slide 1/32noaa soil moisture/soil temperature workshop, oak ridge, tn, 3-5 march, 2009 value of...

/32NOAA Soil Moisture/Soil Temperature Workshop, Oak Ridge, TN, 3-5 March, 2009

Value of Ground Network Observations in Value of Ground Network Observations in Development of Satellite Soil Moisture Development of Satellite Soil Moisture

Data ProductsData Products

X. ZhanX. Zhan11, J. Liu, J. Liu11, M. Cosh, M. Cosh22, T. Jackson, T. Jackson22,, andand Y. YuY. Yu11

1 1 NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MDNOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD22 USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MDUSDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD


OUTLINEOUTLINE

SatelliteSatellite SM data SM data

GroundGround SM and ST observations SM and ST observations

ResultsResults and and issuesissues in comparing them in comparing them

SuggestionsSuggestions for USCRN for USCRN


VUT ESCAT/ASCAT (Wagner et al, 1999)VUT ESCAT/ASCAT (Wagner et al, 1999)

USDA TMI (Bindlish et al, 2003)USDA TMI (Bindlish et al, 2003)

Princeton TMI (Gao et al, 2006)Princeton TMI (Gao et al, 2006)

NASA AMSR-E (Njoku et al, 2003)NASA AMSR-E (Njoku et al, 2003)

USDA AMSR-E (Jackson et al, 2007)USDA AMSR-E (Jackson et al, 2007)

VUA AMSR-E (Owe et al, 2008)VUA AMSR-E (Owe et al, 2008)

USDA WindSat (Jackson et al, 2008)USDA WindSat (Jackson et al, 2008)

NRL WindSat (Li et al, 2008)NRL WindSat (Li et al, 2008)

Satellite Soil Moisture Data Products:Satellite Soil Moisture Data Products:


USDA TMI (Bindlish et al, 2003)USDA TMI (Bindlish et al, 2003) ::

Daily Daily estimates, estimates,

from July 06 to from July 06 to July 21, 1999 July 21, 1999

0.0 – 0.52%v/v0.0 – 0.52%v/v


Princeton University TMI (Gao et al, 2006)Princeton University TMI (Gao et al, 2006) ::

Jan. 1, 1999 with quality masks appliedJan. 1, 1999 with quality masks applied


NASA AMSR-E (Njoku et al, 2003)NASA AMSR-E (Njoku et al, 2003) ::

Within US: Within US: 0.1 – 0.2 v/v0.1 – 0.2 v/v


VUA-GSFC AMSR-E (Owe et al, 2008)VUA-GSFC AMSR-E (Owe et al, 2008) ::

Monthly for Monthly for July 2003: July 2003:

Top: 6.9GHzTop: 6.9GHzBottom: 10.7 Bottom: 10.7

GHzGHz


USDA WindSat (Jackson et al, 2008)USDA WindSat (Jackson et al, 2008) ::

WindSat global WindSat global volumetric soil volumetric soil

moisture (%) for moisture (%) for July 30, 2005.July 30, 2005.

0.0 – 0.5 v/v0.0 – 0.5 v/v


NRL WindSat (Li et al, 2008)NRL WindSat (Li et al, 2008) ::

WindSat global volumetric soil moisture (%) and vegetation WindSat global volumetric soil moisture (%) and vegetation water content (kg/m2) retrievals for 1 – 12 September 2003.water content (kg/m2) retrievals for 1 – 12 September 2003.


