For smaller inland water bodies earth tide is
applied, but elastic-ocean and ocean-loading tides
are only applicable for larger water bodies (~
1000 km2). The inverse barometric correction is
not applied because the lakes/reservoirs are
closed systems. The sea state bias (SSB)
correction is also not applied because wind
effects tend to be averaged out along-track.
Model predicted load tide and solid earth tide was
directly taken from the SARAL SGDR products.
Fig. 4: Flow chart to estimate dynamic height
Figure 4 shows the complete flow chart to
estimate the dynamic water level using altimeter
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dataset. With the modified retrieval algorithms
the water level was retrieved over the Ukai
reservoir and found to be matching within RMSE
15 cm, and validated with the gauge dataset and
GPS measured water levels. The results are
shown in figure 5 [9].
Fig.5: Retrieved water level over Ukai reservoir
5.0 Conclusions and Future Directions
Over inland water bodies due to land
contamination within the altimeter foot-print,
generally altimeter gets very complex multi-
peaked waveforms. Due to complex waveforms,
the accuracy of the retrieved water level is limited
to few tens of centimeter, but with the proposed
methodology we have achieved retrieval
accuracy better than 15 cm. Knowledge about the
waveform shape prior to retracking is useful for
optimizing the choice of the retracking method.
Waveform retracker should be robust so that it
can take care for a number of waveform shapes
that generally found in nature. The range
corrections dedicated to inland water bodies are
important to remove the seasonal trend from the
water level retrieval. Overall, the altimeter
retrieved water level was found to be matched
well both with the gauge measurement. Altimeter
has a limitation that it only gives information
along the nadir track, but generally over inland
water bodies we require across track information
as well. Surface water and Ocean topography
(SWOT) is a proposed altimeter that will utilize
Ka band radar interferometry technique to
estimate the slope and extent information of the
water surface in across track direction.
References
1 G. S. Hayne, “Radar altimeter mean return
waveforms from near-normal-incidence ocean
surface scattering,” IEEE Transactions on
Antennas Propagations 28(5), 687–692 (1980).
2 T. V. Martin, H. J. Zwally, A. C. Brenner, and
R. A. Bindschadler, “Analysis and retracking of
continental ice sheet radar altimeter waveforms,”
Journal of Geophysical Research: Oceans
88(C3), 1608–1616 (1983).
3 D. J.Wingham, C. G. Rapley, and H. Griffiths,
“New techniques in satellite tracking system,”
Proceedings of IGARSS’86 symposium, 1339–
1344 (1986).
4 C. H. Davis, “A robust threshold retracking
algorithm for measuring ice-sheet surface
elevation change from satellite radar altimeters,”
IEEE Transactions on Geoscience and Remote
Sensing 35(4), 974–979 (1997).
5 E. K. Smith and S. Weintraub, “The Constants
in the Equation for Atmospheric Refractive Index
at Radio Frequencies,” Proceedings of the IRE
41(8), 1035–1037 (1953).
6 M. Bevis, S. Businger, S. Chiswell, T. A.
Herring, R. A. Anthes, C. Rocken, and R. H.
Ware, “GPS meteorology - Mapping zenith wet
Delays onto Precipitable Water,” J. Appl. Meteor.
33, 379–386 (1994).
7 N. Picot, K. Case, S. Desai, and P. Vincent,
“AVISO and PODAAC User Handbook, IGDR
and GDR Jason Products,” (2003).
8 D. Ganguly, S. Chander, S. Desai, and P.
Chauhan, “A Subwaveform-Based Retracker for
Multipeak Waveforms: A Case Study over Ukai
Dam/Reservoir,” Marine Geodesy 38(1), 581–
596 (2015).
9 S. Chander and D. Ganguly, “Development of
water level estimation algorithms using
SARAL/Altika dataset & Validation over the
Ukai Reservoir, India”, accepted in Journal of
Applied Remote Sensing (2016).
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HYDROLOGICAL MODELLING AND REMOTE SENSING
P. K. GUPTA
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Water in our planet is available in the atmosphere, the oceans, on land and within the soil and
fractured rock of the earth’s crust Water molecules from one location to another are driven by the
solar energy. Moisture circulates from the earth into the atmosphere through evaporation and then
back into the earth as precipitation. In going through this process, called the Hydrologic Cycle,
water is conserved – that is, it is neither created nor destroyed. All hydrological models are
simplified representations of the real world. Models can be either physical (e.g. laboratory scale
models), electrical analogue or mathematical. The physical and analogue models have been very
important in the past. However, the mathematical group of models is by far the most easily and
universally applicable, the most widespread and the one with the most rapid development with
regard to scientific basis and application. A hydrological model is composed of two main parts, a
hydrological core and a technological shell. The hydrological core is based on a certain
hydrological scientific basis providing the definitions of variables, the process descriptions and
other aspects. The technological shell is the programming, user interface, pre- and post-processing
facilities etc. Recent advances in the remote sensing technologies such as altimetery,
scatterometery, LANDASAT8, passive radiometers, multiple sensors in a single platform, SMAP,
SMOS etc. have made it possible to estimate various hydrological parameters such as rainfall,
river water levels, ET, soil moisture, groundwater etc. Current research is focused on
parameterization of various hydrologic-hydraulic models using remote sensing derived
hydrological variables.
Key Words: Satellite Remote Sensing, Hydrological models, flood modeling, water balance,
river flow models, curve number.
1.0 Key issues in water Resources
Effects of exploitation of water resources
Periodic or permanent lowering of
groundwater table
Increased concentration of pollutants in
the aquifer
Increased risk of salt water intrusion and
land subsidence
Irrigation
Continuing low efficiency of irrigation
projects
Environmental concerns due to
excessive irrigation
Land Degradation and Soil Erosion
Desertification due to increased human
and livestock population
Negligence of upland catchment
management
Surface and Groundwater Pollution
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Contamination of water due to waste
disposal
Nitrogen and Pesticides pollutions due
to agricultural activities
Floods and Droughts
Changes in the land use
Changes in the hydrological regime
2.0 Hydrological processes
Following hydrological processes need to be
considered for the development of the
hydrological models;
• Canopy rain Interception
• Infiltration
• Depression storage
• Soil moisture storage
• Shallow sub-surface runoff / baseflow
• Preferential /macropore flow
• Runoff generation processes
• Infiltration excess (Horton flow)
• Saturation excess (Dunne flow)
• Flow routing
• Soil evaporation
• Vegetation transpiration
• Water surface evaporation
• Canopy water evaporation
• Groundwater flow
• Lakes
• Wetlands
• Structures that control waters
• Snowmelt
Precipitation
Precipitation occurs when atmospheric moisture
becomes too great to remain suspended in clouds.
It denotes all forms of water that reach the earth
from the atmosphere, the usual forms being
rainfall, snowfall, hail, frost and dew. Once it
reaches the earth’s surface, precipitation can
become surface water runoff, surface water
storage, glacial ice, water for plants,
groundwater, or may evaporate and return
immediately to the atmosphere. Rainfall
measurements can be done using rain gauge and
Fig. processes involve for the development of the
hydrological system model.
satellite remote sensing. Sources from which
remote sensing derived rainfall is available is
Climate Prediction Centre (NOAA), tropical
rainfall measurement mission (TRMM),
meteorological satellite (METEOSAT) etc.
Interception:
Interception is defined as the process whereby
precipitation is retained on the leaves, branches,
and stems of vegetation. This intercepted water
evaporates directly without adding to the
moisture storage in the soil. The interception
process is modelled as an interception storage,
which must be filled before stem flow to the
ground surface takes place. The size of the
interception storage capacity, depends on the
vegetation type and its stage of development,
which is characterised by the leaf area index.
Runoff:
Runoff is the water that flows across the land
surface after a storm event. As rain falls over
land, part of that gets infiltrated the surface and
RAINFALL
Through fall
Canopy storage
INFILTRATION
Soil water store
Groundwater discharge
Direct
rain onto stream
From Open waterFrom
Canopy storage
From soil
EVAPORATION
Geological lenses
From crops
TRANSPIRATION
STREAM FLOW
Interception
Figure: Processes for modelling hydrological cycle in the forest system
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remaining water flows as overland flow. As the
flow bears down, it notches out rills and gullies
which combine to form channels. The
geographical area which contributes to the flow
of a river/channel is called a catchment of that
river/channel.
Storage:
Portion of the precipitation falling on land surface
which does not flow out as runoff gets stored as
either surface water bodies like Lakes, Reservoirs
and Wetlands or as sub-surface water body like
soil moisture and Ground water.
The following definitions may be useful:
Lakes: Large, naturally occurring inland body of
water
Reservoirs: Artificial or natural inland body of
water used to store water to meet various
demands.
Wet Lands: Natural or artificial areas of shallow
water or saturated soils that contain or could
support water–loving plants.
Unsaturated Zone (Vadose Zone):
Zone between ground surface to groundwater
table is known as unsaturated zone. The
unsaturated zone is usually heterogeneous and
characterized by cyclic fluctuations in the soil
moisture as water is replenished by rainfall and
removed by evapotranspiration and recharge to
the groundwater table. Unsaturated flow is
primarily vertical since gravity plays the major
role during infiltration.
Soil water constants
For a particular soil, certain soil water
proportions are defined which dictate whether the
water is available or not for plant growth. These
are called the soil water constants, which are
described below.
• Saturation capacity: this is the total water
content of the soil when all the pores of the soil
are filled with water. It is also termed as the
maximum water holding capacity of the soil. At
saturation capacity, the soil moisture tension is
almost equal to zero.
• Field capacity: this is the water retained by an
initially saturated soil against the force of gravity.
Hence, as the gravitational water gets drained off
from the soil, it is said to reach the field capacity.
At field capacity, the macro-pores of the soil
are drained off, but water is retained in the
micropores. Though the soil moisture tension at
field capacity varies from soil to soil, it is
normally between 1/10 (for clayey soils) to 1/3
(for sandy soils) atmospheres.
• Permanent wilting point: plant roots are able
to extract water from a soil matrix, which is
saturated up to field capacity. However, as the
water extraction proceeds, the moisture content
diminishes and the negative (gauge) pressure
increases. At one point, the plant cannot extract
any further water and thus wilts.
The Saturated Zone (SZ):
Saturated subsurface flow or ground water table
is known as saturated zone. Ground water storage
is the water infiltrating through the soil cover of
a land surface and traveling further to reach the
huge body of water underground. As mentioned
earlier, the amount of ground water storage is
much greater than that of lakes and rivers.
However, it is not possible to extract the entire
groundwater by practicable means. It is
interesting to note that the groundwater also is in
a state of continuous movement – flowing from
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regions of higher potential to lower. The rate of
movement, however, is exceptionally small
compared to the surface water movement.
