Remote Sensing campaign in Australia by Rocco Panciera. Soil moisture retrieving. SAR, Hydraprobe, Smapex

1. Towards
Global
Remote
Sensing
of
Soil
Moisture:
Rocco
Panciera
Wednesday, September 25, 13

2. The
last
10
years
• 2003:
Master,
University
of
Trento,
Distributed
hydrological
modeling
• 2004
–
2009:
PhD,
University
of
Melbourne,
Passive
microwave
remote
sensing
of
soil
moisture,
• 2009
–
2010:
Research
Fellow,
University
of
Melbourne,
Passive
and
ac9ve
microwave
remote
sensing
of
soil
moisture
• 2011
–
present:
Super
Science
Fellowship,
ARC
SAR
remote
sensing
of
soil
moisture
2
Wednesday, September 25, 13

3. AcIvity
Overview
Instrument
Development
Field
Experiments Research
• Soil
moisture
monitoring
system
• Airborne
SyntheIc
Aperture
Radar
(SAR)
• NAFE’05
• NAFE’06
• SMAPEx-­‐1
• SMAPEX-­‐2
• SMAPEx-­‐3
• …
3
Wednesday, September 25, 13

4. Research
Overview
Remote
sensing
of
Land
surface
Soil
Moisture Land
Cover VegetaAon
Biomass
4
Wednesday, September 25, 13

5. Research
Overview
Remote
sensing
of
Land
surface
Soil
Moisture Land
Cover VegetaAon
Biomass
LiDAR
SAR
Passive
Microwave
OpIcal/IR
SAR SAR
OpIcal/IR
SAR:
SyntheIc
Aperture
Radar
4
Wednesday, September 25, 13

6. (MANY)Field
Experiments
2011
Soil
Moisture
AcIve
Passive
Experiment
(SMAPEx)
Dec
2010
AMSR-­‐E
ValidaIon
2004
NaIonal
Airborne
Field
Experiments
(NAFE)
20062005
Jul
2010
5
Wednesday, September 25, 13

7. Instrument
Development
• Hydraprobe
Data
AcquisiIon
System
(HDAS)
6
Wednesday, September 25, 13

8. Instrument
Development
• Hydraprobe
Data
AcquisiIon
System
(HDAS)
Soil
moisture
(vol) VegetaIon
height
(cm)VegetaIon
Type
7
Wednesday, September 25, 13

9. Instrument
Development
• Polarimetric
L-­‐band
Imaging
SAR
(PLIS)
8
Wednesday, September 25, 13

10. Instrument
Development
• Polarimetric
L-­‐band
Imaging
Sca`erometer
(PLIS)
SAR
SensiIvity:
Soil
moisture
Surface
roughness
VegetaIon
structure
VegetaIon
water
content
VegetaIon
height
Flight
path
3km
3km
15°
15°
45°
45°
9
Wednesday, September 25, 13

11. Airborne
Field
Experiments
• Soil
Moisture
AcIve
Passive
Experiments
(SMAPEx)
~40km
Soil
moisture
Sampling
Surface
roughness
&
vegetaIon
sampling
Panciera,
R.,
Walker,
J.P,
Jackson,
T
J.,
Ryu,
D.,
Gray,
D.,
Monerris,
A.,
Yardley,
H.,
Tanase,
M.,
Rudiger,
C.
et
al.,“The
Soil
Moisture
Ac9ve
Passive
Experiments
(SMAPEx):
Towards
Soil
Moisture
Retrieval
from
the
SMAP
Mission”,
IEEE
Transac9ons
of
Geoscience
and
Remote
Sensing,
51(9),
2013.
Passive
AcIve
10
Wednesday, September 25, 13

12. Airborne
Field
Experiments
11
Wednesday, September 25, 13

13. Soil
Moisture
from
SAR
• SensiIvity
of
SAR
to
soil
moisture
(Mv)
Bare
soil
Canola
~
140cm
height
Wheat
~
50cm
height
-­‐20.0000
-­‐15.0000
-­‐10.0000
-­‐5.0000
0 2 4 6 8 10
SAR
dB
Days
SAR
HH-­‐pol Mv
SAR
VV-­‐pol
12
IrrigaAon
Wednesday, September 25, 13

14. Soil
Moisture
from
SAR
• SensiIvity
of
SAR
to
soil
moisture
13
Rough
Surface
Smooth
surface
Surface
RMS
[cm]
Wednesday, September 25, 13

15. Soil
Moisture
from
SAR
• Time-­‐series
approach:
Backsca`er
dynamic
over
short
periods
solely
due
to
soil
moisture
changes
Snapshot
approach
Time-­‐series
approach
14
Wednesday, September 25, 13

