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![NISAR Science Users’ Handbook NISAR Science Users’ Handbook
from the data. Subsequently, the structure function Sw
is
calculated according to (Lohman & Simons, 2005)
(6.1-11)
where dx=[1:nx], dy=[1:ny] are the sampling intervals
of w in the two geographic directions, nx and ny are the
maximum distances covered by the matrix w in x and y,
and ng
is the number of valid values within the overlap-
ping region at each shift (dx,dy). ng
is not necessarily
equivalent to nx times ny, due to water bodies and other
regions that are decorrelated in most interferograms.
While, in general, noise in w is anisotropic, here we
neglect this anisotropy and assume that the directional
average of Sw
versus distance is a good approximation
of Cn
(r). Given Sw
, values at scales L=[5,10,20,30,40,50]
km are extracted from Sw
and compared to the L2
requirements at these scales for validation.
6.1.3 Planned Output Products
NISAR L3 Solid Earth products will include:
• Maps of locations where the InSAR and GNSS data
are being compared
• LOS displacement vs. time plots, showing:
• InSAR time series using a standard SBAS
approach (Berardino et al., 2002, Hooper,
2006)
• The parameterized LSQ solution to the
InSAR data
• The corresponding time series of the LOS
component of the GNSS time series
• The corresponding LSQ solution to the LOS
component of the GNSS time series
• Tables and/or figures of comparisons showing
LSQ solutions and error estimates of velocities and
offsets as a function of baseline length from both
InSAR and GNSS observations.
6.2 ECOSYSTEMS PRODUCTS –
BIOMASS
The NISAR L2 science requirement for above ground
biomass (AGB) is expressed as:
The NISAR project shall measure above ground woody
vegetation biomass annually at the hectare scale (1 ha)
to an RMS accuracy of 20 Mg/ha for 80% of areas of
biomass less than 100 Mg/ha.
Above ground biomass (AGB) is a fundamental param-
eter for characterizing the spatial distribution of carbon
in the biosphere. Biomass is defined as the total mass
of living matter within a given unit of environmental
area and is of interest for a number of applications. It
is the raw material of food, fiber, and fuelwood. It is
important for soil, fire and water management. It is
also related to the vegetation structure, which, in turn,
influences biological diversity of the planet (Bergen et
al., 2009; Saatchi et al., 2007; Frolking et al., 2009).
Biomass density (the quantity of biomass per unit
area, or Mg, 106
grams of dry weight per ha) is used
to determine the amount of carbon released to the
atmosphere (as CO2
, CO, and CH4
through burning
and decay) when ecosystems are disturbed and is a
strong indicator of the ecosystem function in terms
of carbon sequestration through photosynthesis and
primary production. Above ground carbon density of
woody vegetation is approximately 50% of the biomass
with small variations depending on forest type and
composition (IPCC, 2006). The current knowledge of
the distribution and magnitude of terrestrial biomass is
based almost entirely on ground measurements over
an extremely small, and possibly biased sample, with
almost no measurements in the southern hemisphere
and equatorial regions (Schimel et al., 2015).
62
The NISAR mission’s observing strategy is designed to
provide sufficient coverage to estimate above ground
woody vegetation biomass at a spatial resolution of
100 m (1 ha), over the lifetime of the mission. This
will provide fine-grain products of carbon stocks and
changes required for understanding and quantifying the
global carbon cycle. An upper threshold of 100 Mg/ha is
set to reflect the sensitivity of L-band backscatter mea-
surements to biomass and allowing coverage of more
than 50% of the global forests and the entire area of
other woody vegetation (FRA, 2010). This sensitivity will
allow NISAR to quantify the carbon stocks and changes
of the most dynamic and variable component of global
vegetation and to provide significant contribution to the
global carbon cycle and climate science (Houghton et
al., 2009; Saatchi et al., 2011; Harris et al., 2012).