Evapotranspiration
Evapotranspiration is actually the combination of
two terms – evaporation and transpiration. The
first of these, that is, evaporation is the process of
liquid converting into vapour, through wind
action and solar radiation and returning to the
atmosphere. Evaporation is the cause of loss of
water from open bodies of water, such as lakes,
rivers, the oceans and the land surface. It is
interesting to note that ocean evaporation
provides approximately 90 percent of the earth’s
precipitation. However, living near an ocean does
not necessarily imply more rainfall as can be
noted from the great difference in the amount of
rain received between the east and west coasts of
India.
Transpiration is the process by which water
molecules leaves the body of a living plant and
escapes to the atmosphere. The water is drawn up
by the plant root system and part of that is lost
through the tissues of plant leaf (through the
stomata). In areas of abundant rainfall,
transpiration is fairly constant with variations
occurring primarily in the length of each plants
growing season. However, transpiration in dry
areas varies greatly with the root depth.
Evapotranspiration, therefore, includes all
evaporation from water and land surfaces, as well
as transpiration from plants.
Potential evapotranspiration (PET)
Pan evaporation The evaporation rate from pans
filled with water is easily obtained. In the absence
of rain, the amount of water evaporated during a
period (mm/day) corresponds with the decrease
in water depth in that period. Pans provide a
measurement of the integrated effect of radiation,
wind, temperature and humidity on the
evaporation from an open water surface.
Although the pan responds in a similar fashion to
the same climatic factors affecting crop
transpiration, several factors produce significant
differences in loss of water from a water surface
and from a cropped surface. Reflection of solar
radiation from water in the shallow pan might be
different from the assumed 23% for the grass
reference surface. Storage of heat within the pan
can be appreciable and may cause significant
evaporation during the night while most crops
transpire only during the daytime.
Overland flow:
The amount of rainfall in excess of the infiltrated
quantity flows over the ground surface following
the land slope. This is the overland flow. The
portion that infiltrates moves through an
unsaturated portion of the soil in a vertical
direction for some depth till it meets the water
table, which is the free surface of a fully saturated
region with water (the ground water reserve).
3.0 Remote Sensing and GIS
For water resources engineer, locating aerial
extent of water bodies like lakes, rivers, ponds,
etc. from remotely sensed data is an important
task. The spectral response from a water body is
complex, as water in any quantity is a medium
that is semi-transparent to electromagnetic
radiation. Electromagnetic radiation incident on
water may be absorbed, scattered and transmitted.
The spectral response also varies according to the
wavelength, the nature of the water surface (calm
or wavy), the angle of illumination and
observation of reflected radiation from the
surface and bottom of shallow water bodies. Pure
clear water has a relatively high reflectance in the
visible wavelength bands between 0.4 and 0.6μm
with virtually no reflectance in the near-infrared
(0.7μm) and higher wavelengths. Thus clear
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water appears dark on an infrared image.
Therefore, location and delineation of water
bodies from remotely sensed data in the higher
wave bands can be done very accurately.
The satellite Remote Sensing provides
information in spatial and temporal domains,
with high resolution about the processes of the
land phase of the hydrological cycle, which is
very crucial for successful model analysis,
prediction and validation (Jagadeesha, 1999). RS
techniques can be extremely useful in estimating
a number of key variables of DHMs, particularly
for large basins with sparse data. RS technologies
are often considered as innovative ways of
obtaining data at a reduced cost (Koblinsky et al.,
1992) and replace the conventional techniques. In
addition to that RS can provide time series of data
relatively easily and enabling periodically
updating of variables. Benefit/cost ratio ranging
from 75:1 to 100:1 can be realised in using
remotely sensed data in hydrology and water
resources management (Kite and Pietroniro,
1996).
Table: RS based hydrological variables
The use of RS technology involves large amount
of spatial data management. The GIS technology
provides suitable alternatives for efficient
management of large and complex databases. The
possibility of rapidly combining data of different
types in a GIS has led to significant increase in its
use in hydrological applications. The use of RS
data, in combination with DHM, provides new
possibilities for deriving spatially distributed
time series of input variables, as well as new
means for calibration and validation of the
hydrological model (Bastiaanssen et al., 2000;
Fortin et al., 2001).
Table: Hydrological processes and address
through remote sensing derived variables.
3.0 Hydrological models classifications
Models are classified based on the process
description.
Fig. classification of hydrological models based
on the process description.
Hydrologic parameters Sensor Technology Resolution Repeat ivity
Rainfall TRMM, INSAT, NOAA CPC,
JAXA
Precip. Radar (JAXA)TMI,
VIRS VHRR
0.01 to 0.25 Deg Daily
3 hourly
Soil moisture SSMI, AMSR Radiometers 12-56 km 5-day
Groundwater GRACE gravity 100,000 km2 30 days
Lake/reservoir levels Jason-2, ALTIKA, Sentinel-3 Altimetric radar 350 m 10 day
Evapotranspiration MODIS, INSAT Visible/NIR 1 km to 8 km 1-2 days
Stream discharge Jason-2, ALTIKA Altimetric radar 350m, 175m 10 -35 day
Leaf area index INSAT, MODIS Visible/NIR 1 km 8 day comp.
Topography CARTOSAT-1, SRTM,
GTOPO, ASTER
Optical, microwave 10 m to 1 km -
Insolation INSAT, MODIS VHRR 1 km to 8 km daily
Land Surface Temp. INSAT, MODIS Thermal Infrared 8 km hourly
Land use/cover RESOURCESAT-2, MERIS,
MODIS, SPOT
Optical 56 m to 1 km yearly
Lakes/Wetland extents RESOURCESAT-2, MODIS Optical 23 m to 250 m yearly
Snow covered area RESOURCESAT-2, MODIS Optical 56 m 8 day
Albedo INSAT, MODIS Optical 1 km 16 day
NDVI INSAT, MODIS, SPOT Optical 1 km 16 day
Wind speed , humidity INSAT-
3D/MEGHSTROPICS
Optical/sounder Daily
GPP INSAT, MODIS Optical 1 km 8 day
NPP INSAT, MODIS Optical 1 km yearly
S.N. Processes RS parameters Other Data
1 Canopy rain Interception LAI
2 Infiltration LULC, Rainfall Soil
3 Depression storage DEM
4 Soil moisture LULC, Soil Moisture, DEM Soil
5 Base flow DEM Geological formation
6 Preferential flow LULC, DEM Macroporosity
7 Runoff (Saturation excess-Infiltration excess)
DEM, LULC, Rainfall Soil
8 Soil Evaporation LST, Humidity Soil
9 Water Evaporation LST, Water Bodies, Humidity
10 Canopy water Evaporation LAI
11 Transpiration LAI, Humidity, Water level (Altika) Root depth and density
12 Flow routing DEM, LULC
13 Ground Water Flow Ground Water Anomaly (GRACE) Lethology
14 Lake/Wetlands Land Water Mask
15 Structural control water Reservoir Extent Dam Locations
16 Snow Melt Albedo, Insulation, Snow cover, Short and long wave radiation, LST
Weather Parameters
Hydrological Simulation Model
Deterministic
Empirical
Stochastic
Lumped
Conceptual
Distributed
Physically based Joint Stochastic-
Deterministic
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Some important definitions in hydrological
modeling:
Model: a conceptual or physically based
procedure for numerically solving the
hydrological processes.
A mathematical model: is a set of mathematical
expressions and logical statements combined in
order to simulate the natural system.
Static model: empirical and regression model in
which time is not independent variable
Dynamic model: require different equation with
time as independent variable and this shows the
time variability of output.
Why simulation model: are used to understand
how system work and interact one another.
Deterministic model: is a model where two
equal sets of input always yields the same output
if run through the model under identical
conditions. A deterministic model has no inner
operations with a stochastic behaviour.
Empirical model: is a model developed without
any consideration of the physical processes that
we otherwise associate with the catchment. The
model is merely based on the analysis of the
concurrent input and output time series. Also,
known as black box model.
Lumped model: is a model where the catchment
is regarded as one unit. The variables and
parameters are thus representing average values
for the entire catchment. Thus the virtual world is
just reduced to just one object.
Conceptual model: physically sound knowledge
and empirically derived equations. Physical
significance is not clear that is why not possible
to assess the parameters from direct
measurement.
Stochastic model: has at least one component of
random character which is not explicit in the
model input, but only implicit or hidden.
Therefore, identical inputs will generally results
in different outputs if run through the model
under, externally seen, identical conditions.
Physical based: description of natural system
using the basic mathematical representation of
the flows of mass, momentum and various forms
of energy. Known as white box model. These
model consists of linked Partial Differential
Equations (PDE’s) with parameters, which in
principle have direct physical significance and
can be evaluated by independent measurements.
Physical processes like conservation of mass and
momentum acting upon input variables are taken
into account.
Distributed: able to take spatial variation of
variables and parameters into account which are
spatially interactive on cell by cell basis.
Simulations: is time varying description of the
natural system computed by the hydrological
model. A simulation may be seen as the models
imitation of the behaviour of the natural system
Parameter: a parameter is a constant in the
mathematical expressions or logical statements of
the mathematical model. It remains constant in
the virtual time.
Variable: is a quantity which varies in space and
time. It can be a series of inputs to and outputs
from the model, but also a description of
conditions in some component of the model.
Modelling system: is defined as a generalized
software package, which, without program
changes, can be used to establish a model with the
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same basic types of equation (but allowing
different parameter values) for different
catchments.
Model qualification: an estimation of the
adequacy of the conceptual model to provide an
acceptable level of agreement for the domain of
intended application.
Model Verification: substantiation that a
computerized model is in some sense a true
representation of a conceptual model within
certain specified limits or ranges of application
and corresponding accuracy.
Model calibration: involves manipulation of a
specific model parameters to reproduce the
response of the catchment under study within the
range of accuracy specified in the performance
criteria. It is important to assess the uncertainty in
the estimation of model parameters, for example
from sensitivity analysis.
Model validation: is the processes of
demonstrating that a given site specific model is
capable of making sufficiently accurately
predictions. This implies the application of the
calibrated model without changing the
parameters values that were set during the
calibration when simulating the response for
another period than the calibration period. The
model is said to be validated if its accuracy and
predictive capability in the validation period have
been proven to lie within acceptable limits
Distributed Hydrological Models (DHMs) can
serve as a tool to simulate the hydrological water
balance in the command/watershed, which is
essential to reassess the crop water demand both
in space and time. But these DHMs face the
problem of inadequate field data to describe the
processes of hydrological cycle accurately. The
amount of information available using the
conventional method is often very less than the
ideal to run a spatially distributed model
(Vachaud and Chen, 2002). Secondly,
development of more complex, physically
realistic, distributed hydrological models has
dramatically increased the demand for spatial
data (Pietroniro and Leconte, 2000).
Contrary to the lumped conceptual models, a
distributed physically based model does not
consider the water flows in an area to take place
between a few storage units. Instead, the flows of
water and energy are directly calculated from the
governing continuum (partial differential)
equations, such as for instance the Saint Venant
equations for overland and channel flow,
Richards’ equation for unsaturated zone flow and
Boussinesq’s equation for groundwater flow.
