HR: 08:45h
AN: H21C-04    [PDF]
TI: Using TRMM/TMI to retrieve soil moisture over southern United States from 1998 to 2002: results and validation
AU: * Gao, H
EM: huiling@princeton.edu
AF: Princeton University, Department of Civil and Environmental Engineering Princeton University, Princeton, NJ 08544 United States
AU: Wood, E F
EM: efwood@runoff.princeton.edu
AF: Princeton University, Department of Civil and Environmental Engineering Princeton University, Princeton, NJ 08544 United States
AU: Drusch, M
EM: dar@ecmwf.int
AF: ECMWF, ECMWF Shinfield Park, Reading, RG2 9AX United Kingdom
AU: McCabe, M
EM: mmccabe@princeton.edu
AF: Princeton University, Department of Civil and Environmental Engineering Princeton University, Princeton, NJ 08544 United States
AU: Jackson, T J
EM: tjackson@hydrolab.arsusda.gov
AF: USDA ARS Hydrology and Remote Sensing Lab, USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD 20705 United States
AU: Bindlish, R
EM: bindlish@hydrolab.arsusda.gov
AF: USDA ARS Hydrology and Remote Sensing Lab, USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD 20705 United States
AB: Operational soil moisture products from passive microwave satellite remote sensing are expected to improve our understanding of land-atmospheric interactions. The Tropical Rainfall Measuring Mission (TRMM) satellite launched in November, 1997, carries a microwave imager, offering one of two spaceborne sensors sensitive to soil moisture changes. The Advanced Microwave Scanning Radiometer on board the EOS-Aqua satellite provides another C\&X band sensor, however there have been issues related to Radio Frequency Interference (RFI), which affect results from the C band. In this presentation, a Land Surface Microwave Emission Model (LSMEM) is used to retrieve surface soil moisture over southern United States from TRMM/TMI.10.65GHz horizontal polarized brightness temperature. Land surface temperatures required for model simulation are derived from validated Variable Infiltration Capacity (VIC) model outputs, driven primarily by the North American Land Data Assimilation System (NLDAS). Other variables and parameters (soil texture, soil salinity, soil surface roughness, vegetation water content, vegetation structure parameter and atmospheric contribution, etc. ) necessary for model operation come from operational sources. Soil moisture was estimated over southern United States from 1998 to 2002, with a sampled resolution of 1/8 degree. The results are compared with soil moisture from prediction from VIC model outputs (10cm depth) and Oklahoma Mesonet observations for validation purposes over the Southern Great Plains. While the three soil moisture datasets have shown consistent patterns in the spatial and temporal domains, TMI soil moisture demonstrates a large dynamic range compared to the other datasets, a result of the thin soil layer over which soil moisture is sensed. The final soil moisture product is determined after masking out frozen soil, snow covered area, precipitation and heavily vegetated areas to produce a useful and needed data source.
DE: 0933 Remote sensing
DE: 1719 Hydrology
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2003 Fall Meeting