Land surface emissivity from high temporal resolution geostationary infrared imager radiances: Methodology and simulation studies

• Published in: Journal of Geophysical Research 2011-01, Vol.116 (D1), p.n/a, Article D01304
• Main Authors: Li, Jun, Li, Zhenglong, Jin, Xin, Schmit, Timothy J., Zhou, Lihang, Goldberg, Mitchell D.
• Format: Article
• Language: English
• Subjects: ABI; Earth sciences; Earth, ocean, space; Exact sciences and technology; land surface emissivity; retrieval; SEVIRI; time continuity
• ISSN: 0148-0227; 2156-2202
• DOI: 10.1029/2010JD014637

The time continuity of measurements from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation (MSG) Meteosat‐8/9 and from the Advanced Baseline Imager (ABI) on board the next generation of Geostationary Operational Environmental Satellite (GOES‐R) can be uniquely taken into account for infrared (IR) land surface emissivity (LSE) retrievals. The algorithm is based on the assumption that land surface temperature (LST) is temporally variable while the LSE is temporally invariable within a short period of time, i.e., a few hours. SEVIRI/ABI radiances from multiple time steps can be used to retrieve temporally invariable IR LSE and variable LST. The algorithm theoretical basis is described. Sensitivity studies with simulations show that (1) the algorithm is less sensitive to the first guesses of LST and the 8.7 μm LSE but quite sensitive to the first guesses of the 10.8 and 12 μm LSE, (2) the algorithm is weakly sensitive to the observational noise and radiative transfer calculation uncertainty (in the form of random noise), and (3) except for the 8.7 μm LSE and LST, the algorithm is weakly sensitive to the radiance biases from dust contamination but sensitive to the radiance biases in the 12 μm channel from the radiative transfer calculation. It is emphasized that the radiance biases from dust contamination are very difficult if not impossible to estimate due to the high temporal and spatial variations of the spatial distribution and optical properties of dust aerosol. It is also found that the algorithm is sensitive to the LST weighting functions rather than the sensor's local zenith angle; as long as the LST weighting functions are large enough, the retrieval precision is good.