MetOp ASCAT (Wagner et al, 1999)MetOp ASCAT (Wagner et al, 1999) ::

VUT ASCAT soil moisture is actually soil wetness, could be converted VUT ASCAT soil moisture is actually soil wetness, could be converted to volumetric soil moisture by dividing them with their soil porosityto volumetric soil moisture by dividing them with their soil porosity


Watershed SM Vitel Network (2001- present)Watershed SM Vitel Network (2001- present)

Soil Climate Analysis Network (1996- present)Soil Climate Analysis Network (1996- present)

Surface Radiation Budget Network (1993- present)Surface Radiation Budget Network (1993- present)

US Climate Reference Network (2002- present)US Climate Reference Network (2002- present)

Ground SM & ST Observations:Ground SM & ST Observations:


USDA-ARS Watershed SM Vitel Network:USDA-ARS Watershed SM Vitel Network:

LW: Little Washita, OK

WG: Walnut Gulch, AZ

AMSR-E U.S. Soil Moisture Validation SitesAMSR-E U.S. Soil Moisture Validation Sites

RC: Reynolds Creek, ID

LR: Little River, GA


USDA-ARS Watershed SM Vitel Network:USDA-ARS Watershed SM Vitel Network:

Multiple sites within Multiple sites within a satellite footprinta satellite footprint

Rain gauge overlay Rain gauge overlay SM sitesSM sites

Multiple layers (5cm, Multiple layers (5cm, 15cm, 30cm)15cm, 30cm)

Continuous data Continuous data sampling (30 min.)sampling (30 min.)

Stevens-Vitel Hydra Stevens-Vitel Hydra ProbesProbes

WG

LW

RC

LR


USDA-NRCS Soil Climate Analysis Network (SCAN):USDA-NRCS Soil Climate Analysis Network (SCAN):

Mostly single site in Mostly single site in a satellite footprinta satellite footprint

Rain gauge at the Rain gauge at the same sitesame site

Multiple layers (2”, Multiple layers (2”, 4”, 8”, 20”, 40”)4”, 8”, 20”, 40”)

Hourly dataHourly data

Hydra ProbesHydra Probes


NOAA Surface Radiation Budget Network (SurfRad):NOAA Surface Radiation Budget Network (SurfRad):

6 sites from 1995 6 sites from 1995 and 1 site from 2003and 1 site from 2003

Mainly solar and Mainly solar and thermal radiationthermal radiation

LST observational LST observational DataData

Sample per 1 or 3 Sample per 1 or 3 minutesminutes

Precision Precision Infrared Infrared RadiometerRadiometerss


NOAA Climate Reference Network (USCRN):NOAA Climate Reference Network (USCRN):

>100 stations with >100 stations with a few paired onesa few paired ones

Most climate Most climate variables variables including including precipitationprecipitation

SM/ST plannedSM/ST planned

SM/ST Sample SM/ST Sample freq ?freq ?

SM/ST sensors ?SM/ST sensors ?


Validating AMSR-E Soil Moisture Retrievals: Validating AMSR-E Soil Moisture Retrievals:

with with Watershed SM Vitel NetworkWatershed SM Vitel Network

(USDA-ARS)(USDA-ARS)


Validating AMSR-E Soil Moisture Retrievals: Validating AMSR-E Soil Moisture Retrievals:

with with SCAN Data SCAN Data (USDA-NRCS)(USDA-NRCS)


Validating Multiple Soil Moisture Retrievals: Validating Multiple Soil Moisture Retrievals:

with with SCAN Data SCAN Data (USDA-NRCS)(USDA-NRCS)


Evaluating LST Estimates (TEvaluating LST Estimates (Tskinskin) for SM Retrievals: ) for SM Retrievals:

TB,icmp= Tskin {er,p exp (-i/cos) +

(1 – ) [1 – exp (-i/cos)] [1 + Rr,i exp (-i/cos)]}

i = b *VWCRr,i = Rs exp(h cos2θ)

Rs = f(ε) -- Fresnel Equationε = g(SM) -- Mixing model

TB,iobs= TB06h , TB06v , TB10h , TB10v , TB18h , TB18v

}min{

26

1

,,2

i i

cmpiB

obsiB TT

Multi-channel Inversion (MCI) Algorithm (Njoku & Li, 1999):Multi-channel Inversion (MCI) Algorithm (Njoku & Li, 1999):


Evaluating LST Estimates (TEvaluating LST Estimates (Tskinskin) for SM Retrievals: ) for SM Retrievals:

