Active microwave remote sensing of land surface hydrology · 014 Why microwave remote sensing Cloud...
Transcript of Active microwave remote sensing of land surface hydrology · 014 Why microwave remote sensing Cloud...
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ACTIVE MICROWAVE REMOTE SENSING OF LAND SURFACE HYDROLOGY
Basics, methods & applications
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Active microwave remote sensing of land surface hydrology
Landsurface hydrology:
Near surface water storage: soil, snow, water bodies
Introduction
Why microwave remote sensing?
Why active?
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Why microwave remote sensing
Cloud independent
Therefor frequent acquisitions possible, what is of interest when we study fast changes
Complements optical and thermal
For climate modelling interesting regional to global products available
Aim Basic understanding on what radar based products can
offer with respect to land surface hydrology
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Why active systems (radar)
Systems available which cover different scales and part of the electromagnetic spectrum
Specific techniques available which offer unique insight into landsurface processes such as movements or surface structure
Several relevant satellite launches in 2014
Sentinel-1 as part of copernicus is a radar system! + Future Biomass mission
To some extent similar application potential like passive microwave sensors
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Basics
Microwaves ~1cm – 1m
But (spaceborne) systems work on ~ 2 - 23cm
Jensen 2005
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Basics
Wavelength – Bands
Polarization
Single, dual, cross …
X C L Ku P S
HH VV HV VH
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Basics - Polarization
Send – Received HH or VV HV or VH
Polarimetry analyses polarization state of an electromagnetic field
http://www.ccrs.nrcan.gc.ca
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Basics - Polarization
Cross polarisation modes detect the amount of backscatter whose polarisation has changed as a result of surface interaction
Polarisation determines the penetration depth (beside the actual wavelength)
http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1025
HH VV
HV colour composite
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Basics
Wavelength – Bands
Polarization
Single, dual, cross …
Incidence angle
Range
Azimuth
X C L Ku P S
HH VV HV VH
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Terminology
altitude above-ground-level, H
Nadir
azimuth flight direction
range (near and far)
depression angle (γ)
look angles (φ)
incidence angle (Θ)
Jensen, 2009
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Basics
Backscatter – local incidence angle
Ku-Band; Stephen 2006
Dense vegetation
Bare dry soil
Near range far range
Normalized measure of the radar return from a distributed target ESA Radar Glossary
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Basics
Data are acquired
At ascending and descending orbit, and
in different modes
vary in spatial resolution and area covered
Example ENVISAT ASAR Right looking Varying length of frame
Near range
Far range
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Basics – preprocessing Normalization
before normalization after normalization
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Basics – preprocessing
Distortion phenomena
Image distortion phenomena in side-looking radar imaging:
(left) Foreshortening and (right) Layover & shadowing
Rees 2001
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Basics – preprocessing
LIA for flat area LIA for Hochschwab, eastern Alps
ENVISAT ASAR WS examples
Local Incidence Angle - LIA
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Basics – preprocessing
original
ENVISAT Synthetic Aperture Radar (SAR)
orthorectified
Source: W. Wagner
Orthorectification of SAR – terrain correction
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Basics - SAR Pre-processing
Radiometry and geometric distortions
Orthorectification
Normalization
Speckle reduction
Multilooking
Adaptive filtering
Speckle: caused by random constructive and destructive interference from the multiple scattering returns that will occur within each resolution cell
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Basics
Wavelength – Bands
Polarization Single, dual, cross …
Incidence angle
Range
Azimuth
Beam A certain area on the surface is illuminated
Spatial resolution?
The product grid is ‚nominal resolution‘
X C L Ku P S
HH VV HV VH
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Ground instantaneous field of view Footprint in case of non-imaging sensors
Basics spatial resolution (distance between distinguishable objects)
range
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Basics spatial resolution
range
L
SRa
S - Slant range distance γ - Wavelength L - Antenna length
½ of the pulse length
Resolution for real aperatures – coarse from space!
