The 9th GEOSS Asia-Pacific Symposium
Earth Observations Supporting the Implementation of the SDGs in the Asia Pacific Region

A Water Cycle Observation
Mission （WCOM）
Qinhuo LIU
State Key Laboratory of Remote Sensing Science,
Institute of Remote Sensing and Digital Earth, CAS

Jiancheng Shi
Xiaolong Dong, Tianjie Zhao, Jinyang Du, Lingmei Jiang, Hao Liu, Zhenzhan Wang, Dabin Ji, and Chuan Xiong

Jan. 12， 2017Tokyo

Outline
1. Background
2. Payloads Design
3. Phase A research
4. Cooperation and Expectation2

Water Cycle & Climate Change
Water Cycle /Climate LinkageClausius-Clapeyron Equation• One of the Earth system’s major cycles• The Clausius–Clapeyron equation governs the waterholding capacity of the atmosphere that increases by
about 7% per degree Celsius.

Expectations:drizzles, storms, ET, speed of water
cycle, therefore, hydrological extreme events
Water in the climate system functions
on all time scales (from hours tocenturies)

Key Science Questions
What are the spatial-temporal distribution characteristics
of water cycle components and processes? Are thechanging speeding up?

Importance of Water Cycle
Application Linkage
Basic requirements for monitoring and prediction ofwater resource, flood, drought, agricultures …..
1998 flood event in China
1998.6.12-8.12 3hr precipitation2010 drought monitoring in SouthWest China

Available Sensors for Water
Cycle
SensorFrequency（GHz）

vapor

Preci.

Temp.

SoilMoistur
eFreeze
Thaw

SWE

AMSR-E

6.925;10.65;18.7;23.8;36.5;89

√

√

√

√

√

√

√

GCOM/AMS
R26.9;7.3;10.65;18.7;23.8;36.5;89

√

√

√

√

√

√

√

FY3/MWRI

10.65;18.7;23.8;36.5;
89

√

√

√

√

6.6;10.7;18;21;37√

Multiple SMMR
Frequency
SensorSSM/ITRMM/TMI

19.35;22.235;37.0;85.
510.65;19.35;21.3;37;8
5.5

√

√

Sea
SalinitySea
Surfacewind√

√√√

√

√

√√

√

√

√
√

WindSat6.8;10.7;18.7;23.8;37

√

√

SSMIS

19.35;22.235;37;5060;91.655;150;183.31

√

√

ASCAT

5.255

√

ERS

5.3

√

13.4

√

SingleQuikSCAT
Frequency
Aquarius
Sensor

√

√

1.413

√

√

SMOS

1.41

√

SMAP

1.26; 1.41√√√

Outline
1. Background

2. Payloads Design
3. Phase A research
4. Cooperation and Expectation

6Importance of snow in water
cycle research
Importance1）Snow water equivalence:
great importance to snowmelt
runoff forecast, water resourcesmanagement and flood
prediction. Snowmelt is an
important factor of water cycle
Global snow melting
and the main source of
runoff dominating area
freshwater in many areas.

2） Snow cover area and SWE

Energy and mass balancecomputations

are important elements ofhydrology, meteorology andclimate monitoring, and the key
variables for energy and massbalance in water cycle model.

Terrestrial Snow: Spatialtemporal distribution
characteristics and itschange characteristics

Key Science Questions
1） What is the impact ofsnow on global and regionalenergy and mass balance
and its response ？
2）In the background of
global changing, what is thespatial-temporal distribution
characteristics and its change
characteristics of snowfall ？
3） what is the impact on
global and regional water
resources ？

Problems in SWE inversion• Passive microwave（~25km)：
AMSR-E B04 product(no pixel mixingdecomposition)

50.00

40.00

Our algorithm ( withpixel mixing
decomposition)(SD( SWE ) = a + b ⋅ TBp (18) − TBp (37)30.00

s d_e– SMMR
– SSM/I– AMSR-E– AMSR2– FY-3

20.0010.00) 4. Insufficient spatial resolution, horizontally
0.00

010

1. Semi-empirical algorithm:
in homogenous of snow (mixed pixel)
Regional differences, inconsistentaccuracy globally
2. Vertical inhomogeneous (layeredsnow), changes in snow
characteristics
3. Atmospheres