TB10h = Ts [1 –Rr exp (-2 /cos)]

Rr = Rs exp(h cos2θ)

Rs = f(ε) -- Fresnel Equationε = g(SM) -- Mixing model

Ts = reg1(TB37v) or TsLSM

= b * VWCVWC = reg2(NDVI)

Single Channel Retrieval (SCR) Algorithm (Jackson, 1993):Single Channel Retrieval (SCR) Algorithm (Jackson, 1993):


Evaluating NCEP-GDAS TEvaluating NCEP-GDAS Tskinskin Estimates: Estimates:

with with SurfRadSurfRad(NOAA)(NOAA)


Evaluating TEvaluating Tskinskin Estimates from AMSR-E TB36v: Estimates from AMSR-E TB36v:


Issues in Satellite SM Validation with in situ Data: Issues in Satellite SM Validation with in situ Data:

1.1. Footprint Footprint representation/ representation/ heterogeneity heterogeneity issueissue



2.2. Hydra probe depth Hydra probe depth vs vs satellite sensor depthsatellite sensor depth

SCAN probe depth:SCAN probe depth:2”, 4”, 8”, 20”, 40”2”, 4”, 8”, 20”, 40”

USDA Vitel Network:USDA Vitel Network:5cm, 15cm, 30cm5cm, 15cm, 30cm

Noah LSM depth: 10cm, Noah LSM depth: 10cm, 40cm, 100cm, 200cm 40cm, 100cm, 200cm

AMSR-E (C-band) AMSR-E (C-band) sensible depth: < 2cmsensible depth: < 2cm

SMAP (L-band): < 5cmSMAP (L-band): < 5cm

USCRN: ?USCRN: ?



3.3. Hydra Probe Hydra Probe calibration calibration standardizationstandardization


Representation issue:Representation issue: Identify those sites with good Identify those sites with good area landscape heterogeneity for SM/ST sensor area landscape heterogeneity for SM/ST sensor installation if not all sites;installation if not all sites;

Probe depth issue: Probe depth issue: Consider future operational or long Consider future operational or long term C/X-band satellite sensors (MIS, AMSR2, GPM, term C/X-band satellite sensors (MIS, AMSR2, GPM, etc) as well as L-band sensors (SMOS, SMAP);etc) as well as L-band sensors (SMOS, SMAP);

Data quality issue:Data quality issue: Plan frequent sensor calibration Plan frequent sensor calibration based on timely data analysis;based on timely data analysis;

LST (TLST (Tskinskin): ): LST observations are desirable for both LST observations are desirable for both

satellite (SM/ST) data products validation and climate satellite (SM/ST) data products validation and climate monitoring;monitoring;

Data access: Data access: Open, timely, and convenient access to Open, timely, and convenient access to USCRN data benefits all of their potential applications USCRN data benefits all of their potential applications (drought monitoring, satellite data validation, etc)(drought monitoring, satellite data validation, etc)

SUGGESTIONS FOR USCRNSUGGESTIONS FOR USCRN


Currently availableCurrently available satellite SM data products satellite SM data products are are significantly differing from each other and their qualities significantly differing from each other and their qualities need to be improved for operational uses;need to be improved for operational uses;

SM ground measurements SM ground measurements are useful for satellite SM/ST are useful for satellite SM/ST data product validation and verification of land surface data product validation and verification of land surface model SM/ST data assimilation;model SM/ST data assimilation;

There are spaces for improving the ground SM/ST There are spaces for improving the ground SM/ST measurement qualitymeasurement quality with consideration to satellite with consideration to satellite footprint representation, sensor depth, calibration footprint representation, sensor depth, calibration standardization and open, timely, convenient data access;standardization and open, timely, convenient data access;

In addition to soil temperature measurements, In addition to soil temperature measurements, Land Land Surface Temperature (TSurface Temperature (Tskinskin)) observations are also observations are also

desirable for satellite (SM/ST) data product validations;desirable for satellite (SM/ST) data product validations;

SUMMARYSUMMARY

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