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Jensen, 2009
Basics – spatial resolution
Synthetic Aperture Radar
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Basics – instruments and applications
real aperatures instruments
scatterometer
gridding required
Used for global applications
Frequent acquisitions
Operational (designed for ocean applications)
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Basics – instruments and applications
SAR – synthetic aperture radar
A technique to overcome the resolution problem, but local to regional applications
Resolution – azimuth and range difference
Data availability a matter of request and priority
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Currently in space, a selection
Jensen 2005
Sentinel 1, Radarsat, ASCAT
TerraSAR-X
ALOS2 PALSAR
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• Polarisation schemes for IW, EW & SM:
single polarisation: HH or VV
dual polarisation: HH+HV or VV+VH
• Wave mode: HH or VV
• SAR duty cycle per orbit:
up to 25 min in any of the
imaging modes
up to 74 min in Wave
mode
Main modes of operations:
- IW over land and coastal waters
- EW over extended sea and sea-ice areas
- WV over open oceans
Currently in space, a selection
Sentinel – 1 (launched April 2014)
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Currently in space, a selection
Sentinel 1 – acquisition plan (selected modes)
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Currently in space, a selection TerraSAR-X (since 2007)
Acquisition plan
http://terrasar-x-archive.infoterra.de/
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Currently in space, a selection ALOS-2 PALSAR (May 2014)
Acquisition plan
Wetlands & deforestation
Crustal deformation
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Past sensors - SAR
Potential service demonstration
Mid-term changes
ENVISAT ASAR 2002-2012
C-Band
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Currently in space, a selection
Scatterometer
ASCAT on Metop A and B
For meteorological purposes so continuation ensured
Operational products, global
C-band
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Past sensor - scatterometer
ERS1, ERS2 (1991-2011)
C-band
Seawind on QuikScat (1999-2009)
Ku -band
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Quelle: http://www.scp.byu.edu/
900 km 800 km
Perry 2000
Examples
Past sensor - scatterometer
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Daily coverage 900 km 800 km
Distribution
of footprints
and their
time stamp
Bartsch et
al. 2007
Perry 2000
1999 - 2009
Naeimi 2010
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Images based on ‚Eggs‘: 4.45 km Effective res. 8.-10. km (source BYU)
BYU
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Example Metop ASCAT
Figa et al. 2002
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Example Metop ASCAT ASCAT soil moisture product
gridding
Naeimi et al. 2009
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Whats in space - soon
SMAP – soil moisture active passive
currently planned for October, 2014
Frequency: 1.26 GHz Polarizations: VV, HH, HV (not fully polarimetric) Relative accuracy (3 km grid): 1 dB (HH and VV), 1.5 dB (HV) Data acquisition:
High-resolution (SAR) data acquired over land Low-resolution data acquired globally
NASA SMAP
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NASA SMAP
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Signal interaction
Wavelength is very important
Penetration depth into soil, snow, vegetation
Change of direction
Polarization is very important
Penetration depth into especially vegetation (has a regular structure)
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Signal interaction
+ Reflection enhanced when rel. permittivity (dielectric constant) and/or roughness is high
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Signal interaction
Examples C-Band (ASAR WS)
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Signal interaction
Examples C-Band
Naeimi et al. 2012
ASCAT
frozen/dry/inundated/melting snow
Saturated/ corner reflection
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Signal interaction
Near surface soil moisture
Surface roughness
Volume scattering in vegetation
Snow
Summer Winter
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ERS C-band
Seawinds QuikScat Ku-Band
Daily air temperature range
Bartsch, (2010)
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Seawinds QuikScat - noise
Bartsch 2010
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ERS C-band
Seawinds QuikScat Ku-Band
Daily air temperature range
Bartsch, (2010)
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Snowmelt
QuikScat: Precise timing from diurnal difference
Supplement to: Bartsch (2010): Ten Years of SeaWinds on QuikSCAT for Snow
Applications. Remote Sensing, 2(4), 1142-1156, doi:10.3390/rs2041142
http://doi.pangaea.de/10.1594/PANGAEA.834198
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Freeze/thaw
Metop ASCAT
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Paulik et al. (2014) doi:10.1594/PANGAEA.832153
(2007-01 to 2013-12)
ORCHIDEE-Land surface model
Gouttevin et al. 2013
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Ulaby & Stiles 1980
backscatter snow height
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Increase of snow depth in a very short time?