Need: Spatial observation capacity

2030s d_m

4050

Problems of Current Techniques
1、Lack of synergistic observations on
the other affecting factors the retrievalof water cycle components

2、Lack of systematicalobservations on the water cycle
components that are related toeach other

Parameters

Disadvantages inObservations

Disadvantages in Inversion

Soil Moisture

Weak penetration for high freq.; lackof temperature for low freq. ; RFI

Lack of valid inversion technique on
vegetation and surface roughness

SWE

Low spatial resolution of passive
microwave

More considerations needed for snow
process and atmosphere conditions

FT

Low spatial resolution for passive
microwave

Limited validity for using fixed
Threshold values

Sea Salinity

Lack of temperature and atmosphereobservations

Lack of surface roughness correction

SeaEvaporationlack of simultaneous observations on
both sea surface and atmosphere

Uncertainties in the inversion of relatedparameters

Precip.

Cloud 3D properties

Need to Discern rain and snow

Characteristics of the Spatial-Temporal
Distribution of Water Cycle Components
Hydro-climatology 50-100km，Hydro-meteorology 4-15km resolution
Water Cycle
Strong Variability
in Time：Precip./vapor

Temporal
Resolution

Ideal SpatialResolution

Minimum
Requirement

Obs, Error

1-2hour

1km

25km

1 mm hr-1• Precip./vapor

Sea Evap.

1-2hour

10km

25km

15 W m-2• Ocean
Evaporation

Soil moisture

2-3day100m-1km50km

0.04 m3/m3

Sea salinity

10-30day

10km

100km

0.1-0.2 psu

FT

2-3day100m-1km

50km

10-20 %

SWE

2-3day100m-1km

50km

10 %

Water body

3-7day30m

1km1000 m2

UndergroundwaterLand ET

1month

50km

300km

~

1-2hour

30m-1km

5km

30 W m-2

runoff1-2hour~~~

Strong Variabilityin Space：•FT•SWE•Soil Moisture
Weak Variability ：•Sea salinity•Polar Ice

Process model improvementfrom observations
1、Parameter optimization using single-element observation
Changes in model performances
CaseSoiltemperature

Soil moistureSensible heat flux

Latent heat flux

soil temperature observation

21.99%-41.87%

11.13%-46.08%-0.46%
10.85%
1.15%
1.29%
Test experiments by CoLM demonstrate that: the model error will transfer to another state variables when only
one state is optimized by using single-element observation
Soil moisture observation

2、 Parameter optimization using multi-element observation
Parameter Selection

Building Surrogate Models

Select the most sensitiveand controlling parameters

Building statistical models tosurrogate the physical models

ASMO：higher efficiency

SCE-UA：lower efficiency

Multi-Object Optimization

Simultaneous
multiparameters optimization
provide a much betterchance for the modelimprovement

Payloads and Configurations• 1. IMI，Full Polarized Interferometric Radiometer：
Soil Moisture and Sea Salinity• 2. DPS, Dual Frequency Polarized Scatterometer：SWEand FT• 3. PMI, Polarimetric Microwave Imager， 6.8～89GHz：
Temperature, rain, water vapor , atmosphere correction,and bridge to historical data
Payloads

IMI

PMI

DPS

Frequency（GHz）

L, S ,C(1.4,2.4,6.8)

C~W
X, Ku(7.2,10.65,18.7,23.8,37,89) (9.6,14/17)

Spatial Resolution（km）

L: 50, S: 30, C:15

4~50（frequencies）

2~5（processed）

>1000

>1000

Swath Width（km） >1000
Polarization

Full-PolFull-PolFull-Pol

Sensitivity

0.1~0.2K

0.3～0.5K

0.5dB

2～32～3

Temporal Resolution
2～3（Day）

Advantages of WCOM Payloads
DesignIMI
Soil Moisture

Sea Salinity

PMI

1 More sensitive to land
surface
2 Minimizing vegetation
effects
3 Mitigating RFI
1 More sensitive to sea
surface2
Faraday
rotationcorrection

