Update status of soil moisture algorithm

2008.7.15 Joint AMSR Science Team Meeting, July 14-16, Telluride, CO

H.Fujii (JAXA/EORC) and T.Koike(UT)

Defect of Koike's Algorithm

"There are significant weaknesses in the JAXA product over vegetated sites" (T.J. Jackson, The GLI / AMSR workshop 2008 in Atami Japan, Jan.23,2008)

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Fig: Scatter plots of AMSR-E Monthly Soil moisture (y-axis) versus in-situ data (x-axis) at the Little River ,GA,USA

Match-upData Period January, 2003 -- December 2007 (60 months)

In-Situ: -data source: SCAN 2027 Little River, GA, U.S.A-sensor depth ; 5cm-sampling method: observed data which has closest timestamp to AMSR-E observation time within 1 hour

AMSR-E: -data source: JAXA product ver.5 Level2 -sampling method: closest FOV to the SCAN point within 5km range.

monthly volumetric soil moisture in %

Update log of Koike’s algorithm

• March, 2004 : algorithm ver.3.01 (product ver.2-5)– Radiative transfer model for soil layer

– Fractional vegetation cover , fc : TB= (1-fc)TBbare+ fcTB

vege

• March, 2008 : algorithm ver.5.11 (product ver.6)– Developing of the global data set of fractional vegetation cover, fc, (3

years average) from Terra/Aqua MODIS(LP DAAC).

– Reconfiguration of vegetation parameter set , b and , based on the result of Ground Based Experiment.

• Jun, 2008 : algorithm ver.5.33 (prototype product)– Expansion of data period of fc global data set ( Jun 2002 - May 2008)

– Reconfiguration of roughness parameter set , Q and h 　

Overview of Koike Algorithm

Roughness parameter, Q-h

AMSR-E / In-situ data

CEOP Mongolia Ref. Site

Vegetation parameter b-

Ground Based Experimentusing the Microwave Radiometer

Terra/Aqua MODISVegetation Indices (LP DAAC)

MOD13A2/MYD13A2(1km, 16days, v5)

NDVI

fc=1%fc=100%

・・・

Look-upTable

Global Data Set of fractional vegetation cover,fc

Period: Jun 2002 – May 2008Data size: 8 days, 0.05x0.05 gridResolution: equivalent size to AMSR-E 10GHz FOVfc model: Carlson and replay, 1997

Soil Moisture Algorithm

TBbare TB

vege

bare soil vegetated surface

Radiative Transfer Model

AMSR-E Global Soil Moisture Map

6-yeas average of July (2002-2007) , Descending

Comparison of AMSR-E Soil Moisture and Precipitation map

AMSR-E Soil Moisture- L2, Descending- Volumetric Soil Moisture [%]

Precipitation : - Total amount of precipitation for 48 hours before AMSR-E observation. - Data source: GSMaP MVK hourly (JST-CREST/GSMaP )

Validation 1:

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2003 Jan. Apr. Jul. Oct.

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0.8(a) Gaize

(b) B.Bolto (c) Little River

(a) Gaize, Tibet, China. (CEOP)location: 32.30N, 84.05E

(b) Balranald Bolto, NSW, Australira. (CEOP)location 34.658S, 143.549E

(c) Little River, GA, USA. (SCAN, NRCS)location: 31.50N, 82.45W

Seasonal Changes of EVI in 2003

Validation 1: New product (prototype)

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Number of DataDes. 214Asc. 200

Number of DataDes. 217Asc. 223

Number of DataDes. 200Asc. 198

(a) Gaize

(b) B.Bolto (c) Little River

(a) Tibet Gaize sensor depth 3cm (1.5 - 4.5cm)

(b) Balranald Boltosensor depth 0-7cm

(c) Little Riversensor depth 5cm (3-7cm)

Validation 1: JAXA product (ver.5)

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(a) Tibet Gaize sensor depth 3cm (1.5 - 4.5cm)

(b) Balranald Boltosensor depth 0-7cm

(c) Little Riversensor depth 5cm (3-7cm)

2003 Jan.

Number of DataDes. 93Asc. 167

Number of DataDes. 206Asc. 220

Number of DataDes. 187Asc. 184

(a) Gaize

(b) B.Bolto (c) Little River

Validation 2: Monthly Soil Moisture at LR.Algorithm Orbit Bias RMSE MAE

(a) JAXA ver.5

Des. 11.5 4.8 11.5

Asc. 9.0 4.2 9.0

(b) new (Prototype)

Des. 3.1 2.5 3.5

Asc. 2.9 2.4 3.5

Scatter plots of AMSR-E monthly averaged soil moisture(y-axis) versus in-situ data at Little River , GA in U.S.A.. The data period is from January 2003 to December 2007 (60 months).

unit: volumetric soil moisture in %

(b) New Product (prototype)

In-situ (SCAN 2027, LR, 5cm)

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(a) JAXA product ver.5

In-situ (SCAN 2027, LR, 5cm)

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Summary

• The new global data set of fractional vegetation cover, fc, is introduced into the algorithm

• The roughness parameter calibrated at Mongolia is used in the Ratiative Transfer Model.

• The algorithm validated at Little River and the result showed that the accuracy was improved by comparing the old algorithms.

Near future plan.

• Masking method for uniformly and dense vegetated area.

• Behavior of the algorithm in Sahara