6.2.1 Theoretical Basis of Algorithm
Because of its sensitivity to volume scattering and
dialectric contrast Synthetic Aperture Radar (SAR)
backscatter measurements are sensitive to vegetation
AGB. SAR observations from a spaceborne platform
can thus be used for mapping and monitoring AGB on
a global scale. However, the SAR backscatter sensi-
tivity to AGB varies depending on the wavelength and
geometry of the radar measurements, and is influenced
by the surface topography, structure of vegetation, and
environmental conditions such as soil moisture and
vegetation phenology or moisture. The NISAR algo-
rithm will make use of high-resolution and time series
backscatter observations at dual-polarizations (HH and
HV) to estimate AGB by compensating for the effects of
environmental changes (soil and vegetation moisture
and phenology) and structure (vegetation and surface
topography).
Radar observations from vegetation have been studied
for more than four decades both theoretically and
experimentally (Ulaby et al., 1984; Tsang et al., 1985;
Ulaby and Dobson, 1989; Cloude, 2012). At L-band
frequencies, these studies have shown that the radar
measurements at different polarizations depend strong-
ly on the AGB but the relationship may vary depending
on the structure and dielectric properties of vegetation
components and underlying soil surface (Saatchi et
al., 1994; Saatchi and McDonald,1997; Ulaby et al.,
1990). The soil is most commonly described as a
homogeneous medium having a complex dielectric
constant, e, that is a function of the volumetric soil
moisture, mv
, as well as the soil texture, temperature,
and bulk density; several empirical models exist for this
relationship (Dobson and Ulaby, 1986; Hallikainen et
al., 1985; Mironov et al., 2004; Peplinski et al., 1995).
Studies of soil surface scattering and soil moisture
remote sensing at L-band have shown that the surface
scattering can be expressed in terms of soil dielectric
constant at the top 5 cm and the surface roughness
characteristics in terms of RMS (Root Mean Square)
roughness height and spatial correlation length (Fung
et al., 1992). In most SAR-related models for the
remote sensing of soil surfaces, it is assumed that the
effect of the spatial correlation is reduced significantly
during the SAR azimuthal processing and multi-looking,
and that the sensitivity of the radar signature to soil
surface RMS height variation remains as the dominant
surface structure influencing the surface scattering (Oh
et al., 1992; Shi et al., 1997; Dubois et al., 1995; Bag-
dadi et al., 2002; Bryant et al., 2007). Other landscape
features such as directional row or tillage may impact
radar cross sections at 100-m spatial resolution but
are assumed irrelevant in natural vegetation such as
forests and shrublands.
A variety of approaches exist for describing vegeta-
tion media, including characterization of vegetation
structure such as stalks, trunks, and leaves in terms of
canonical cylindrical or disk shapes with specified size
and orientation distributions in a set of vegetation lay-
ers, and with dielectric constants similar to live wood
of trees and leaf material (Saatchi et al., 1994; Saatchi
and McDonald, 1997; Saatchi and Moghaddam, 2000;
Yueh et al., 1992; Lang et al., 1983; Karam et al. ,1992;
63
Sw,dx,dy
=
1
ng
wk,l
− wk−dx=1,l−dy=1
( )
2
l=dy
ny
∑
k=dx
nx
∑](https://image.slidesharecdn.com/nisarfinal9-6-19-230209151416-17173123/85/NISAR-NASA-ISRO-Synthetic-Aperture-Radar-NISAR-Mission-Science-Users-Handbook-38-320.jpg)
![NISAR Science Users’ Handbook NISAR Science Users’ Handbook
IPCC, 2007: Climate Change 2007: Synthesis Report.
Contribution of Working Groups I, II and III to the Fourth
Assessment Report of the Intergovernmental Panel
on Climate Change [Core Writing Team, Pachauri, R.K
and Reisinger, A. (eds.)]. IPCC, Geneva, Switzerland,
104 pp.