Distributed physically-based models have been
used for a couple of decades on a routine basis for
the simulation of hydrological processes.
Today, several general-purpose catchment model
codes of this type exist such as SHE (Abbott et
al., 1986), MIKE SHE (Refsgaard and Storm,
1995), IHDM (Beven et al. 1987). Distributed
physically based models give a detailed and
potentially more correct description of the
hydrological processes in the catchment than do
the other model types. Moreover, they are able to
exploit the quasi-totality of all information and all
knowledge that is available concerning the
catchment that is being modelled. The distributed
physically based models can in principle be
applied to almost any kind of hydrological
problem. However, in practice, they will be used
complementary to the other model types for cases
where the other models are not suitable. Some
examples of typical applications are:
Prediction of the effects of catchment changes
due to human interference in the hydrological
cycle, such as changes in land use (including
urbanization), groundwater development and
irrigation.
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Prediction of runoff from ungauged catchments
and from catchments with relatively short
records.
As opposed to the lumped conceptual models,
which require long historical time series of
rainfall, runoff and evaporation data for the
assessment of parameters, the parameters of the
distributed physically-based models may be
assessed from intensive, short-term field
investigations.
Water quality and soil erosion modelling for
which a more detailed and physically correct
simulation of water flows is important.
4.0 Calibration and validation of the models
In the present study, resistance number and
seepage loss are considered as the model
calibration parameters. Resistance number used
in this model is defined as the reciprocal of the
Manning's roughness coefficient, which is
otherwise known as Strickler's coefficient. For
calibrating the model, an initial run is made with
default value of global resistance number and
seepage loss. Selection of locations for
calibration is done based on the availability of
observed flow data. Based on the comparison
between the observed and simulated flows, global
resistance number and seepage loss are adjusted.
This process is continued until the observed and
simulated values are in close agreement. For
further refinement of results, local resistance
numbers are used in the system and simulations
are done till the better match between observed
and simulated flows.
Calibrated model is validated for the period other
than considered for the calibration of the model.
Two goodness-of-fit criteria recommended by the
ASCE Task Committee (ASCE, 1993a,b), i.e.,
percent deviation of flow volume DV and Nash-
Sutcliffe coefficient R2, are considered to draw a
better conclusion from the comparison of
observed and simulated flow values. Percent
deviation of flow volume is calculated by using
the following formula.
100
V
VVD
O
SOV
where, Vs = simulated flow volume (m3); and Vo
= observed flow volume (m3). The value of Dv
should be zero for a perfect model.
The Nash-Sutcliffe Coefficient is calculated as
follows:
2
avO
2
SO2
QQ1R
where, Qo = observed discharge (m3/s); Qs =
simulated discharge (m3/s); and Qav = mean of the
observed discharge (m3/s). The value of R2 is 1
for the perfect model.
In addition, Student's t-value is also computed to
test the significance of the difference of means of
observed and simulated flows. The Student's t-
value is estimated by using the following
formula.
1
0
s
n
S
ddt
where, ts = computed t-value, d = mean of the
residuals; d0 = hypothesized mean which is
considered as zero; S = standard deviation of
residuals and n1 = number of data.
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Status of Application of hydrological
modelling systems to various problems
5.0 Conclusions and Future Directions
Remote sensing has a strong technological basis
for the development of advanced sensors and
processing systems from various satellite
platforms whereas hydrology is more of science
oriented describing the various processes
involved in the water cycle. Therefore, current
research focus on integrating remote sensing
derived hydrological variables into various
hydrologic and hydraulic models to bridge the
gap between the point measurements and
mathematical model simulations.
Parameterization of a hydrological system model
demands for various datasets. Some of the crucial
hydrological parameters which are estimated
from remote sensing may be utilize for the
initialization, boundary and for the calibration
and validation of models. In near future, with the
evolving satellite technology (Surafce Water
Ocean Topography (SWOT, NASA-ISRO multi
frequency SAR (NISAR), some of the
hydrological processes such as surface runoff,
river flow etc. shall be addressed from satellite
platforms.
6.0 References
1. ASCE Task Committee on Definition of
Criteria for Evaluation of Watershed Models
(1993a). Criteria for evaluation of watershed
models. J. Irrig. and Drain. Engrg., ASCE,
119 ( 3): 429-442.
2. ASCE Task Committee on Irrigation Canal
System Hydraulic Modeling. (1993b).
Unsteady flow modeling of irrigation canals.
J. Irrig. and Drain. Engrg., ASCE, 119 (4):
615-630.
3. Bastiaanssen, W. G. M., Molden, D. J. and
Makin I. W. (2000). Remote sensing for
irrigated agriculture: examples from research
and possible applications. Agric. Water
Mgmt., 46: 130-155.
4. Bos, M. G. (1997). Performance indicators
for irrigation and drainage. Irrig. and Drain.
Sys., 11: 119-137.
5. DHI. (1988). MIKE 11 Scientific
documentation and user guide, DHI,
Copenhagen, Denmark.
6. Fortin, P. J., Turcotte, R., Massicotte, S.,
Moussa, R., Fitzback, J. and Villeneuve, P. J.
(2001). Distributed watershed model
compatible with remote sensing and GIS data
II: Application to Chaudie’re watershed. J.
Hydrol. Engrg., ASCE, 6(2): 100-108.
7. Jagadeesha, C. J. (1999). Water resources
development and management.
GISdevelopment, 3(6): 20-22.
8. Kite, G. W and Piteroniro, A. (1996). Remote
sensing applications in hydrological
modeling. Hydrol. Sci. J., 41(4): 561-591.
9. Koblinsky, C.J., Gaspar, P., Lagerloef, G.
(eds). 1992. The Future of Spaceborne
Altimetry: Oceans and Climate Change. Joint
Field Status of Application
Adequacy1
of
Scientific
Basis
Scientifically1
well Tested
?
Validation2
on Pilot
Schemes
?
Practical3
Applications
Major4
Constraints
for Practical
Applications
Water Resources Assessment
*Groundwater Good Good Adequate Standard/Part Administrative
*Surface Water Very Good Very Good Adequate Standard/Part Administrative
Irrigation Good Good Partially Very Limited Techno/Admin
Soil Erosion Fair Fair Very
Limited
Nil Science
Surface Water
Pollution
Good Good Adequate Some Cases Administrative
Groundwater Pollution
*Point Source
(Landfills)
Good Good Partially Standard/Part Techno/Admin
*Non-point
(Agriculture)
Fair Fair Very
Limited
Very Limited Techno/Admin
Effect of Land Use Changes
* Flows Good Fair Fair Very Limited Science
*Water Quality Fair Fair Fair Nil Science
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Oceanographic Institutions Incorporated:
Washington, DC; 75 pp.
10. Ministry of Finance, (2002). Union budget
and economic survey, 2002-2003.
Government of India, New Delhi, India.
11. Swaminathan, M.S. (2000) Natural resources
management- for an evergreen revolution. In
The Hindu - Survey of Indian Agriculture
2000. pp. 9-16.
12. Ongley, E. D. (1996). Control of water
pollution from agriculture. Irrig. and Drain.
Paper No. 55, Food and Agric. Organization,
Rome.
13. Palanisami, K., 1984. Irrigation water
management : The determinants of canal
water distribution in India - A micro
analysis. Agricole Publishing Company,
New Delhi, 120 p.
14. Pietroniro, A. and Leconte, R. (2000). A
review of Canadian remote sensing
applications in hydrology, 1995-1999.
Hydrol. Process., 14: 1641-1666.
15. Refsgaard, J. C. and Storm, B. (1995). MIKE
SHE. Computers Models in Watershed
Hydrology, V. P. Singh (ed.), Water
Resources Publications, Colorado, USA,
806-846.
16. Sanmugnathan, K. and Bolton, P. (1988).
Water management in third world irrigation
schemes. ODA Bulletin, No. 11, Hydraulic
Research, London, UK.
17. Vachaud, G. and Chen, T. (2002). Sensitivity
of a large-scale hydrologic model to quality
of input data obtained at different scales;
Distributed versus stochastic non-distributed
modeling. J. Hydrol., 264: 101–112.
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EVAPOTRANSPIRATION: TOOLS AND TECHNIQUES
ROHIT PRADHAN
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Evapotranspiration(ET) is the combination of two processes where water is lost on one hand from
soil surface by evaporation and on the other hand from vegetation by transpiration.
Evapotranspiration forms a key component of the hydrological cycle which directly influences
the climate system. Estimation of ET is also important for management of irrigation in agricultural
systems, for addressing water balance in catchments of reservoirs and rivers etc. ET and soil
moisture greatly influence the climate and are now being used extensively for improving weather
forecasts. This note provides a brief introduction to the processes of evaporation and transpiration
and its influencing parameters followed by the various models used in estimation of potential and
actual ET over land surface. This is followed by a section on estimating ET using remotely sensed
data.
Key Words: Evapotranspiration, Potential ET, evaporation models, remote sensing.
1. Introduction
As the name suggests, ET involves two
components: (a) Evaporation is the process
whereby liquid water is converted to water
vapour (vaporization) and removed from the
evaporating surface (vapour removal). Water
evaporates from a variety of surfaces, such as
lakes, rivers, soil and wet vegetation. (b)
Transpiration consists of the vaporization of
liquid water contained in plant tissues and the
vapour removal to the atmosphere. Plants
predominately lose their water through stomata
(stomata are small openings on the plant leaf
through which gases and water vapour pass).
Nearly all water taken up by plants is lost by
transpiration and only a tiny fraction is used
within the plant.
Evaporation and transpiration occur
simultaneously and there is no easy way of
distinguishing between the two processes. The
driving force to remove water vapour from the
evaporating surface is the difference between the
water vapour pressure at the evaporating surface
and that of the surrounding atmosphere.
1.1 Factors affecting evapotranspiration:
(i) Weather parameters: Net radiation, air
temperature, humidity and wind speed.
(ii) Crop factors: Differences in resistance
to transpiration, crop height, crop
roughness, albedo and crop rooting
characteristics.
(iii) Environmental conditions: Ground
cover, plant density and the soil water
content.
The evaporation process over any vegetated
landscape is linked by two fundamental
equations:
(a) Water balance: Evapotranspiration can
be determined by measuring the various
components of the soil water balance.
This approach consists of assessing the
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incoming and outgoing water flux into
the crop root zone over some time period.
P = Eact + Q + ∆S
Where, P is rainfall, Eact is actual
evaporation, Q is runoff and ∆S is change
in moisture storage of soil.
(b) Energy balance: Evaporation of water
requires relatively large amounts of
energy. The process of evaporation is
governed by energy exchange at the
vegetation/soil surface and is limited by
the amount of energy available. The
amount of energy arriving at a surface
must equal the energy leaving the surface
for the same time period.
R = H + λEact + G
Where, R is net radiation received at
surface, H is sensible heat flux, λEact is
outgoing energy as actual evaporation
and G is heat conduction into soil. λ is the
latent heat of vaporization.
