HR: 0800h
AN: B21A-0042    [Abstracts]
TI: A Practical Method for Retrieving Land Surface Temperature from AMSR-E over the Amazon Forest
AU: * Gao, H
EM: huilin.gao@eas.gatech.edu
AF: Georgia Institute of Technology, School of Earth and Atmospheric Sciences 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Fu, R
EM: fu@eas.gatech.edu
AF: Georgia Institute of Technology, School of Earth and Atmospheric Sciences 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Dickinson, R
EM: robted@eas.gatech.edu
AF: Georgia Institute of Technology, School of Earth and Atmospheric Sciences 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Negrón Juárez, R I
EM: rjuarez@tulane.edu
AF: Tulane University, Ecology and Evolutionary Biology Tulane University 6823 St. Charles Ave. Rm# 400 Boggs Center, New Orleans, LA 70118-5698, United States
AB: Remote sensing of land surface temperature (LST) using infrared sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is only capable of retrieval under clear-sky conditions. Such LST observations over tropical forests are very limited due to clouds and rainfall, especially during the wet season, and high atmospheric water vapor content. In comparison, low frequency microwave radiances are minimally influenced by meteorological conditions. Exploring this advantage, we have developed an algorithm to retrieve LST over the Amazonian forest. The algorithm uses multi-frequency polarized microwave brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) on NASA's Earth Observing System. Relationships between polarization ratio and surface emissivity are established for forested and non-forested areas, such that LST can be calculated solely from microwave radiance. Results are presented over three time scales: at each orbit, daily, and monthly. Results are evaluated by comparing with available air temperature records on daily and monthly intervals. Our findings indicate that the AMSR-E derived LST agrees well with in situ measurements. Results during the wet season over the tropical forest suggest that AMSR-E LST is robust under all-weather conditions and shows higher correlation to meteorological data (r=0.70) than infrared based LST approaches (r=0.42).
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting