Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI - Peres, L.F.; DaCamara, C.C. (IEEE transactions on geoscience and remote sensing, 2005)
Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 /spl mu/m) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from /spl plusmn/0.6% to /spl plusmn/24% and /spl plusmn/0.1% to /spl plusmn/33%, respectively, whereas the broadband spectrum errors lie between /spl plusmn/0.3% and /spl plusmn/7%. In the case of channels IR10.8 and IR12.0, 73% of the land surfaces within the MSG disk present relative errors less than /spl plusmn/1.5%, and almost all (26%) of the remaining areas have relative errors of /spl plusmn/2.0%. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required.