Gao, Y.

Gray, D.

Jackson, T.

Monerris, A.

O'Neill, P.

Panciera, R.

Ryu, D.

Walker, J. P.

Wu, X.

J.P. Walker

P. O'Neill

X. Wu

Y. Gao

A. Monerris

R. Panciera

T. Jackson

D. Gray

D. Ryu

Crossref

An airborne simulation of the SMAP data stream

NASA Technical Reports Server

Engineering
An Airborne Simulation of the 
SMAP Data Stream
J. P. Walker1, P. O’Neill2, X. Wu1, Y. Gao1, A. Monerris1, R. Panciera3, 
T. Jackson4, D. Gray5, and D. Ryu6
1Department of Civil Engineering, Monash University, Australia
2NASA/Goddard Space Flight Center, USA
3Cooperative Research Centre for Spatial Information, University of Melbourne, Australia
4United States Department of Agriculture (USDA), USA 
5Department of Electrical and Electronic Engineering, University of Adelaide, Australia
6Department of Infrastructure Engineering, University of Melbourne, Australia
Acknowledgement: Australian Research Council DP0984586, LE0453434 and LE0882509
https://ntrs.nasa.gov/search.jsp?R=20120015416 2019-08-30T22:39:48+00:00Z
SMAP Specifications
Launch: NASA, 2014
Frequency band: L-band 
Incidence angle: 40˚
Azimuth direction: conically-scanning antenna
Resolution: Soil Moisture ~9km -- 36km radiometer + 3km radar
Repeat: 2-3 days
1An Airborne Simulation of the SMAP Data Stream
Radiometer observation 
~36km
Radar observation 
~3km
Downscaled product
~9km
＋ = Algorithms
Active Passive Retrieval
and Downscaling
The Soil Moisture Active Passive mission
4An Airborne Simulation of the SMAP Data Stream
Airborne simulator
Datasets:   
TB at 36km
& σ˚ at 3km
Downscaled product:
TB/SM at 9km
Reference dataset:   
TB/SM at 1km
TEST DATA EVALUATION DATA
Pre-launch algorithm validation largely based on synthetic studies & few 
airborne data sets
SMAP Data Simulation
2An Airborne Simulation of the SMAP Data Stream
Motivation
Radiometer TB
36km
H&V pol
L-band
Incidence angle:
40°
Radar σ
3km
HH, VV & HV pol
L-band
Incidence angle:
40°
SMAP
PLMR TB
1km
H&V pol
L-band
Incidence angle:
±7˚, ± 21.5˚ and ± 38.5˚
PLIS σ
10-30m
HH, VV & HV pol
L-band
Incidence angle:
15°- 45°
Aircraft simulator
Upscaling
Incidence angle normalization
Azimuth effect
Azimuth: rotating Azimuth: left/right of track
5An Airborne Simulation of the SMAP Data Stream
Simulation of SMAP data
Soil Moisture Active Passive Experiments 
(SMAPEx)
Location: Yanco, Murrumbidgee Catchment, NSW;
Field campaigns:  SMAPEx-1 (5th-10th July 2010)
SMAPEx-2 (4th-8th Dec 2010)
SMAPEx-3 (5th-23rd Sept 2011) 
Flights
Regional flight, Target flights, Transect flight;
Multi-angle flights and multi-azimuth flights
Ground sampling   
Soil moisture; and
vegetation
3An Airborne Simulation of the SMAP Data Stream
Study site
Multi-angle flights
at 3,000m altitude
Multi-azimuth and multi-resolution flights
both at 1,500m altitude
7An Airborne Simulation of the SMAP Data Stream
Target flights
Caveat
 Calibration solutions used here for PLIS are 
still preliminary
 Absolute calibration accuracy for PLIS based 
on SMAPEx-3 is ~0.8dB
 Application of a specific SMAPEx-3 calibration 
to other dates results in ~1.5dB error
 The calibration procedure for PLMR is mature 
and is accurate to ~2K
Flight line Swath
An Airborne Simulation of the SMAP Data Stream 6
Reference 40˚ map
and more…
8 strips from 8 flights (HH-polarization)
Incidence angle: 42.5˚~37.5˚ ~3km
~5km
8An Airborne Simulation of the SMAP Data Stream
Normalization to 40˚ for PLIS
Original flight (HH-pol) CDF method
See poster: WEP. P. 5, Wednesday 25th July 17:20-19:00
“A cumulative distribution function based method for normalising …”