ET rate is expressed as millimeters (mm) per unit
time. This rate expresses the amount of water lost
from a surface in units of water depth. For e.g. if
we say that ET is 1 mm/day it implies that 10
cubic meter of water is lost per hectare per day
from that place. In terms of energy, 2.45 MJ of
energy is required to vaporize 1 kg water at 20oC.
This is the value of latent heat of vaporization i.e.
the amount of energy required to vaporize 1 kg of
water at given temperature.
2. Measurement and Estimation of ET
ET can be measured at field level using
instruments like lysimeters, evaporimeters, eddy
flux towers etc. However, these are expensive
and cumbersome to maintain and provide point
measurements of ET which is not valid over
larger areas. To overcome this issue, various
models have been developed over the years for
estimation of ET. These models range from
simple empirical equations to the much advanced
radiation based models used today. Most of these
models are region-specific and the empirical
coefficients apply to those locations only limiting
their use in other regions. However, these models
have been widely used by hydrologists all around
the world for estimation of ET in their study area
and later calibrated for those regions.
Before proceeding to the various models
employed for estimating ET, it is necessary to
define some of the basic terms used frequently in
ET modelling. The terms and their short forms
given below will be used throughout this section.
Potential Evapotranspiration (PET): Dingman
(1992) defines PET as the rate at which
evapotranspiration would occur from a large area
completely and uniformly covered with growing
vegetation which has access to an unlimited
supply of soil water, and without advection or
heating effects.
Reference crop Evapotranspiration: It is the
evapotranspiration from a crop with specific
characteristics and which is not short of water.
FAO-56 adopts the specific characteristics of a
reference crop with certain height (0.12 m),
surface resistance (70 s m-1) and albedo (0.23)
and then determines the reference ET using the
Penman-Monteith Equation.
Actual Evapotranspiration (AET): AET is
defined as the quantity of water transferred as
water vapour to the atmosphere from an
evaporating surface. This surface can refer to
anything from the real world eg open lake, bare
soil, vegetated surface etc.
2.1 Models for estimating PET
A brief description of some of the most widely
used models for estimating potential and crop
reference ET is given below:
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(a)Thornthwaite (1948): In the Thornthwaite
evaporation method, the only meteorological data
required to compute mean monthly potential
evapotranspiration is mean monthly air
temperature. It is expressed by:
𝐸𝑡ℎ,𝑗 = 16 (ℎ𝑟
12) (
ⅆ𝑎𝑦
30) (
10𝑇𝑗
𝐼)
𝛼𝑡ℎ
Where, Eth,j is the estimate of PET for month j, hr
is the mean daylight hours in month j, day is
number of days in month j, Tj is the mean
monthly air temperature (in oC) and I is annual
heat index. Once the mean monthly temperature
is known, the mean monthly PET can directly be
derived for each of the twelve months of a year.
(b)Penman (1948): Penman used an energy
equation based on net incoming radiation. This
approach does not require the surface temperature
variable.
Where, EPen is daily PET from a saturated surface,
Rn is net daily radiation to the evaporating
surface, Ea is a function of daily average wind
speed and vapour pressure, ∆ is the slope of
vapour pressure curve at air temperature, γ is the
psychrometric constant and λ is the latent heat of
vaporization.
(c)Penman-Monteith (1981): This is the most
widely used model for estimating PET from a
vegetated surface. It is expressed as:
Where, ETPM is the Penman-Monteith PET, Rn is
net daily radiation at the vegetated surface, G is
soil heat flux, ρa is mean air density at constant
pressure, ca is specific heat of the air, ra is
aerodynamic resistance, rs is surface resistance.
FAO has provided an excellent guide for
estimating crop reference ET in agricultural
regions based on Penman-Monteith equations.
This was formulated to make a universal method
of estimating PET. It is called the FAO-56 model
and is represented by the equation:
Where ETRC is the reference crop ET, and Ta is
mean daily air temperature. All other variables
have the same meaning as mentioned in previous
models.
(d)Priestley-Taylor (1972): This model computes
PET in terms of energy fluxes without an
aerodynamic component using the following
equation:
Where, EPT is the Priestley-Taylor PET, αPT is the
Priestley-Taylor constant which was taken as
1.26 in the original.
(e)FAO-24 Blaney-Criddle: The FAO-24
Reference Crop version of Blaney - Criddle is
defined as:
Where, ETBC is the Blaney-Criddle reference crop
ET, RHmin is minimum relative daily humidity,
n/N is measured sunshine hours to possible
sunshine hours, py is percentage of actual daytime
hours for the day compared to the daylight hours
for the entire year. ei (i=0 to 5) are the empirical
coefficients.
(f)Turc (1961): The Turc method is one of the
simplest empirical equations used to estimate
reference crop ET.
Where, ETTurc is Turc reference ET, Rs is the
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incoming solar radiation and Ta is the average air
temperature.
(g)Hargreaves-Samani (1985): The HS equation
which estimates reference crop ET is as follows:
Where, ETHS is the reference crop ET, CHS is
empirical coefficient, Ra is extra-terrestrial
radiation, Tmax, Tmin and Ta are the maximum,
minimum and average air temperatures
respectively. This equation is used for weekly or
monthly time scales for better results.
(h)Morton (1985): Morton’s approach uses
iterative solution of the following two energy-
balance and vapour transfer equations for PET at
the equilibrium temperature.
Where ETMo is the Morton’s estimate of PET, fv is
vapour transfer coefficient and is a function of
atmospheric stability, ɛs is the surface emissivity,
σ is Stefan-Boltzmann constant, Te and Ta are
equilibrium and air temperatures. This is a part of
the CRAE model which computes both PET and
AET.
The above mentioned models showcase only a
few of the ones being used today for estimation
of PET. Hydrologists continuously make
improvements in these models to better represent
their study area by conducting vigorous
calibration or introducing subsequent correction
terms.
2.2 Models for estimating AET
Once the potential of evaporation is determined
at a place using weather data, AET or actual
evapotranspiration is computed by incorporating
the limiting factors like actual soil moisture, state
of vegetation on ground etc. The following
models are used regularly for AET estimation:
(a)Morton models: Bouchet (1963) stated that
PET and AET depend on one another via
feedback from land and atmosphere
simultaneously, given that the area is sufficiently
large and homogeneous with no or little advective
heat and moisture. This complementary
relationship (CR) is given by:
ETAct = 2 ETWet - ETPot
ETwet is potential or wet environment
evapotranspiration and ETpot is the point
potential ET at a place whose area is so small that
its heat and water vapour fluxes have no effect on
the overpassing air.
Morton’s CRAE (Complementary Relationship
Areal Evapotranspiration) model computes AET
for land environments. It calculates point
potential ET (ETPot) using the equation in
previous section and wet-environment areal
evapotranspiration or ETwet by modifying the
Priestley-Taylor approach as:
Where, ETwet
Mo is wet-environment areal ET, Rne
is net radiation for the soil/plant surface at
equilibrium temperature, p is atmospheric
pressure and ∆e is slope of saturation vapour
pressure curve at equilibrium temperature, b1 and
b2 are empirical coefficients.
(b)Chen and Dudhiya (2001): This was
developed as a part of a coupled land surface-
hydrology model in the Penn state-NCAR fifth-
generation Mesoscale Model (MM5). The PET is
computed using Penman-based energy balance
approach. AET is computed as sum of direct
evaporation from soil, wet canopy evaporation of
intercepted water and canopy transpiration. These
components are derived as a fraction of PET
based on the vegetation fraction, canopy
resistance and few other parameters.
𝐸𝑑𝑖𝑟 = (1 − 𝜎𝑓)𝛽𝐸𝑃
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β =θ − θw
θref − θw
𝐸𝐶 = 𝜎𝑓𝐸𝑝 (𝑤𝐶
𝑠)
𝑛
𝐸𝑡 = 𝜎𝑓𝐸𝑝𝐵𝐶 (1 − (𝑤𝐶
𝑆)
𝑛)
Where, Edir is the direct evaporation from soil, σf
is the vegetation fraction, Ep is potential ET, β
determines the availability of water for
evaporation as a function of soil moisture θ, θref
and θw are field capacity and wilting point for that
soil type. Ec is wet canopy evaporation
(intercepted water), Et is canopy
evapotranspiration, Wc is intercepted canopy
water content, S is maximum canopy capacity, Bc
is a function of canopy resistance.
(c)FAO-56: FAO-56 method (Allen et al 1998)
for estimation of ET in agricultural areas involves
the computation of PET using Penman-Monteith
approach and then experimentally determined
ratios of ETc/ETo, called crop coefficients (Kc),
are used to relate AET to PET.
𝐸𝑇𝑐 = 𝐾𝑐𝐸𝑇0
Where, ETc is the crop ET, Kc is crop coefficient
and ETo is potential ET. Due to variations in the
crop characteristics throughout its growing
season, Kc for a given crop changes from sowing
till harvest. The effects of characteristics that
distinguish field crops from the reference grass
crop are integrated into the crop coefficient Kc.
Instead of using one value of Kc, dual crop
coefficients can also be used to distinguish soil
and crop ET. FAO-56 provides a handbook
detailing the entire procedure and experimental
values of Kc for different crops at various stages
of growth. This is the most widely used procedure
for estimating ET throughout the world.
Other notable models for AET include Granger-
Gray model (1989) and Szilagyi-Jozsa model
(2008). For more details on the above mentioned
models, including solved examples, please refer
to MacMahon et al 2013 (main paper and
supplementary material).
3. Remote Sensing and ET
In the previous section, many widely used models
were discussed in brief. When we wish to apply
such models over a large area, say a state or a
country, it becomes impractical to use
meteorological data from ground stations for ET
estimation as they are not well distributed over
the country. Remote sensing satellites orbiting
around Earth can provide a holistic view of an
entire region and can be used to estimate various
meteorological parameters. These satellite
observations are well distributed over space and
time and hence prove to be a viable tool for
estimating ET for large regions.
Satellites DO NOT provide any direct measure of
ET. Instead, they measure different
meteorological variables and land surface /
vegetation parameters that are then used in
different models for estimation of ET.
Space-based remote sensing satellites are
categorized based on their orbits into two types:
polar orbiting and geostationary. Polar-orbiting
satellites (e.g. ResrouceSAT-2, LANDSAT
series, Aqua/Terra-MODIS etc.) are placed
usually at ~500-800 km altitude and move around
the earth to capture images of the whole earth
surface. But the revisit time (i.e. the time interval
between successive observations of same region
on Earth’s surface) of such polar satellites is
anywhere from 2 days to 30 days depending on
the swath covered. These satellites provide
variables like leaf area index, snow cover, land
use-land cover etc. which can be used in the
models mentioned in previous section.
Geostationary satellites stay stationary relative to
a fixed point on earth surface at an approximate
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altitude of 36,000 km. These type of satellites
provide continuous data recording over a fixed
coverage area at frequent time intervals.
