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

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Soil Moisture Measurements  50 stations in the former SU provide: Soil moisture in the uppermost meter Total number:17748 Frequency:10-daily Period:

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

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Illustration of decomposition Total variance External variance Internal variance = Variance between + Mean variance the means of the within the subsamples subsamples

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 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 Variance in mm 2 Number of bins Error of the total mean Seeming external variance Error of external means Internal variance True external variance Relative external variance Annual Cycle % Interstation % Interannual %

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Local longtime means singlecumulative Climatolog. rain58.6 Soil texture Vegetation Terrain slope % of the soil moisture variance is explained by four parameters :

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Temporal Anomalies  In a second step 10 Ghz measurements are used to retrieve the remaining temporal part of the variance.  A correlation of is attained.

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

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Westward intensification The scatter is low in Sibiria, increases westward and reaches maximum values near St. Petersburg

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

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Soil Moisture Climatological mean Temporal anomalies +

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Algorithm test Illinois Verification by independent measurements from Illinois On global scale the Illinois data set represents practically only one single site. However, this spot is retrieved accurately, lying on the 1-to-1 line.

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Algorithm test China Underestimation of the mean soil moisture by 74mm. (200 mm / 274 mm) Correlation low with Mainly due to four desert stations.

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November years mean soil moisture REMO Algorithm

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Oder Catchment  REMO Stronger interannual variability Stronger annual cycle Annual cycle delayed

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 AMSR Data August February Humid winter in SH Constant moisture in tropics Summer monsoon in NH Dry-out in NH mid-latitudes

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Annual Cycle 2003 Tropics: constant moisture India: Monsoon increase Europe: Summer dry-out But spatial differences dominate anyhow Brazzaville Bombay Budapest

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 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 ( ) with SMMR Global soil moisture fields ( ) using AMSR

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Chinese Soil Moisture Data  40 Stations  Measurements of 1m soil moisture for the period

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Comparison of monthly maps REMO Dez 1979 Algo REMO Sep 1985 Algo

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November Input Parameters Original resolution: Vegetation 0.01° Rain2.5° Soil texture1.0° Terrain slope1.0° w(d) = exp(-d/d 0 ) d 0 = 50 km

Measuring Soil Water Contents at Different Scales – FZ Jülich – 17 th November 2005 Difference REMO-Algorithm  REMO dry in South Europe and Scandinavia  REMO wet in Poland and the Ukraine  REMO extremly wet, where peat is prescribed