IPCC, 2007: Climate Change 2007: The Physical
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Fourth Assessment. Report of the Intergovernmental
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JECAM, 2014, JECAM Guidelines for Cropland and
Crop Type Definition and Field Data Collection, Group
on Earth Observations, 7 pp. downloaded from http://
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Millennium Ecosystem Assessment, 2005. Ecosys-
tems and Human Well-being: Synthesis. Island Press,
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National Research Council, 1991. Mitigating Losses
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Academies Press.
National Research Council, 2013. Induced Seismicity
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mental Change: NASA’s Plan for a Climate-Centric
Architecture for Earth Observations and Applications
from Space (2010). Available at: https://smd-prod.
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s3fs-public/atoms/files/Climate_Architecture_
Final.pdf.
UNFCC, 2006. Climate Change: Impacts, Vulnerabili-
ties, and Adaptation in Developing Countries, United
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Agnew, D.C., 2012. SPOTL: Some Programs for
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uc/item/954322pg.
Agram, P.S., Jolivet, R., Riel, B., Lin, Y.N., Simons, M.,
Hetland, E., Doin, M.P. and Lasserre, C., 2013. New
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Ahlstrøm, A., Andersen, S.B., Nick, F.M., Reijmer, C.H.,
van de Wal, R.S.W. and Hubbard, A., 2013. Seasonal
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in situ GPS instruments. Earth system science data
discussions, 6(1), pp. 27-57.
Alley, W.M., Healy, R.W., LaBaugh, J.W. and Reilly,
T.E., 2002. Flow and storage in groundwater systems.
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Alley, R.B., Clark, P.U., Huybrechts, P. and Joughin,
I., 2005. Ice-sheet and sea-level changes. Science,
310(5747), pp. 456-460.
Ali, S.T. and Feigl, K.L., 2012. A new strategy for
estimating geophysical parameters from InSAR data:
application to the Krafla central volcano in Iceland.
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DOCUMENTS
ACIA, 2004. Impacts of a Warming Arctic: Arctic
Climate Impact Assessment. ACIA Overview report.
Cambridge University Press. 140 pp.
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Space. The Committee on Earth Observation Satellites
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129
11 REFERENCES](https://image.slidesharecdn.com/nisarfinal9-6-19-230209151416-17173123/85/NISAR-NASA-ISRO-Synthetic-Aperture-Radar-NISAR-Mission-Science-Users-Handbook-71-320.jpg)
![NISAR Science Users’ Handbook NISAR Science Users’ Handbook
cycle (i.e., we do not measure the absolute phase), it is
not possible to observe the deterministic propagation
component directly.
Interferometric synthetic aperture radar (InSAR) (Rosen
et al., 2000; Hanssen, 2001) techniques use two or
more SAR images over the same region to obtain sur-
face topography or surface motion. In this section, we
explain how an InSAR phase measurement relates to
actual ground deformation (see Massonnet et al. 1993;
a tutorial summary is found in Chen 2014a).
Figure 15-2 illustrates InSAR imaging geometry. At time
t1, a radar satellite emits a pulse at S1, then receives
an echo reflected from a ground pixel, A, and measures
the phase φ1 of the received echo. All scatterers within
the associated ground resolution element contribute
to φ1. As a result, the phase φ1 is a statistical quantity
that is uniformly distributed over interval (0, 2π) so
that we cannot directly use φ1 to infer the distance r1
between S1 and A. Later at time t2, the satellite emits
another pulse at S2 and makes a phase measurement
φ2. If the scattering property of the ground resolution
element has not changed since t1, all scatterers within
the resolution element contribute to φ2 the same way
as they contribute to φ1. Under the assumption that
|r1 − r2| << |r1| (the parallel-ray approximation), the
phase difference between φ1 and φ2 can be used to
infer the topographic height z of the pixel A (Hanssen,
2001, Section 3.2).
