Diurnal cycle of land surface temperature in a desert encroachment zone as observed from satellites

Details Diurnal cycle of land surface temperature in a desert encroachment zone as observed from satellites Diurnal cycle of land surface temperature in a desert encroachment zone as observed from satellites

Jun 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007georl..3411809p&link_type=abstract

Geophysical Research Letters, Volume 34, Issue 11, CiteID L11809

Physics

2

Atmospheric Composition And Structure: General Or Miscellaneous, Biogeosciences: Remote Sensing, Biogeosciences: General Or Miscellaneous

Scientific paper

Climate variability in the African Soudano-Sahel savanna zone has attracted much attention because of the persistence of anomalously low rainfall. Past efforts to monitor the climate of this region have focused on rainfall and vegetation conditions, while land surface temperature (LST) has received less attention. Remote sensing of LST is feasible and possible at global scale. Most remotely sensed estimates of LST are based on the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) that are limited in their ability to capture the full diurnal cycle. Although more frequent observations are available from past geostationary satellites, their spatial resolution is coarser than that of polar orbiting satellites. In this study, the improved capabilities of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the METEOSAT Second Generation (MSG) instrument are used to remotely sense the LST in the African Soudano-Sahel savanna zone at a resolution of 3 km and 15 minutes. In support of the Radiative Atmospheric Divergence using the ARM Mobile Facility (AMF), GERB and AMMA Stations (RADAGAST) project, African Monsoon Multidisciplinary Analyses (AMMA) project and the Department of Energy's Atmospheric Radiation Measurement (ARM) program, the ARM Mobile Facility was deployed during 2006 in this climatically sensitive region, thereby providing a unique opportunity to evaluate remotely sensed algorithms for deriving LST.

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