Geostationary Indian weather satellites such as
Kalpana-1, INSAT 3D and INSAT 3DR estimate
parameters like rainfall and earth’s radiation
budget using its various spectral channels and
provide data every 30 minutes. This data can be
used to estimate parameters like land surface
temperature, down-welling shortwave radiation,
upwelling longwave radiation, cloud cover etc. to
determine the surface energy budget and then be
implemented in the PET estimation models.
A few of the notable remote sensing based ET
estimation methodologies are the MOD16
MODIS ET algorithm (Mu et al 2007,2011),
SEBEL (Bastiaanssen et al 1998, Bastiaanssen
2000), METRIC (Allen et al 2007) and
methodologies by Batra et al 2006, Cleugh et al
2007, Yao et al 2013 etc. For further study on
remote sensing based ET estimation please refer
to the above papers. Following are two case
studies of operational ET estimation methods
using satellite data.
3.1 Case Study: MOD16 Algorithm
Estimation of PET/AET from space based
platforms can be best understood with the help of
a case study. NTSG employs a standard
methodology to compute ET using MODIS
(Moderate-resolution Imaging Sensor) onboard
Terra/Aqua satellites (Mu et al 2007, 2011).
This methodology uses the following set of
inputs:
(a) Remote-Sensing Derived Inputs: Land cover,
Leaf Area Index (for vegetation fraction),
Albedo, FPAR (Fraction of absorbed
Photosynthetically Active Radiation). These
products are available at 8/16 day intervals
and derived from MODIS.
(b) Meteorological Inputs: Air pressure, air
temperature, humidity, solar radiation. These
inputs are taken at daily basis from global
weather forecasting models calibrated against
ground stations.
The first step involves partitioning of incoming
solar radiation into net radiation available to
plants and net radiation to soil. This is done by
computing the vegetation fraction using the LAI
parameter. Potential soil evaporation is computed
using the Penman-Monteith approach which
utilizes meteorological parameters.
Plant transpiration is computed by first estimating
canopy conductance, water cover fraction,
aerodynamic resistance and plant intercepted
radiation. Then, plant transpiration is computed
using a Penman-Monteith based approach.
𝜆𝐸𝑡𝑟𝑎𝑛𝑠
=(𝑠 𝐴𝑐𝐹𝑐 + 𝜌 𝐶𝑝(𝑒𝑠 − 𝑒)
𝐹𝑐𝑟𝑎
) ∗ (1 − 𝐹𝑤𝑒𝑡)
𝑠 + 𝛾 (1 + 𝑟𝑠
𝑟𝑎⁄ )
Where, s is slope of saturated vapour pressure
curve, Ac is energy available to plant canopy, Fc
is vegetation fraction, Cp is specific heat of air, es
is saturated vapour pressure, rs is surface
resistance and ra is aerodynamic resistance, Fwet is
wet surface fraction and is a function of relative
humidity.
Wet canopy evaporation is computed by using
LAI and wet fraction to estimate wet canopy
aerodynamic resistance. Following the Biome-
BGC model λEwet_can is computed.
The soil surface is divided into saturated surface
and moist surface by the parameter Fwet. The
potential soil evaporation is computed as sum of
evaporation from saturated and moist soil
surfaces. The actual soil ET is computed using the
complementary hypothesis by Bouchet (1963).
𝜆𝐸𝑠𝑜𝑖𝑙 = 𝜆𝐸𝑤𝑒𝑡_𝑠𝑜𝑖𝑙 + 𝜆𝐸𝑝𝑜𝑡_𝑠𝑜𝑖𝑙(𝑅𝐻
100)𝑉𝑃𝐷/𝛽
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Where, 𝜆𝐸𝑠𝑜𝑖𝑙 is total actual soil ET, 𝜆𝐸𝑤𝑒𝑡_𝑠𝑜𝑖𝑙 is
AET due to wet soil, 𝜆𝐸𝑝𝑜𝑡_𝑠𝑜𝑖𝑙is the potential ET
from moist soil surface (unsaturated), RH is
relative humidity in %age, VPD is vapour
pressure deficit and β is set as 200.
The total daily ET (λE) is computed as sum of
evaporation from wet canopy surface, the
transpiration from dry canopy surface and
evaporation from soil surface.
𝜆𝐸 = 𝜆𝐸𝑤𝑒𝑡_𝑐𝑎𝑛 + 𝜆𝐸𝑡𝑟𝑎𝑛𝑠 + 𝜆𝐸𝑠𝑜𝑖𝑙
This MODIS ET product has been extensively
validated by using Eddy covariance towers from
FLUXNET (Mu et al 2011). The following global
map of mean annual ET during 2000-2006 was
produced by the NTSG using the above
mentioned algorithm.
Fig.1. Mean annual ET during 2000-2006
(Adapted from Mu et al 2011).
Fig 2. Flowchart of MODIS ET algorithm
(Adapted from Mu et al 2011).
3.2 Case study: Indian Perspective
Bhattacharya et al (2010) developed a simplified
single-source energy balance scheme to estimate
ET. Indian geostationary satellite Kalpana-1’s
Very-High Resolution Radiometer (VHRR) data
was used to obtain the major inputs for the ET
model, namely Land Surface Temperature (LST),
surface albedo, insolation and air temperature.
This methodology is based on an energy balance
approach. The available energy at surface is given
as sum of latent and sensible heat fluxes. A
parameter called evaporative fraction (Λ) is
introduced as the ratio of latent flux and total
available energy.
Λ = 𝜆𝐸
(𝜆𝐸 + 𝐻)
Subsequently, λE is estimated by multiplying the
net energy with this evaporative fraction term.
𝜆𝐸 = (𝑅𝑛 − 𝐺) 𝛬
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Λ term is computed using the relationship
between LST and albedo and their relationship
with soil moisture. For wet soil, its albedo is 2-3
times lower compared to dry soil. Surface albedo
determines the outgoing shortwave radiation.
This method uses a third order polynomial
relationship between LST, Soil moisture and
albedo as stated in Bastiaanssen et al (1998).
Fig 3. A concept figure of LST (TS) vs surface
albedo curve. At a given albedo, Dry edge (DC)
and wet edge (EF) represent maximum (TH) and
minimum (TE) LST lines. (Adapted from
Bhattacharya et al 2010).
The evaporative fraction at a given time in day
(noontime) is approximated by:
𝛬 =𝑇𝐻 − 𝑇𝑆
𝑇𝐻 − 𝑇𝐸
This term is multiplied to the net radiation to
obtain actual ET over Indian region. Figure 4
shows the output of this methodology at 0.08o
resolution.
Figure 4. Estimated AET in mm/day for India for
two 8-day periods in Nov and Dec 2005. Adapted
from Bhattacharya et al 2010.
4. Future Prospects
Most models for ET estimation rely on other
proxy parameters to solve one of the two
fundamental equations. This results in significant
errors in estimated ET.
The state-of-art techniques in the field of ET, for
e.g. Jasechko et al 2013, use the stable isotope
ratios of oxygen (18O/16O) and hydrogen (2H/1H)
to separate transpiration from evaporation. It
relies on the fact that evaporation process results
in enrichment of heavy isotopes of O and H in the
leftover water. However, transpiration process
does not produce fractionation of these isotopes.
Their analysis of catchments of some major lakes
of the world has shown that terrestrial water flux
is dominated by transpiration and not
evaporation.
ET plays a major feedback role in the land-
atmosphere system, thus affecting the global
climate. As the climate warms up, it is expected
that global evaporation losses will increase.
However, in a recent study by Jung et al 2010, the
authors have shown that there is indeed a decline
in global land ET owing to reduced moisture
supply. They computed and analyzed global ET
data for 27 years using integrated flux tower
measurements and remote sensing inputs. Also,
they related the impact of major El-Nino to the
changes in spatio-temporal behavior of ET.
Upcoming ISRO mission, GISAT (Geostationary
Imaging SATellite), is an advanced earth-
observation satellite. As the name suggests,
GISAT will be a geostationary satellite providing
high resolution multi-spectral and hyper-spectral
observations over India in optical, near-infrared
and thermal wavelengths, multiple times in a day.
This satellite will be capable of providing all the
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necessary parameters required for estimation of
ET over India on a daily basis. This will greatly
improve the evaporation estimates over Indian
region and will help us better understand ET and
its associated processes.
Evapotranspiration is not easy to measure and
even today it has huge potential for
improvements. Symons in 1867 best described
estimating evaporation as “...the most desperate
art of the desperate science of meteorology”
(Monteith, 1997). With the little we know and the
vast unknowns left to explore, ET is definitely
one of the most challenging and exciting
branches of Hydrology.
5. References
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driven Estimation of Terrestrial Latent Heat
Flux in China Based on a Modified Priestley-
Taylor Algorithm. Agricultural and Forest
Meteorology. 171, 187-202.
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WATER QUALITY MONITORING FROM SPACE
ASHWIN GUJRATI
Land Hydrology Division
Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)
Space Applications Centre, ISRO
Ahmedabad-380015
Water provides basic resources for various human uses and diverse habitat ecosystem services, supporting
high levels of biodiversity. Thus, it is paramount that water quality is assessed every so often to determine
suitability and safety for varying purposes. Monitoring and understanding the water quality i.e. physical,
chemical and biological status of global water is immensely important to scientists and policy makers.
Key Words: Water Quality, water color, turbidity, radiative transfer.
1. Introduction
Water quality of any system can be measured by
the following physical, chemical and biological
parameters. The physical data includes, pH,
temperature, dissolved oxygen, turbidity, Secchi
depth, specific conductivity. Chemical analysis
includes concentrations of Silicate, Nitrate,
Nitrite, Ammonium, total Nitrogen and total
Phosphorus. Biological parameter includes
chlorophyll, dissolved organic matter and
suspended particulate matter.
The in-situ measurements of water quality are
often very scarce because of large areas to
monitor. Furthermore, these measurements do
not represent the actual water quality at a large
scale since measurements are restricted to
specific regions. Consequently, one may consider
measurement techniques so as to get relevant
information especially at large scales and to be
able to characterize water quality over a whole
region. In this context, remote sensing from space
is a perfect tool to get the required information.
Satellite data may be able to provide a greater
amount of spatial information at an improved cost
compared to spot sample grabs.
1.1 Remote sensing of water
Conventional monitoring approaches tend to be
limited in terms of spatial coverage and temporal
frequency. Remote sensing has the potential to
provide an invaluable complementary source of
data at local to global scales. But remote sensing
of water can measure only those water quality
parameters that have optically active constituents.
Constitutes that interact with light and changing
the energy spectra of reflected solar radiation
emitted from surface waters (Ritchie et al., 2003).
These include phytoplankton pigments
(chlorophylls, carotenoids, phycocyanin, etc.),
colored dissolved organic matter (CDOM), and
inorganic and non-living suspended matter,
which coincide well with the previously
mentioned parameters determining the majority
of water quality issues in inland waters.