If we know the topographic height z, we can further
measure any small ground deformation occurring at
pixel A between t1 and t2. Figure 15-3 illustrates the
InSAR imaging geometry in this case. At time t1, a
radar satellite measures the phase φ1 between the
satellite and a ground pixel A along the LOS direction.
Later at time t2, the ground pixel A moves to A′ and the
satellite makes another phase measurement φ2 be-
tween the satellite and the ground pixel, Chen (2014b).
After removing the known phase φ′ due to the surface
topography, the unwrapped (module 2π) InSAR phase
is proportional to the ground
deformation ∆d between t1
and t2
along the satellite
LOS ground direction as:
where λ is the radar wavelength. In this equation,
we assume that there is no error in the InSAR phase
measurement. Below we discuss in depth various error
sources in InSAR deformation measurements and their
impact on InSAR image quality.
Note that InSAR techniques only measure one-dimen-
sional LOS motion. However, deformation is better
characterized in three dimensions: east, north and up.
Given an LOS direction unit vector e = [e1, e2, e3],
we can project the deformation in east, north and up
coordinates along the LOS direction as:
Because radar satellites are usually polar orbiting, the
north component of the LOS unit vector e2 is often
negligible relative to the east and vertical components.
When InSAR measurements along two or more LOS
directions are available, we can combine multiple LOS
deformation measurements over the same region to
separate the east and vertical ground motions, given
that the term e2 ∆dnorth
is negligible.
For this mission, interferometric observations of any
given point on the ground are acquired every 12 days.
Ground motion can be measured for a gradually chang-
ing surface that is not disrupted between images.
15.2 DEFORMATION-RELATED
TERMINOLOGY
The Earth’s crust and cryosphere deform due to differ-
ence forces acting on them. Deformation may be linear,
episodic, or transient.
F I G U R E 1 5 - 2
Ground
A
S1
S2
r2
r1
Z
Illustration of InSAR imaging geometry.
The distance between the satellite at
S1 and a ground pixel A is r1 and the
distance between the satellite at S2 and
the ground pixel A is r2. The topographic
height of the pixel A is z. Here we assume
|r1 − r2| << |r1| (the parallel-ray approxi-
mation) and no ground deformation
occurs at pixel A between the two SAR
data acquisition times.
F I G U R E 1 5 - 3
LOS Direction
Ground at t1
Ground at t'2
A
A'
Df
f1
f2
InSAR deformation geometry.
At time t1, a ground pixel of
interest is at point A and a radar
satellite measures the phase φ1
between the satellite and the ground
pixel along the LOS direction. Later
at time t2, the ground pixel moves to
A′ and the satellite makes another
measurement φ2 between the satel-
lite and the ground pixel. The phase
difference ∆φ is proportional
to the ground deformation between
t1 and t2 along the LOS direction.
154 155
Δϕ = ϕ2
−ϕ1
− ′
ϕ =
4πΔd
λ
Δd = e1
Δdeast
+ e2
Δdnorth
+ e3
Δdup
Δϕ = ϕ2
−ϕ1
− ′
ϕ =
4πΔd
λ](https://image.slidesharecdn.com/nisarfinal9-6-19-230209151416-17173123/85/NISAR-NASA-ISRO-Synthetic-Aperture-Radar-NISAR-Mission-Science-Users-Handbook-84-320.jpg)
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NISAR NASA-ISRO Synthetic Aperture Radar (NISAR) Mission Science Users Handbook
The NISAR (NASA-ISRO Synthetic Aperture Radar) mission is a joint initiative between NASA and the Indian Space Research Organisation set to launch in January 2022, aimed at producing global radar measurements to understand land surface changes due to various natural phenomena. The mission will utilize dual-frequency, fully polarimetric radar systems to provide comprehensive data on solid earth processes, ecosystems, and cryospheric dynamics, enhancing our understanding of critical environmental issues. NISAR's data will be made freely available, supporting a wide range of scientific research and societal applications, including disaster response and climate change monitoring.