9An Airborne Simulation of the SMAP Data Stream
Normalization to 40˚ for PLIS
Normalized flight (HH-pol) Reference (HH-pol)
RMSE vs Angle (1˚ / ~90m)
RMSE (dB) 10m 100m 500m 1km
Original 7.5 7.2 7.1 6.5
Normalized 4.7 2.9 2.3 1.8
Normalized flight  (HH-pol)
10m 100m
500m 1km
10An Airborne Simulation of the SMAP Data Stream
Normalization to 40˚ for PLIS
11An Airborne Simulation of the SMAP Data Stream
Normalization to 40˚ for PLMR
360m
38.5˚ 7˚22˚
1.3km 700m850m
~3km
2
3
8
~6km
1
RMSE(K) 1km 3km 6km
Original 13.7 12.6 11.2
Normalized 7.4 5.7 3.0
Normalized flight (7˚, H-pol)
at 1km at 3km at 6km
Normalized flight -- 7˚̊
RMSE (K) 1km 3km 6km
Original 11.6 7.2 6.6
Normalized 6.7 4.0 2.9
Normalized flight -- 22˚̊
12An Airborne Simulation of the SMAP Data Stream
Normalization to 40˚ for PLMR
Azimuth effect for PLIS
Reference
90˚120˚150˚
210˚240˚270˚
700m
900m
13An Airborne Simulation of the SMAP Data Stream
Normalized to 40˚ at HH-pol
Azimuth effect for PLIS
RMSE vs Azimuth direction for PLIS
Reference Azimuth=240
˚
at 10m
at 500m
at 100m
14An Airborne Simulation of the SMAP Data Stream
1km
1km
Reference (40˚, H-pol)
90˚120˚150˚210˚240˚270˚
RMSE vs Azimuth direction for PLMR
15An Airborne Simulation of the SMAP Data Stream
Azimuth effect for PLMR
Original10
m
Upscaled to 
50m
Upscaled to 
150m
Original 
150m
Original 
50m
600m
2.5km
16An Airborne Simulation of the SMAP Data Stream
HH-pol, 
normalised 40˚
Upscaling for PLIS
50m 150m
RMSE (dB) of upscaling 5.1 3.3
2.3
100m10m 500m
RMSE (dB) of normalisation 4.7 2.9
Panciera, Walker et al. (2009), RSE
17An Airborne Simulation of the SMAP Data Stream
Upscaling for PLMR
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(
K
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Resolution (m)
1km (H-pol) 9km 36km
PLIS 10m (HH-pol) 1km 3km
18An Airborne Simulation of the SMAP Data Stream
Example of simulated data
(Data collected on 23rd Sept. 2011)
36km
36km
36km
36km
PLMR
PLIS
• Refine the PLIS calibration and repeat the analysis
• Try and eliminate any angle normalisation contributions to the azimuth and 
scaling results and assess georegistration contributions 
• Produce a time sequence of simulated SMAP data
• Undertake soil moisture retrievals from 1km PLMR (passive only), validated 
with higher resolution PLMR data and ground observations, for:
• Evaluation of SMAP downscaling algorithms based on simulated 
SMAP data from airborne simulator, and
• Evaluation of proposed active-passive retrieval algorithms using 1km 
and 36km radar/radiometer data 
19An Airborne Simulation of the SMAP Data Stream
Future work
WELCOME TO MELBOURNE, AUSTRALIA
for
IEEE IGARSS 2013
21 – 26 July 2013
www.igarss2013.org


An Airborne Simulation of the SMAP Data Stream

Once launched in late 2014, NASA’s Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth’s water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.J.P. Walker, P. O'Neill, X. Wu, Y. Gao, A. Monerris, R. Panciera, T. Jackson, D. Gray and D. Ry

Walker, J.

Adelaide Research &amp; Scholarship

An Airborne Simulation of the SMAP Data Stream

Abstract

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Full text

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Crossref

NASA Technical Reports Server

Adelaide Research & Scholarship

An Airborne Simulation of the SMAP Data Stream

Abstract

Similar works

Full text

Available Versions

Crossref

NASA Technical Reports Server

Adelaide Research &amp; Scholarship

Adelaide Research & Scholarship