Bartsch (2010)
140°E150°E160°E170°E180°170°W150°W140°W
40°E40°W 30°E30°W 20°E20°W 10°E10°W 0°
50°N
60°N
50°N
60°N
50°N50°N
average events per winter
once
twice
three times
four times
more than four times
Bartsch et al. 2010
(WMO512)
Strong backscatter increase in a few days
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Snow profile taken on the 19th of November 2006. (Photo: Florian Stammler)
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Rennert et al. 2009
14
0°E
15
0°E
16
0°E
17
0°E
18
0°
17
0°W
15
0°W
14
0°W
40°E
40°W
30°E
30°W
20°E
20°W
10°E
10°W
0°
50°N
60°N
50°N
60°N
50°N
50°N
ave
rag
e e
ven
ts p
er
win
ter
on
ce
twic
e
thre
e t
ime
s
fou
r ti
me
s
mo
re t
ha
n f
ou
r ti
me
s
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Wilson, R.R., et al. 2013
Median 470 km²
number
Size of events
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Semmens et al. (2013).
Fog derived from
NARR data
Air temperature, due point,
rel. humidity, visibility
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Wetland mapping
Inundation – permanent, seasonal
Wet soils
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Inundation mapping with SAR
Specular Reflection over water
Bartsch et al. (2008)
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60%
Bartsch, A., Trofaier, A., Hayman, G., Sabel, D., Schlaffer, S., Clark
D. & E. Blyth (2012): Detection of open water dynamics with
ENVISAT ASAR in support of land surface modelling at high latitudes;
Biogeosciences, 9, 703-714.
West Siberien Lowlands Test with
more than 4000 subsets of 0.25°
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Wetland areas
Near surface soil moisture
Double bounce – standing water (permanent and emerging vegetation)
Volume scattering in vegetation
Surface roughness
Mostly smooth water
Double bounce – urban (permanent phenomen)
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Okavango example
end of rain season
dry season
Bartsch 2009
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ESA STSE ALANIS Methane
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ESA STSE ALANIS Methane – experimental product from ENVISAT ASAR WS
June 2007 September 2007
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ENVISAT ASAR Wide swath
Best available coverage among SAR sensors
Actual coverage does however vary
C-Band – sensitivity to weather in case of this specific application
Continuity with Sentinel 1
Bartsch et al., 2012
Example
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ESA STSE ALANIS Methane – experimental product
10 days All summer including saturated area
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ESA STSE ALANIS Methane – experimental product
Supplement to: Reschke, Julia; Bartsch, Annett; Schlaffer, Stefan; Schepaschenko, Dmitry (2012): Capability of C-Band SAR for operational wetland monitoring at high latitudes. Remote Sensing, 4(12), 2923-2943, doi:10.3390/rs4102923
http://doi.pangaea.de/10.1594/PANGAEA.834502
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Signal interaction
Near surface soil moisture
Two adjacent pixels, same total backscatter
Surface roughness
Volume scattering in vegetation
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Signal interaction
How to separate those contributions
Use a model
Exploit different polarizations
Combine with optical
Rule out changes of certain mechanisms over time
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Signal interaction
Near surface soil moisture
Surface roughness
Volume scattering in vegetation
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Soil moisture
Time series for a single location (C-band)
Wet reference
Dry reference Wagner et al. 1999
Assumptions: - No rougness
change - Vegetation impact
can be modelled from incidence angle variation
- References need to be known
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ASCAT
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Basics
Backscatter – local incidence angle
Ku-Band; Stephen 2006
Dense vegetation
Bare dry soil
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Soil moisture
Time series for a single location (C-band)
Wet reference
Dry reference Wagner et al. 1999
Bartsch et al. 2012
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Soil moisture
Penetration depth?
Bartsch et al. 2012
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Soil moisture
But roughness change is possible
In areas with high water fraction
Change of scattering mechanism in part of the footprint – flooding, freezing, snow
Comparison with landsurface model ORCHIDEE, Gouttevin et al. 2013
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Summary
Soil moisture
Footprint heterogeneity
Weather impact!
Snow
Derived information: frozen/unfrozen
Timing crucial (diurnal changes)
Short wavelength required
Inundantion
Weather impact!