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AMSR-E Soil Moisture Retrievals Using the SCA During NAFE’06

AMSR-E Soil Moisture Retrievals Using the SCA During NAFE’06. T.J. Jackson and R. Bindlish USDA ARS Hydrology and Remote Sensing Lab September 22, 2008. Background. Single Channel Algorithm (SCA) was applied to AMSR-E data using approach designed for JAXA real time retrievals.

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AMSR-E Soil Moisture Retrievals Using the SCA During NAFE’06

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  1. AMSR-E Soil Moisture Retrievals Using the SCA During NAFE’06 T.J. Jackson and R. Bindlish USDA ARS Hydrology and Remote Sensing Lab September 22, 2008

  2. Background • Single Channel Algorithm (SCA) was applied to AMSR-E data using approach designed for JAXA real time retrievals. • SCA uses an estimate of vegetation water content (VWC) based on NDVI. • A key aspect of this approach is that vegetation effects are corrected using climatological (AVHRR) NDVI data in order to meet operational requirements. 

  3. Example of Annual vs. Climatological NDVI Variations • Variability in AVHRR NDVI from year to year for the Mongolia study area (ASSH Network).

  4. Location of VSM sites

  5. NAFE’06 Soil Moisture Results • Figure provided by Clara Draper: SCA retrievals and in situ observations. • Anomalous (high) soil moisture values during campaign • Hypothesis: Drought conditions resulted in low NDVI that was not accounted for in the climatological values. • This may also be associated with the version of the products, a correction of the temperature component was made that fixed some errors. See next graph.

  6. Updated SCA Retrievals • Several revisions to the SCA have been made in the period since the data were provided • Soil Temperature – The top 0.2 cm soil temperature was computed using 37V GHz observations (similar to De Jeu). The relationship was recomputed using data from LW, WG, LR and RC. Heat transfer process based of soil thermal properties was used to compute 1 cm soil temperature. Thermal properties of soil are dependent on soil texture. • Vegetation – The b parameter was optimized globally for each landcover. Optimization was done based on estimating physically reasonable dielectric constant for maximum number of AMSR-E observations (above rock and below water). • Flags • Most of the anomalous behavior has been eliminated • Example: Kyeamba (we do not have the ground data) • Appears to match up much better Revision Data originally provided

  7. Updated SCA Retrievals for NAFE’06

  8. Comparison of AVHRR and MODIS NDVI for June-December 2006 • MODIS products (16 day) were extracted for each of the 10 in situ sites (0.25 degree area averaged) Example: Kyeamba

  9. Interpretation of AVHRR and MODIS NDVI for June-December 2006 • For most sites • Before Oct. 1: AVHRR NDVI < MODIS NDVI • After Oct. 1: AVHRR NDVI > MODIS NDVI • Most significant deviations during NAFE’06 • This error would lead to over estimation of soil moisture.

  10. Summary • The SCA corrections eliminated the obvious errors in the NAFE’06 retrievals • Retrieved soil moisture looks good as compared to the observed (visual interpretation) • Suggest redoing analysis • There were deviations between the long term average NDVI and the 16 day MODIS products that were reasonable considering the weather conditions • However, this would need further evaluation AFTER a review of the revised products

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