DPS

1 Sensitive to temperature
1Surface
Roughness
2 Observing
large-scale vegetation
surface roughness
2 high resolution soil moisture
1 effective correction onatmosphereHigh resolution Wind Vector
2 ensitive to sea temperature

SeaEvaporation

Correctionsonsurface roughness

FT

Obtaining Soil
Parameters

Surface Sensitivechanges

SWE

Obtaining Soil
Parameters

Surface Obtaining SWE by scattering 1 Estimating SWE
effects
2 Mitigating Mixed pixel effects

Vapor
Precip.

andsea Sensitive to temperature
toHigh resolution Wind Vector

1 Time series techniques for FT
temperature detection
2 Downscaling techniques for FT
inversion

Water Vapor
Helping determine land 1）obtaining2）Precip.
Ratesurface emissivity3）Discerning Rain and snow

Vitalandmajor

High resolution observations on
precip.

help

The Payloads Design：1）Optimal channels for inversion，2）Effective corrections
on affecting factors，3）Simultaneous observations

Outline
1. Background
2. Payloads Design

3. Phase-A research
4. Cooperation and Expectation

14

Phase-A Objectives
Science part

Technology part1）Further evaluation of science objectives;
further optimization of payloads, to achieve
higher precision water cycle parameters
observation than any existing satellites;

1）Design and evaluation ofpayloads: FPIR, PMI and DFPSCAT2）Based on the simultaneously multisensor observation, to achieve joint key
water cycle parameters and environmental
parameters retrieval, and the preliminary
algorithm validation;3）The study of the method to calibrationof historical observations of other satellites
based on WCOM observations; Water cycle
models parameter optimization;2）To make breakthroughs in key
technologies in payloads, and theexperimental validation of the key
technologies ;3）WCOM satellite platform designand evaluation based on therequirement of payloads and theirobservation; Design and evaluationof interface between satellite systemand other systems

SWE retrieval and Validation
SWE inversion algorithm for
DPS scatterometer isdeveloped based on
Bicontinuous+VRT model .
Three-year time series
measurements at dualpolarization X and Ku bands
in Finland Nosrex campaign.

2009-2010 Winter2010-2011 Winter2011-2012 WinterL/S/C Microwave InterferometricRadiometersystem

1D Interferometry + parabolic cylinderreflector

frequency

L：1.4～1.427GHz，S：2.64~2.70GHz，
C：6.6～6.9GHz

Sensitivity

L-band：0.1K；S-band：0.4K；C-band：

size

Feed array：4m×0.5m

FOV>1000km

Incidence

30~55ºSpatialL-band ： 50km ， S-band ： 30km ， C-resolution

band：15km

revisit

2-3 daysweight250kg

Data rate< 1Mbps

Instrument Concept：1D Microwave
0.4K
Interferometric Radiometer with
Polarization Full pol（H,V,Q,R）
parabolic cylinder reflector
antenna
Antenna
Reflector:6.0m×6.0m（after deployment）
••
•
360
340
320
300
280
-900

Use parabolic cylinder reflector andinterferometric technology to achieve Highspatial resolution
Patch feeds and shared reflector to achieve
the multi-frequency ability
Dual-size feeds to enhance the systemsensitivity performance-800-700-600-500-400-300-200-1000100200

300

400500600

Simulated footprints on the ground700800900

Advantages in soil moisture retrieval
RFI influence
FPIR-L&S

AtmospherePMI• IMI
Vegetation
correct
FPIR-L&S

Surface effectivetemperature
PMI-Ka1）Combination of L- and S-band can solve thepolarization effects in vegetation correction.
2）The probability of RFI occurrence at thesame area and frequency is vary small. RFI canbe avoid by switching L- and S-band .

