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Uni Bonn contributions to SMOS Validation

This article discusses the detection of radio frequency interference (RFI) in SMOS satellite data and compares SMOS soil moisture measurements with existing data from GSMDB and AMSR. The study also includes the investigation of signal noise due to rain and vegetation. The findings highlight the importance of interpreting validations with point measurements properly.

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Uni Bonn contributions to SMOS Validation

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  1. Uni Bonn contributions to SMOS Validation Ralf Lindau Bonn University SMOS Workshop – Hamburg, 9.-10. November 2006

  2. Bonn contributions • Detection of Radio Frequency Interference • Found in SMMR, AMSR etc. • Expected also in SMOS • Comparison of SMOS soil moisture with existing data • Direct measurements from GSMDB • AMSR-derived SM • Pixel-intern variance • Necessary to interprete the intended validations with point measurements properly. • build and operate a transportable polarized L-Band radiometer • Investigate signal noise due to rain and vegetation SMOS Workshop – Hamburg, 9.-10. November 2006

  3. 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. SMOS Workshop – Hamburg, 9.-10. November 2006

  4. SMMR RFI in 6 GHz The scatter is low inSibiria, increases westward and reaches maximum values near St. Petersburg SMOS Workshop – Hamburg, 9.-10. November 2006

  5. ASMR RFI In Europe the 10 GHz channel is completely noisy in England and Italy. In the USA the 6 GHz measurements are corrupted over New York, Boulder etc. SMOS Workshop – Hamburg, 9.-10. November 2006

  6. AMSR 10 GHz RFI 250 K • RFI is detectable by • high monthly averages • high monthly stddev. 300 K SMOS Workshop – Hamburg, 9.-10. November 2006

  7. Simple RFI Scheme • TB > 260 K • Stddev > 5 K • DTB > 20 K compared to a 10° x 30° surrounding SMOS Workshop – Hamburg, 9.-10. November 2006

  8. Soil Moisture from AMSR Longtime mean + 10-day anomalies SMOS Workshop – Hamburg, 9.-10. November 2006

  9. 2-Step Retrieval SMOS Workshop – Hamburg, 9.-10. November 2006

  10. Pixel-intern Variance Belarussian soil moisture data 21 stations, 10 daily during about 10 years Average spatially and compare the reduced variance to the total variance 40% are left for 400-km-Pixels 59% are left for SMMR-Pixels The left external variance is equal to the maximum correlation between point measurements and area averages SMOS Workshop – Hamburg, 9.-10. November 2006

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