1. SeaWiFS Views the Agulhas Retroflection Gene Feldman NASA GSFC, Laboratory for Hydrospheric Processes, SeaWiFS Project Office (gene.c.feldman@nasa.gov) This scene, collected on May 15, 2003, shows a region of high chlorophyll concentration protruding jet-like from the southern end of the African continent. This feature exists in a dynamic region of colliding currents and changing sea floor topography. Major currents in the region include the Agulhas, Antarctic Circumpolar, and Benguela currents. The same oceanographic conditions prevailed in March of 1999 when a SeaWiFS image very similar to the one above was collected.

2. SeaWiFS Views the Agulhas Retroflection

3. For the summer ice, the presence of liquid water on the ice surface greatly reduces the radiance and backscatter contribution from the sea ice. The traditional tracking method on satellite data fails when the signal-to-noise ratio is relatively low during the summer. A new preprocessing technique has been developed at GSFC to enhance the signal-to-noise ratio on AMSR-E 89 GHz high-resolution data. Then the wavelet analysis method is applied to track ice texture feature between daily AMSR-E images. Sea-ice motion has been processed from AMSR-E data in July and August 2002, and the preliminary results show consistent drifts compared with buoy. For example, attached is a sea-ice motion map on July 23, 2002 derived from AMSR-E data. The red arrows are daily buoy data and agree reasonably well with satellite derived motion vectors. The general circulation pattern can be clearly observed in this map. The background shows the radiance of AMSR-E data and the ice edge is determined by QuikSCAT data. This is the first time a daily summer sea-ice motion map of Arctic Basin has been derived from satellite data SUMMER SEA-ICE MOTION FROM AMSR-E Antony Liu and Yunhe Zhao NASA GSFC, Laboratory for Hydrospheric Processes, Oceans and Ice Branch (antony.liu@nasa.gov)

4. SUMMER SEA-ICE MOTION FROM AMSR-E Antony Liu and Yunhe Zhao NASA/GSFC Code 971

5. Goal is to improve our understanding and prediction of the dynamic seasonal impact of vegetation on accurate soil moisture retrieval Field data were taken throughout a corn crop growing season in 2002, with planting in mid-April, peak biomass in late July, and harvesting in October -- weekly quad-pol L & C band radar data from a truck-mounted system -- automated hourly dual-pol L band radiometer data from a new tower-mounted instrument Coincident vegetation (canopy geometry & water content) and soil moisture information were acquired using different manual and automated methods -- these data were used to validate the microwave soil moisture retrieved using an active/passive approach With accurate vegetation information, the microwave algorithm can retrieve soil moisture over a large change in biomass Development of new active/passive algorithms is continuing, which has direct relevance to soil moisture space missions Active/Passive Microwave Remote Sensing for Soil Moisture Retrieval through a Growing Season *P. O’Neill, A. Joseph, G. De Lannoy, R. Lang, C. Utku, E. Kim, P. Houser, and T. Gish *NASA GSFC, Laboratory for Hydrospheric Processes, Hydrological Sciences Branch (peggy.oneill@nasa.gov)

6. MICROWAVE INSTRUMENTS Truck-mounted Radar -- two frequencies (1.6 and 4.75 GHz) -- four polarizations (HH, VV, HV, VH) -- three nadir angles (15, 35, 55 deg) -- 120-deg azimuthal sweep -- 12-m boom height -- weekly measurements Tower-mounted Radiometer (Lrad) -- single frequency (1.4 GHz) -- two polarizations (H, V) -- five nadir angles (25, 35, 45, 55, 60 deg) -- three azimuthal positions -- ~17-m tower height -- continuous measurements

7. -- radar measurements, a vegetation scattering model, and canopy geometry data are used to estimate vegetation attenuation -- vegetation attenuation data are converted into transmissivity curves for the growing season Vegetation Effect on Soil Moisture Signal

8. Soil moisture is retrieved by solving the brightness temperature equation for R S given vegetation transmissivity and scattering information, and then using Fresnel and dielectric-SM relationships to estimate soil moisture: TB C = [( 1 + R S  ) ( 1 -  ) ( 1 -  )] T V + ( 1 – R S )  T S

9. Simulating the rain and raindrop size distribution R. Meneghini NASA GSFC, Laboratory for Hydrospheric Processes, Microwave Sensors Branch (robert.meneghini@nasa.gov) Realistic simulations of rain and size distribution (DSD) needed for radar- radiometer algorithm development Model can be used to characterize the spatial-temporal properties of rain and DSD –Applicable to TRMM & GPM Validation Studies –Provides concise description of spatial-temporal variation of rain characteristics Rain Rate spatial model developed from TRMM PR overpasses of 5 0 x5 0 lat-long boxes

10. Two TRMM PR overpasses in 10 0 x10 0 region of Florida coast (left) and corresponding spatial correlation of rain (right) Average spatial correlation of the rain field from a set of overpasses during the month (circles) with fitting fnc (solid)

11. Two simulated rain fields (left) and corresponding spatial correlations (right) based on measured rain statistics