16. Soil
Moisture
from
SAR
• Time
series
approach
Using
1km
ALOS
PALSAR
data
in
Australia
Satalino,
G.,
Maja,
F.,
Balenzano
A.,
Panciera,
R.,
Walker,
J.P,
“Soil
Moisture
Maps
from
Ime
series
of
PALSAR-­‐1
scansar
data
over
Australia”,
Proceedings
of
IEEE
Interna9onal
Geoscience
and
Remote
Sensing
Symposium
2013
(IGARSS
2013),
21-­‐26
July,
Melbourne,
Australia.
15
Wednesday, September 25, 13

17. Surface
roughness
from
LiDAR
Turner,
R.,
Panciera,
R.,
Tanase,
M.,
Lowell,
K.,
Hacker,
J.,
Walker,
P.,
J.,”
Es9ma9on
of
Soil
Surface
Roughness
of
Agricultural
Soils
using
Airborne
LiDAR”,
Remote
Sensing
of
Environment,
In
review,
2013.
16
Wednesday, September 25, 13

18. Soil
Moisture
from
Passive
microwave
• Algorithm
development
for
ESA’s
SMOS
for
Australian
condiIons
Uncalibrated
parameter
“b”
Calibrated
parameter
“b”
(Jackson
and
Schmugge,
1991)
Wheat/barley
pastures
Panciera,
R.,
Walker,
J.P.,
Kalma,
J.D.,
Kim
E.J.,
Saleh,
K.,
Wigneron,
J.-­‐P.,
“Evalua9on
of
the
SMOS
L-­‐MEB
passive
microwave
soil
moisture
retrieval
algorithm”.
Remote
Sensing
of
Environment,
113(2):
p.
435-­‐444,
2009.
17
Wednesday, September 25, 13

19. Soil
Moisture
from
AcIve/Passive
microwave
• Downscaling
algorithm
development
for
NASA’s
SMAP
mission
Airborne Simulated
SMAP
AcIve/passive
Downscaling
to
9km
Downscaling
error
KPassive
AcIve
Passive
AcIve
18
RMSE
=
1.5
–
5.8
K
SMAP
target
=
2.4K
Wednesday, September 25, 13

20. Soil
Moisture
from
Passive
&
OpIcal/NIR
• Downscaled
SMOS
+
MODIS
1km
soil
moisture
product
January
2-­‐14,
2011
Tropical
Cyclone
Oswald
Piles,
M.,
Camps,
A.
,
Vall-­‐llossera,
M.,
Corbella,
I.
Panciera,
R.,
Rudiger,
C.,
Kerr,
Y.
and
Walker,
J.,
“Downscaling
SMOS-­‐derived
soil
moisture
using
MODIS
visible/infrared
data”,
Accepted
for
publica9on
in
IEEE
Transac9on
on
Geoscience
and
Remote
Sensing,
TGRS-­‐2010-­‐00403.R1,
2010.
20
Wednesday, September 25, 13

21. Land
cover
from
SAR
&
opIcal
• Supervised
land
cover
classificaIon
using
Cosmos-­‐SkyMed
&
Landsat
19
Overall
classifica9on
Accuracy
(OA)
Landsat5,
2
images
OA
=
93%
Cosmo-­‐SkyMed,
8
images,
HH
and
HV:
OA
=
80%
Wednesday, September 25, 13

22. Forest
Biomass
from
SAR
and
LiDAR
Tanase,
M,
R.
Panciera,
K.
Lowell,
C.
Aponte,
J.
M.
Hacker,
J.
P.
Walker,
“Forest
Biomass
Es9ma9on
at
High
Spa9al
Resolu9on:
Radar
vs.
Lidar
sensors”,
accepted
for
publica9on,
IEEE
Geoscience
and
Remote
Sensing
Le_ers;
21
Wednesday, September 25, 13

23. CollaboraIons
• Consiglio
Nazionale
della
Ricerca,
Italy
–
AcIve
microwave
&
land
cover
mapping
• Jet
Propulsion
Laboratory,
Pasadena
–
AcIve
Microwave
(SMAP
mission)
• United
States
Department
of
Agriculture
–
AcIve/passive
microwave
(SMAP
mission)
• European
Space
Agency
–
Passive
microwave
(SMOS
mission)
• Australian
Defence
Science
and
Technology
OrganisaAon
(DSTO)
–
Airborne
SAR
development/calibraIon
• Barcelona
SMOS
Expert
Centre
–
SMOS/MODIS
soil
moisture
product
22
Wednesday, September 25, 13