• DPS
Vegetation information of high resolution• PMI : Surface effective temperature
PALS

Only L-band

SMEX02 experiment area

RMSE=0.047

Various vegetation types

L+S-band

RMSE=0.035

Soil moisture Products
A） Passive microwave (FPIR)

Spectral analysis downscaling method for passive microwave
V

Soil Moisture Active
L/S/C-bands:
50/30/15
and Passive(SMAP) km
Experiment Mission
with Airborne data：

Downscale the L-band Tb (4km) at ascale of 800m using higherresolution Tb of S-band, and its
validation with original L-band data

Passive：Sensitive to soil moisture but low resolution

HDownscaling using both active and passive

Active：High resolution but sensitive to vegetation and roughnessB）Active/passive microwave(FPIR/PMI+DFPSCAT)
TBp =A + C

σσt
vhtvh
σ vht  t+  B + D t  σ pp
σ vh 

Active /passive
combination of Cand X band：TBv~4km

Observed Downscaled(~0.8km)(~0.8km)

Products：Soil moisture estimates at a scale of both 15km and 5km(research) over nominal areas and 30km over forests.

19WCOM data simulator
Dynamic forcing data module
Land/Ocean/Atmosphere radiativetransfer and backscatter modelling

WCOM payloads
configuration
DPS

1. FPIR/PMI Brightness temperature
2. DFPSCAT Backscatter coefficient

Initial WCOM data

Satellite orbit, Sensor gain
function, footprints andresampling

IMI

Calibration with current
satellites (SMOS/SMAP,AMSR2, etc)
Final WCOM dataPMI

3) Parameter
optimization ofhydrologic model

2) Evaluate
instrument error on
science requirements
1）Retrieval algorithm
development and validation

Science Requirements forInstrument Error
Analysis of Effect of Payload observational error on
parameters inversion accuracy
Parameters

Instrumental error

Retrieval
RMSE

SWE

<0.5dB<10%

Soil Moisture

Observed Tb <1K<0.04m3/m3

SSS

observed Tb: L band
<0.2K, S band<0.5K,
C band<0.4K< 0.2psu/week

Freeze-thawState< 1-0.5dB

classification
accuracy>8090%

Precipitation

observed Tb <3K<0.4mm/hr

Error=0.5dB

Analysis of the effect of instrument
observation error on soil moisture
inversion accuracy

Analysis of the effect ofinstrument observation error on
SWE inversion accuracy

反演精度: L+ S+ C波

SSS
0.7
147
100.6

天平均天平均天平均天平均0.5

SSS0.3(psu)

反演精度

0.4

0.2

0.1

0.1

Analysis of the effect of instrument observation
error on freeze-thaw monitoring inversion accuracy

0.15
0.2
仪 器 亮 温 测 量 精(K度)

0.25

0.3

Analysis of the effect of instrument
observation error on SSS inversion accuracy

Analysis of the effect of instrument
observation error on precipitation
inversion accuracy

Outline
1. Background
2. Payloads Design
3. Phase-A research

4. Cooperation and Expectation

22

Progresses of WCOM
• 2013, WCOM was selected as one of 8 candidate science
driving missions to be launched before 2020; It is only one
that for EO in China.
• 2014-2015：Phase-A to study key technologies;• In Feb., 2015, 3 from 8 candidate missions were selected as
the key support missions with full funding for 2014-2015.
WCOM is one of them;• WCOM is now under the engineering defense;• Launch date 2019-202023

International Collaborations
WCOM,SMOS,SMAP
FY-3 PreciSat

GPM

Global Water Cycle
Observations
WCOM

Ocean Salinity,
Water ResourceSat(s)

SWOT,GRACE

Form a global water cycle consolidation

Water Cycle Observation Mission(WCOM) SummaryWCOM

Accurate
Inversion ofWater Cycle
Parameters
based on
Synergistic
Observations

Systematic and Simultaneous
Observations For Water Cycle
Soil Moisture
Freeze/Thaw
SWE
Ocean Salinity
Precipitation

New Payloads
Designs

Demand and feasibility
analysis for the sensors

Ocean
Evaporation

1) Accurate
keycomponents
measurement;2) Improve
historicalmeasurements
and toImproving therelated models

Science andmeasurements currentdrawbacks

Key ScienceQuestions：
1) Improving onunderstanding of
spatial/temporal
distribution
characteristics of water
cycle key parameters
and related physicalprocesses？
2) Response andfeedback of water cycle
to global changes？

Thank You!
WCOM(Welcome)