The measured radiance originates from sunlight
that passes through the atmosphere, is reflected,
absorbed, and scattered by constituents in the
water bodies, and is transmitted back through the
atmosphere to the satellite-based sensor (Fig. 1)
(http://www2.dmu.dk/resc-
oman/project/Backgrounds/challenges.htm). The
processes of scattering and absorption by
optically active constituents in the water affect
the spectrum and radiance distribution (light
field) of the light emerging from the water – the
so called water-leaving radiance. The scattering
and absorption characteristics of water and its
constituents are described as the inherent optical
properties (IOPs). The spectral quality and
quantity of the water-leaving radiance is largely
determined by the inherent optical properties.
The modification/alteration of the radiance has
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been used to determine water constituents,
typically the desired parameter has been the
chlorophyll-a concentration, colored dissolved
organic matter (CDOM), and inorganic and non-
living suspended matter. In essence, water colour
is determined by inherent optical properties.
Fig.1: Schematic diagram of Remote sensing of
water
Inherent optical properties (IOP's) are those
properties that depend only upon the medium,
and therefore are independent of the ambient light
field within the medium. The two fundamental
IOP's are the absorption coefficient and the
volume scattering function. Other IOP's include
the index of refraction, the beam attenuation
coefficient and the single-scattering albedo.
Apparent optical properties (AOP's) are those
properties that depend both on the medium (the
IOP's) and on the geometric (directional)
structure of the ambient light field, and that
display enough regular features and stability to be
useful descriptors of the water body. Commonly
used AOP's are the irradiance reflectance, the
average cosines, and the various diffuse
attenuation coefficients.
Remote sensing of water is broadly divided into
retrieval over Case 1 waters and Case 2 waters.
Case 1 waters are waters in which the
concentration of phytoplankton is high compared
to non-biogenic particles. Absorption by
chlorophyll and related pigments therefore plays
a major role in determining the total absorption
coefficient in such waters, although detritus and
dissolved organic matter derived from the
phytoplankton also contribute to absorption in
case 1 waters. Case 1 water can range from very
clear (oligotrophic) water to very turbid
(eutrophic) water, depending on the
phytoplankton concentration. Case 2 waters are
"everything else," namely waters where inorganic
particles or dissolved organic matter from land
drainage dominate, so that absorption by
pigments is relatively less important in
determining the total absorption.
2. Literature Review
Two types of methods are commonly used for
interpreting water quality from remotely sensed
data: empirical and analytical approach (Bhatti et
al. 2010; Cannizzaro and Carder 2006; Giardino
et al. 2007; Kallio 2000; Knaeps et al. 2010;
Ritchie et al. 2003). The empirical based
approaches are most commonly used method
which are determined through statistical
relationships between measured spectral
properties (i.e. radiance or reflectance) versus the
measured water quality parameter of interest (e.g.
Lee et al. 1996; Garver & Siegel, 1997; Hoge &
Lyon, 1996, 2005; Le et al., 2009a; Ritchie et al.
2003; Wang et al., 2005; Bhatti et al. 2010).
Usually algorithm development searches for a
combination of radiance signals at several
wavelengths to find ratio, or other combination,
that relates radiance at particular band
empirically to the desired water quality
parameter. The coefficients contained in these
algorithms are generally derived by pooling data
collected at various spatial and temporal scales.
Empirical approaches are region dependent, that
works better for one site but may fail on other site.
On the other hand, analytical algorithms are
based on radiative transfer equations works
equally well for different water bodies and
usually perform better than the empirical
algorithm (L. Li el al.2013).
Recently many analytical and semi-analytical
algorithms for inland waters are developed for
retrieving inherent optical properties from remote
sensing reflectance. Gege (2012) developed an
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analytic model for the direct and diffuse
components of the down-welling irradiance in the
water column. Giardino et al. (2012) developed a
software package incorporating their Bio-Optical
Model Based tool for Estimating water quality
and bottom properties from Remote sensing
images (BOMBER). Brando et al. (2012) present
an adaptive implementation of the linear matrix
inversion (LMI) method which accounts for
variability in both IOPs and mass-specific IOPs
(SIOPs) over space and time in wide-ranging
optically-complex waters. More sophisticated
neural network and physics-based inversion
methods have also been used to estimate in-water
inherent optical properties (IOPs) (Odermatt et
al., 2012). Salama and Verhoef (2014) present a
new, forward model analytical inversion solution
(“2SeaColor”) for the retrieval of the depth
profile of the down-welling diffuse attenuation
coefficient.
Remote sensing of inland water bodies poses a
challenge due to its highly complex optical nature
as compared to clear marine waters. Simulated
remote sensing reflectance spectra of water with
different water quality parameters plotted with
their true color is shown in figure 2. The optical
complexity of inland waters stems from the fact
that these waters are typically characterized by
high concentrations of phytoplankton biomass
(typically on the order of between 1 and 100 mg
m−3 chlorophyll-a (chl-a), and up to 350 mg m−3
(Gitelson et al., 1993), mineral particles, detritus
and CDOM that typically do not co-vary over
space and time. Moreover, their optical properties
are highly variable between and even within
water bodies.
Fig.2: Remote sensing reflectance spectra of
different water color.
Table 1: Variables that affect the water quality
parameters
Variables that
can affect
remote sensing
of physical
water-quality
characteristics
Variable Explanation
Time of year
The Earth receives 7 per
cent more energy from the
sun on 1 January than on 1
July because of an oval
orbit.
Sun-elevation
angle
More solar energy is
specularly reflected from
water surfaces at low sun-
elevation angles than at
high angles. Also,- the
path length of solar energy
through the atmosphere is
longer at low sun-
elevation angles, and more
solar energy is absorbed
and scattered.
Aerosol and
molecular content
of atmosphere
These constituents
determine the amount of
solar energy absorbed and
scattered by the
atmosphere. Some energy,
received by a satellite, is
backscattered before
reaching the water
surface.
Water-vapour
content of the
atmosphere
Water vapour affects
energy absorption at near
infrared and thermal
infrared wavelengths.
Specular
reflection of
skylight from
water surface
Specularly reflected
skylight is received by a
satellite. The intensity and
wavelength distribution of
this energy depends on
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atmospheric scattering,
which produces skylight.
Roughness of
water surface
A rough surface may
produce more or less
specular reflection than a
smooth surface. At high
sun-elevation angles, the
area of sun glint may be
within the satellite fields
of view.
Film, foam,
debris, or floating
plants on water
surface
These features may not be
resolved on a satellite
image, but they contribute
to the spectral
characteristics of the
measured signal.
Water colour
Dissolved, coloured
materials increase
absorption of solar energy
in water.
Water turbidity
The concentration, size,
shape, and refractive
index of suspended
particles determine
turbidity and increase the
amount of energy
backscattered in water
bodies.
Reflectance and
absorptance
characteristics of
suspended
particles
Particles may be inorganic
sediments, phytoplankton,
zooplankton, or a
combination. When
present in high
concentrations, particles
affect the spectral
distribution of
backscattered energy.
Multiple
reflections and
scattering of solar
energy in water
The spectral results of
these processes are
difficult to predict, but
may not be important.
Depth of water
and reflectance of
bottom sediments
Water clarity determines
the importance of bottom
reflectance. Solar energy
may not reach bottom in a
turbid water.
Submerged or
emergent
vegetation
Vegetation may change
bottom reflectance,
obscure water surface, or
contribute to the spectral
characteristics of the
measured signal.
3. Radiative Transfer Model
In optically shallow waters, the upwelling
irradiance just below the surface, Eu(0), results
from adding the flux backscattered by the water
column and the flux reflected by the bottom
substrate and then transmitted through the
column as shown in fig 3 below,
𝐸𝑢(0) = [𝐸𝑢(0)]𝐶 + [𝐸𝑢(0)]𝐵
The subscripts C and B stand for water column
and bottom. The first component corresponds to
the photons that have never interacted with the
bottom, whereas those that have interacted with
the bottom at least once form the second
component.
Fig.3: Schematic diagram of radiative transfer
model
To estimate the first component on RHS, we
consider an infinitely thin layer of uniform
thickness dZ at depth Z. At this level, the down-
welling irradiance is Ed(Z). The backscattering
coefficient (or reflectance function) for the down-
welling light stream is denoted bbd; the fraction of
upwelling irradiance created by this layer is:
𝑑𝐸𝑢(𝑍) = 𝑏𝑏𝑑𝐸𝑑(𝑍)𝑑𝑍
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Ed(Z) can be expressed as
𝐸𝑑(𝑍) = 𝐸𝑑(0)exp(−𝐾𝑑𝑍)
Ed(0) is the downwelling irradiance at null depth,
and Kd is the diffuse attenuation for downwelling
irradiance. Before it reaches the surface, dEu(Z)
suffers an attenuation along the path from Z up to
0, expressed by exp(-kZ), where k is the vertical
diffuse attenuation coefficient for upward flux.
This coefficient refers to an exponential
attenuation (with distance travelled upward) of
the upward flux while travelling upward
originating from any thin layer. The contribution
of the considered layer to the upwelling
irradiance just below the surface is denoted
dEu(Z0); this term is expressed as
𝑑𝐸𝑢(𝑍 → 0) = 𝑏𝑏𝑑𝐸𝑑(0) exp[−(𝐾𝑑 + 𝑘)𝑍] 𝑑𝑍
The contributions of each layer between Z and 0
in forming Eu(0, Z) can be summed, so that
𝐸𝑢(0, 𝑍) = 𝑏𝑏𝑑𝐸𝑑(0)
× ∫ exp[−(𝐾𝑑 + 𝑘)𝑍] 𝑑𝑍𝑍
0
𝐸𝑢(0, 𝑍) = (𝐾𝑑 + 𝑘)−1𝑏𝑏𝑑𝐸𝑑(0) × [1− exp−(𝐾𝑑 + 𝑘)𝑍]
For an infinite water depth (Z=∞), above equation
reduces to
𝐸𝑢(0,∞) = (𝐾𝑑 + 𝑘)−1𝑏𝑏𝑑𝐸𝑑(0)
𝐸𝑢(0, 𝑍) = 𝑅(0,∞)𝐸𝑑(0)
𝑅(0,∞) =𝑏𝑏𝑑
(𝐾𝑑 + 𝑘)
R(0,∞)represents the reflectance at null depth of
the deep ocean, hereafter denoted R∞. For a
column limited by the presence of a perfectly
absorbing bottom at a depth H,
𝐸𝑢(0, 𝐻) = 𝑅∞𝐸𝑑(0)× [1 − exp−(𝐾𝑑 + 𝑘)𝑍]= [𝐸𝑢(0)]𝐶
and thus provides the first term in first equation.
