Global estimation of the 1-m soil water content using microwave measurements from satellite

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Measuring Soil Water Contents at Different Scales – FZ Jülich – 17^th November 2005

Global estimation of the

1-m soil water content using microwave measurements from

satellite

R. Lindau & C. Simmer

University of Bonn

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Soil Moisture Measurements

 50 stations in the former SU provide:

• Soil moisture in the uppermost meter

• Total number: 17748

• Frequency: 10-daily

• Period: 1952 - 1985

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ANOVA

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Illustration of decomposition

Total variance External variance Internal variance

= Variance between + Mean variance the means of the within the

subsamples subsamples

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ANOVA Soil Moisture

 Soil moisture within the uppermost meter measured at 48 stations in the former Soviet Union

 Total number of observations: 7009

 Total variance: 10682 mm

²

Varian ce in mm

²

Numbe r of bins

Error of the

total mean

Seemin g externa

l varianc

e

Error of external

means

Internal variance

True externa

l varianc

e

Relative external variance

Annual

Cycle 36 2 388 51 1034

3 338 3.16

%

Interstati

on 48 2 913

3 10 1558 912

3 85.40

%

Interannu

al 8 2 39 12 1065

4 26 0.25

%

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Local longtime means

single cumulative

Climatolog. rain 58.6 58.6

Soil texture 0.5 69.0

Vegetation 37.7 72.8

Terrain slope 2.8 73.0

73% of the soil moisture variance

is explained by four parameters :

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Temporal Anomalies

 In a second step 10 Ghz measurements are used to retrieve the remaining

temporal part of the variance.

 A correlation of 0.609 is

attained .

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Radio Frequency Interference

 Time series of 6 GHz

brightness temperature from SMMR in France

 Until 1981 the normal annual cycle is found.

 After 1981 the 6 Ghz signal is

completely unusable due to

noise.

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Westward intensification

The scatter is low in Sibiria, increases westward and

reaches maximum values near St.

Petersburg

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Two-step Retrieval

 Longterm local mean of soil moisture

• Longterm mean of precipitation

• Soil texture

• Vegetation density

• Terrain slope

 Anomaly against the longterm local mean

• Brightness temperatures at 10 Ghz

• Anomalies of rain and air temperature

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Soil Moisture

C lim at o lo g ic a l m ea n Temporal

anomalies

+

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Algorithm test Illinois

Verification by independent measurements from Illinois

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Algorithm test China

•

Underestimation of the mean soil moisture by

74mm. (200 mm / 274 mm)

•

Correlation low with 0.523

•

Mainly due to four desert

stations.

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10-years mean soil moisture

REMO Algorithm

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Oder Catchment

 REMO

• Stronger interannual variability

• Stronger annual cycle

• Annual cycle

delayed

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AMSR Data

August

• Humid winter in SH

• Constant moisture in tropics

• Summer monsoon in NH

• Dry-out in NH mid-latitudes

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Annual Cycle 2003

• Tropics:

constant moisture

• India:

Monsoon increase

• Europe:

Summer dry-out

But spatial differences dominate anyhow

Brazzaville Bombay

Budapest

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Conclusions

 Pure spatial variance dominates the soil moisture variability

 Two step algorithm for soil moisture

• Local longterm mean

• Anomalie (temporal variation at each site)

 10 GHz channel is used, because 6 GHz is disturbed by RFI

 Verification of the algorithm by Illinois and Chinese data

 Application

• Validation of REMO (1979-1988) with SMMR

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Chinese Soil Moisture Data

 40 Stations

 Measurements of 1m soil

moisture for the period 1981 -

1999

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Comparison of monthly maps

REMO Dez 1979 Algo

REMO Sep 1985 Algo

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4 Input Parameters

Original resolution:

• Vegetation 0.01°

• Rain 2.5°

• Soil texture 1.0°

• Terrain slope 1.0°

w(d) = exp(-d/d

₀

)

d

0

= 50 km

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