Future mid-infrared satellite missions exploring the Earth will feature advanced high spatial resolution and directional imaging instruments. Consistent end-to-end simulation of them is an important task, and is sometimes the only way to adapt and optimize a sensor and its observation
conditions, to choose and test algorithms for data processing, to estimate errors and to evaluate the capabilities of the whole sensor system. However, contrary to other wavelength ranges, the mid-infrared is highly dependent on atmospheric scattering and emission. Therefore, simulation of
atmospheric radiative transfer for remote sensing images will remain a challenging task, because few studies on this topic include a full treatment of atmospheric effects. With a given resolution and directional capabilities of the instrument, and combining with land surface temperature and
emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel, coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric
directional and adjacency effect). In this way, all major scattering and emission contributions of atmosphere were considered. Based on different atmospheric conditions and geometrical relations between the scene, the Sun and the sensor, simulated TOA radiance images were produced according
to simulated workflows, 10-m spatial resolution and a spectral range of 3.5-3.9 μm. Analysis of results indicates that the analytic model and adjacency effect model are more suitable for mid-infrared imaging simulation than other existing models. This paper describes the principle
of the two models, the applied methodology, the set-up of the actual image simulations, and then discusses the final results obtained.
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Document Type: Research Article
National Engineering Research Center for Information Technology in Agriculture, Beijing, PO Box 2449-26, Beijing, PR China,State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese
Academy of Sciences and Beijing Normal University, Beijing, PR China
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, PR China
National Engineering Research Center for Information Technology in Agriculture, Beijing, PO Box 2449-26, Beijing, PR China
Publication date: 2009-01-01
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