HR: 0800h
AN: B31B-0220    [Abstracts]
TI: A Daily AVHRR Land Surface Temperature Data Set: Evidence of Directional Biases
AU: * Pinheiro, A C
EM: ana@hsb.gsfc.nasa.gov
AF: National Research Council, NASA GSFC Hydrological Sciences Branch, Greenbelt, MD 20771 United States
AU: Privette, J L
EM: Jeffrey.Privette@nasa.gov
AF: NASA GSFC, Biospheric Sciences Branch, Greenbelt, MD 20771 United States
AU: Mahoney, R
EM: robert.mahoney@ngc.com
AF: Northrop Grumman Coorporation, Northrop Grumman Coorporation, El Segundo, CA 90245 United States
AU: Tucker, C J
EM: Compton.Tucker@nasa.gov
AF: NASA GSFC, Biospheric Sciences Branch, Greenbelt, MD 20771 United States
AB: The NOAA AVHRR instrument has been monitoring the brightness temperature of the planet for more than 20 years. Split window algorithms convert these measurements to land surface temperature (LST) by correcting for atmospheric and surface emissivity effects. However, the low precision of LST retrievals -- associated with intractable variability -- has often hindered its wide use. In this study, we developed a 6-year daily (day and night) NOAA-14 AVHRR LST data set over continental Africa and investigate it for the presence of directional effects. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that significant AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including `hot spot' effects when no shadows are seen by the sensor. For example, a woodland case showed that LST measurements within the `hot-spot' geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch).
DE: 1620 Climate dynamics (3309)
DE: 1640 Remote sensing
SC: Biogeosciences [B]
MN: 2004 AGU Fall Meeting