If the bottom is a Lambertian reflector with an
albedo A, the reflected flux at level H (i.e.
immediately above the bottom) is
[𝐸𝑢(𝐻)]𝐵 = 𝐴 ×𝐸𝑑(𝐻)= 𝐴 × 𝐸𝑑(0)exp(−𝐾𝑑𝐻)
This contribution of the bottom to the upwelling
irradiance will be attenuated from H up to the
surface. If we suppose that this upward flux is
attenuated with the same K as above, the
contribution of the bottom to the upward
irradiance reaching the surface becomes
[𝐸𝑢(𝐻)]𝐵 = 𝐴 × 𝐸𝑑(0)exp[(−𝐾𝑑 + 𝑘)𝐻]
By adding, we obtain
𝐸𝑢(0) = 𝐸𝑑(0)(𝑅∞ × [1 − exp−(𝐾𝑑 + 𝑘)𝑍]+ 𝐴 × exp[(−𝐾𝑑 + 𝑘)𝐻])
Dividing by Ed(0) and rearranging, the
reflectance, R(0, H), below the surface of a
homogeneous ocean bounded below by a
reflecting bottom at depth H, is
𝑅(0,𝐻) = 𝑅∞ + (𝐴 − 𝑅∞)exp[(−𝐾𝑑 + 𝑘)𝐻]
To the extent that the two kinds of upward fluxes,
either scattered by the series of thin layers or
reflected by the bottom, do not have the same
geometrical structure, they are not attenuated in
the same way. If KB and KC denote the attenuation
coefficients for the upward streams originating
from the bottom and from the water column
respectively, Equation must be written as
𝑅(0,𝐻) = 𝑅∞ + exp(−𝐾𝑑H) ×[Aexp(−𝐾𝐵𝐻)− 𝑅∞exp(−𝐾𝐶𝐻)]
Authors have simplified the above equation for
implementation with various approximations
based on their study area and practice difficulties
of measurement. The coefficients were derived
using either Monte Carlo or hydro-light
simulations.
4. References
1. Bhatti, A.M., Schalles, J., Rundquist, D.,
Ramirez, L & Nasu, S. (2010). Accuracy
2010 Symposium, July20-23, Leicester, UK
2. Brando, V.E., Dekker, A.G., Park, Y.J., &
Schroeder, T. (2012). Adaptive
semianalytical inversion of ocean color
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radiometry in optically complex waters.
Applied Optics, 51(15), 2808–2833.
3. Cannizzaro, J. P., and K. L. Carder. 2006.
Estimating chlorophyll a concentration from
remote-sensing reflectance in optically
shallow waters. Remote Sensing of
Environment 101 (1): 13-24.
4. Garver, S. A., & Siegel, D. A. (1997).
Inherent optical property inversion of ocean
color spectra and its biogeochemical
interpretation .1. Time series from the
Sargasso Sea. Journal of Geophysical
Research-Oceans, 102, 18607–18625.
5. Gege, P. (2012). Analytic model for the
direct and diffuse components of
downwelling spectral irradiance in water.
Applied Optics, 51(9), 1407–14019.
6. Giardino, C., V. E. Brando, A. G. Dekker, N.
Strombeck, and G. Candiani. 2007.
Assessment of water quality in Lake Garda
(Italy) using Hyperion. Remote Sensing of
Environment 109 (2): 183-195.
7. Gitelson, A., Garbuzov, G., Szilgyi, F.,
Mittenzwey, K. -H., Karnieli, A., & Kaiser,
A. (1993). Quantitative remote sensing
methods for real-time monitoring of inland
waters quality. International Journal of
Remote Sensing, 14, 1269–1295.
8. Hoge, F. E., & Lyon, P. E. (1996). Satellite
retrieval of inherent optical properties by
linear matrix inversion of oceanic radiance
models: An analysis of model and radiance
measurement errors. Journal of Geophysical
Research-Oceans, 101, 16631–16648.
9. Kallio, K. 2000. Remote sensing as a tool for
monitoring lake water quality. In
Hydrological and limnological aspects of
lake monitoring, ed. P. Heinonen, G. Ziglio,
and A. van der Beken, 237-245. Chichester,
England: John Wiley & Sons, Ltd.
10. Knaeps, E., D. Raymaekers, S. Sterckx, and
D. Odermatt. 2010. An intercomparison of
analytical inversion approaches to retrieve
water quality for two distinct inland waters.
In Proceedings of the Hyperspectral
Workshop. Frascati, Italy.
11. Le, C. F., Li, Y. M., Zha, Y., Sun, D. Y., &
Yin, B. (2009a). Validation of a quasi-
analytical algorithm for highly turbid
eutrophic water of Meiliang Bay in Taihu
Lake, China. IEEE Transactions on
Geoscience and Remote Sensing, 47, 2492–
2500.
12. Lee, Z. P., Carder, K. L., Peacock, T. G.,
Davis, C. O., & Mueller, J. L. (1996). Method
to derive ocean absorption coefficients from
remote-sensing reflectance. Applied Optics,
35, 453–462.
13. Odermatt, D., Gitelson, A., Brando, V.E., &
Schaepman, M. (2012). Review of
constituent retrieval in optically deep and
complex waters from satellite imagery.
Remote Sensing of Environment, 118, 116–
126.
14. Ritchie, J. C., P. V. Zimba, and J. H. Everitt.
2003. Remote sensing techniques to assess
water quality. Photogrammetric Engineering
& Remote Sensing 69 (6): 695-704.
15. Salama, M. S., & Verhoef, W. (2015). Two-
stream remote sensing model for water
quality mapping: 2SeaColor. Remote
Sensing of Environment, 157, 111-122.
16. Wang, P., Boss, E. S., & Roesler, C. (2005).
Uncertainties of inherent optical properties
obtained from semianalytical inversions of
ocean color. Applied Optics, 44, 4074–4085.
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Digital Image Processing
Vibhuti Bhushan JhaSpace Applications Centre, Ahmedabad
In this lecture we will try to look into the domain of image processing and the mathematicalformulation of some image processsing tools. We look into different statistical image analysismethods and classification tools.
Introduction
Images obtained from space based observations openthe door for data and interpretations, the key to whichlies in the proper deciphering of the underlying processesand techniques. Image processing is a tool to extract thenot so obvious data. The objective of this lecture is tofamiliarize the audience with the mathematical foundationsof image processing and some basic image operations whichcan be utilised as of when needed in further lectures. Wewill look into different image operations like, sharpening,zooming, shrinking, filtering, histograms, classificationsetc. We will also look into the different types of imagesused in hydrological applications and how to extract somemeaningful information from them which will be followedby hands on tutorial on the use of different softwares andtechniques.This chapter has several objectives: (1) to definethe scope of the field that we call image processing; (2) togive a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing byexamining some of the principal areas in which it is applied;(4) to discuss briefly the principal approaches used in digitalimage processing; (5) to give an overview of the componentscontained in a typical, general-purpose image processingsystem.
What is an image?
An image can be considered as a 2 dimensional functionf (x, y) where x, y are the spatial coordinates and f is a scalarvalues real function ∈ R which is dependent on the source ofillumination. One of the benefits of data acquired from spacebased platforms is availability in digital format. Spatially thedata is composed of discrete picture elements called pixels.From the data handling and analysis point of view, the prop-erties of image data of significance are the number and lo-cation of the spectral measurements provided by a particularsensor, the spatial resolution as described by the pixel size,and the radiometric resolution. The later describes the rangeand the discernible number of discrete brightness values. It isalso sometimes called as dynamic range. It is also expressed
in terms of the number of bits required to represent the rangeof available brightness values. Hence, data with 8 bit ra-diometric resolution has 256 levels of brightness values. Asimilar situation applies when using microwave image data:viz, several transmission wavelengths can be used to assist inidentification of cover types by reason of their different scat-tering behaviours with wavelength. A useful property whichcan be utilised for the image analysis is polarisation. Thereare different techniques for analysis of SAR imagery, namely,interferometry, polarimetry etc.
Types of Spatial Data
Before we embark on this journey of processing, we needto know the types of spatial data which we encounter in satel-lite imagery. The data must be available in discrete formspatially that is corresponding to the pixels, with each pixeldescribing the properties at the ground. Secondly,the imagemust be georeferenced that is should correspond to proper latlong values. It has to be mutually registered and referencedto a a base map like UTM.
Image registration
It is important to georegister the image with some baseimage or map. This tries to establish a mathematical rela-tionships between the addresses of a pixel in an image andthe corresponding coordinates of those points on the groundvia a map. But this has an inherent assumption that a basemap is available of the desired location. A mapping functionf and g is required such that :
u = f (x, y), v = g(x, y) (1)
If the functions are known then we can locate a point on theimage knowing it’s position on the map and this process is in-vertible also. What we use for this is a mapping polynomialdefined by:
u = a0 + a1x + a2y + a3xy + a4x2 + a5y2 (2)v = b0 + b1x + b2y + b3xy + b4x2 + b5y2 (3)
Often the value of the coefficients is unknown andis assigned a value based on the knowledge of
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2 VIBHUTI BHUSHAN JHA
Ground control Points(G.C.Ps). From the image it iseasy to identify the GCPs as they are some time invariantfeatures, such as road intersections, airport runway, bendsin rivers etc. For hydrological applications it is sometimeseasier to identify meandering features and bends in therivers, as will discussed in the tutorial. From the equations,1.2, 3 it is trivial that we should identify atleast 6 groundcontrol points for the mapping to be unique for a secondorder polynomial registration.The coefficients are evaluatedusing the least square estimation method. The three mostused methods for resampling and interpolation of the pixelvalues are discussed below:
• Nearest Neighbour : This method simply choses theactual pixel that has its centre nearest to the point lo-cated in the image, which is then transferred to the dis-play grid location. This is advantageous in the sensethat the originality of the base images’ brightness isretained in terms of pixel values in the new image.
• Bilinear Interpolation :Let B be the pixel brightnessand (i, j), (i, j+1), (i+1, j), (i+1, j+1) be the locationsof the 4 pixels. Let j′ be the horizontal offset of themap point from the (i + 1, j)th pixel. Then the bilinearinterpolation technique uses three linear interpolationsover the four pixels surrounding the point correspond-ing to the given grid.
• Cubic Convolution: CC method uses the 16 surround-ing pixels along the 4 lines of the 4 pixels. The ac-tual form of the cubic polynomial employs techniquesfrom sampling theory and is far from the scope ofthe lecture. Since the interpolation uses convolutiontechniques, hence the name . The advantage with thismethod is that the image is generally smooth and usedfor photointerpretation, but is not used when our aimis to classify the data as the brightness values may befar off from the radiance obtained from the satellite.
Choice of Control points and georeferencing
As we have seen in the previous sections, it is necessaryto define enough control points so that the accurate mappingpolynomial is generated. As to where the points have tobe, it is advised that the points be distributed well along theedges of the image so that the mapping polynomials are wellbehaved over the image. A key point to note is that higherorder polynomials are effective in capturing the image nearthe control points but they differ markedly outside the GCPrange. Thus it is advised to use nearest neighbour or bilinearinterpolation techniques.
It is often the case that a scene is acquired over different datesand has to be processed simultaneously. As will be discussedin the tutorial we will georegister the images and it is usefulin preforming change analysis. One image can be kept asthe master image which is the base for georegistering andthe other as the slave image. Image to image registering hasthe advantage of saving time in registering to the base mapfor both the images. The algorithm used in the accurate colocation of points is called Sequential similarity detection.For hydrological applications, we take points which arejust near the land heads to minimise the error due to waterboundary shifting due to rainfall etc.
Image interpretation techniques
In this section we will look into different operationsrequired for image processing, we will look into thehistogram equalisation techniques, image sharpening, image
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classification etc to name a few.Pattern recognition comes toaid when we want to label and identify the pixels becausethe image comes in the form of spectral classes distributedover the image, our ultimate aim to extract the labels and doclassification, for this we have to first do some operations onthe image so that the classes can be visualised. After this weperform different classification techniques. The pixel vectorscontain the sets of brightness values for the pixels arrangedin the column form as:
x1x2...xn
Where x1, x2......xn are the pixel brightness value in thebands 1 to N respectively.
Histogram
Consider an image, the plot between the number of pixelsvs the brightness value gives us a 2D plot known as the his-togram. An image which spans the brightness values, the his-togram is equally distributed over the region. In this section,we will see some histogram modification techniques whichcan be used to change the contrast, brightness etc.
Contrast Modification
Suppose an image has a poor contrast and it is needed toenhance the image, then one of the easiest ways to do thisis to change the histogram using some mathematical oper-ations. For example an image may occupy the histogramrange between 20 − 60, but for clarity of the image , wewould like to increase the dynamic range to span the entiregray scale 0−255.While the number of bars in the histogramis not altered in this process, the location of the bars is re-specified favourably. The contrast modification process canbe specified as:
y = f (x) (4)
where x is the old brightness of a particular bar in the his-togram and y is the new value and f is the contrast modifica-tion function. The most common contrast modification oper-ation is the one in which the old and new values are relatedby a linear operation, which can be expressed in the form:
y = ax + b (5)
Sometimes a particular region in the image may occupy arestricted limit of values thus saturating linear contrast en-hancement increases the dynamic range in that region to userdefined Bmax and Bmin. Usually the remotely sensed data islow in brightness and poor in contrast, thus there is a need for
automatic contrast stretching in which the mean and stan-dard deviation of the brightness and adjusting the dynamicrange to µ ± 3σ range. Exponential and logarithmic contrastmodification is used for enhancing light and dark featuresrespectively. Another contrast modification method used isthe piecewise modification in which there are break points,where the function changes, it is upto the user to define thepoint and the number of points.
Histogram Equalization
While the previous sections have taken care of basic op-erations on the histograms, sometimes it is desirable that wematch or modify the histogram according to some base im-age. The number of pixels in the range y to y + δy in themodified histogram to match the base image in the range x tox + δx and hi(x) and ho(y) are density functions, which gives,
hi(x)δx = ho(y)δy (6)
Thus if we want to know the shape of the revised histogram,we get after performing some trivial inverse and differentialoperators,
ho(y) = hi( f −1(y))d f −1(y)
dy(7)
But, what will happen if the histogram is a discrete one in-stead of a continuous function. A way in is to find the cumu-lative histogram C(x) which is the sum of the preceding bars.The modified brightness is given by the relation:
y = λC(x) (8)
where λ is a constant. The range of values of y is from 0 toL − 1 for L values of brightness. Thus the constant’s value isL−1N . Following image shows the process of histogram equal-
isation according to a base image.
Image domain analysis
In the previous sections we have seen the pixel wise oper-ations whereas in the present section we will directly work in
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4 VIBHUTI BHUSHAN JHA
the image domain as whole, i.e, we will look into neighbour-hood operations. Sometimes it is necessary to assign valuesto a pixel based on the statistical properties of the neighbour-hood. Image analysis often requires methods like smoothing,edge detection and enhancement. Most of these methods re-quire defining a window or a box over a region which canthen be used to cover the entire image. This generates a ra-diometrically modified image. The response for any imagepixel is given by:
r(i, j) = ΣMm=1ΣN
n=1φ(m, n)t(m, n) (9)
Where φ(m, n) is the pixel brightness value and t(m, n) is thetemplate entry at that location. This can be viewed as a con-volution operator.
Mean value image smoothing
Satellite data many a times contain random noise super-imposed on the pixel brightness owing to noise generated bythe sensors. This shows up as salt and pepper. It can beremoved by the process of filtering and especially low passfilter. Mean value smoothing uses the simple average of pixelbrightness values inside the template, the template taken is:
t(m, n) =1
MN(10)
thus applying equatiion, 1.9,we get the response for the im-age.
r(i, j) =1
MNΣM
m=1ΣNn=1φ(m, n) (11)
Thus the pixel at the centre of the template is thus repre-sented by the average brightness level in a neighbourhooddefined by the template dimensions. The problem with thisapproach is that the information about edges which are highfrequency information is lost in this averaging method. Thiscan be taken care of by defining a threshold T such that:
ρi, j =1
MNΣM
m=1ΣNn=1φ(m, n) (12)
r(i, j) = ρ(i, j) : |φ(i, j) − ρ(i, j)| < T (13)= φ(i, j), otherwise (14)
Median filtering
Median filtering is a way to avoid the problems in meanmethod for the edges. The center pixel is assigned a medianvalue of the all the pixels covered in the template. It is fre-quently used in removing impulse related noises as they havean abrupt change for some pixels and their behaviour is wellcaptured if we look for the median values in the neighbour-hood,rather than the mean. Now since the median filtering isnot a linear function, it cannot be described by the convolu-tion operation.
Edge detection
One of the most important tasks in hydrological applica-tions is to find the edges or boundaries. Edge detection high-lights the edges by some gradient or masking methods. Inthis section, we will discuss the edge detection method.
Spatial derivatives
The vector gradient for an image can be defined for thebrightness function as:
∇φ(x, y) =∂
∂xφ(x, y)i +
∂
∂yφ(x, y)j (15)
In making DEMs, we are interested in the magnitude of thegradient,
|∇| =
√∇2
1 + ∇22 (16)
But we need to discretize the gradient operators so that wecan apply template wise processing.
∇1 = φ(i, j) − φ(i + 1, j + 1) (17)∇2 = φ(i + 1, j) − φ(i, j + 1) (18)
An advantage of defining the gradient operators in this wayis that we can detect horizontal, vertical as well as diago-nal edges. This operator is called the Roberts Operator asthe derivatives are defined for the point (i + 1
2 , j + 12 ) in the
diagonal derivatives. Other edge detection operator is theSobel operator, which is computationally more time con-suming because it calculated both for the horizontal as wellas vertical pixels, thus it is like computing a forward differ-ence scheme.
∇1 = ∇1a − ∇1b (19)
and∇2 = ∇2a − ∇2b (20)
where ∇1a = φ(i−1, j+1)+2φ(i−1, j)+φ(i−1, j−1),∇1b =
φ(i + 1, j + 1) + 2φ(i + 1, j) + φ(i + 1, j − 1) and similarlyfor ∇2a,b. Sobel operator can be thought of as the imple-mentation of following template in the image. Sometimes it
becomes necessary to extract information in the fourier do-main, i.e, the frequency domain. This is done by means offourier transformation. For example, a periodic noise can beremoved by working in the fourier domain rather the spatialdomain as it captures the periodicity pretty well. Familiarity
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with basic complex analysis and integral calculus is assumedin this section. The fourier transformation can be written as:
F(ω) =
∫ ∞−∞
f (t)e−iωtdt (21)
The impulse response relationship is given a convolution op-eration as:
y(t) =
∫ ∞−∞
x(τ)h(t − τ)dτ ∼ x(t) ∗ h(t) (22)
But to extend it to an image requires the use of double inte-grals as the image is two variable function.
Supervised Classification
The analysis of the image requires the extraction of sim-ilar features in the image, for example, a class of vegeta-tion, water bodies, forests etc. For this we need the conceptof classification, supervised and unsupervised in which weclassify the clusters according to some mathematical modelsespecially probabilistic models. Supervised classification in-volves probabilistic models in which the user labels the classand the total number of classes has to be determined. Usuallythe steps involved are:
• Decide the number of classes to be classified, an infor-mation about the segments of the image for example,water, urban regions, croplands.
• Training data is formed in which representative pixelsof a particular class are chosen.
• Estimate the probability models from the training dataand these equations will decide the signature of theclass.
• Finding the accuracy of the final product.
Bayes’ classification rule
The spectral classes of the image are ωi, i = 1, 2, ....,M,where M is the number of classes. We need to find the con-ditional probability of a pixel vector x, which is given by:
p(ωi|x), i = 1, 2, ...,M (23)
Now the pixel vector signifies the brightness values. Thisgives us the likelihood that the correct class is ωi correspond-ing to the pixel brightness vector x. The schema is:
p(ωi|x) > p(ω j|x)∀x ∈ ωi (24)
This figure illustrates the class assignment:
Maximum likelihood
The conditional probability distribution is given by a gaus-sian distribution in multivariate form:
p(x|ωi) = (2π)−N2 |Σ|−
12 e−
12 (x-mi)tΣ−1
i (x-mi)(25)
Where mi and Σi are class mean and covariance matrix re-spectively. The discriminant function is given by gi(x) =
ln(p(x|ωi)p(wi)). Thus the condition for belonging to a classis x ∈ ωi if gi(x) > g j(x)∀ j , i. Now the implementationof the maximum likelihood is to calculate the discriminantfunction and then label the classes based on it,
gi(x) = −ln|Σi| − (x-mi)tΣ−1i (x-mi)(26)
Unsupervised classification
In general it is not possible to discern the number of spec-tral classes present in an image. For this clustering can beused for unsupervised classification. In unsupervised clas-sification, pixels in an image are assigned to spectral classeswithout the user having beforehand knowledge of the classes.In this section we will discuss many clustering techniques.
Clustering criteria
Grouping of the pixels in a multispectral space is calledclustering. The idea is to generate some similarity mea-sure for clustering, this is done by making distance measures
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6 VIBHUTI BHUSHAN JHA
which in a way signify the distance between the pixels. It isa L1 measure and is given by:
d(x1, x2) = ΣNi=1|x1i − x2i | (27)
where x1i,2i are the ith component of the pixel vectors respec-tively. It is thus easy to form clusters based on this distancemeasure. After this we find the sum of sqaured errors (SSE)defined as:
S S E = ΣCiΣx∈Ci ||x −mi||2(28)
Migrating Means clustering
In this method, following steps are followed:
• N points are selected in multispectral space as a samplecluster centres.
• The location of each pixel is assigned to the nearestcluster based on a distance measure.
• The new means are computed, if this matches the ini-tial taken mean ,then the process terminates otherwiseis repeated again.
The most common algorithm for supervised classification ismaximum likelihood. The limitation of this model is that theclasses must be represented by a multivariate normal distri-bution. But most of the times the data is multimodal and thusclustering algorithms are needed for classification.
References
• Richards Jia, Remote sensing digital image analysis.
• Gonzalaez and Woods, Digital Image Processing.
• The images have been taken from Richards Jia, Re-mote Sensing digital image analysis.