Operational Remote Sensing of Aerosols over Land to Account for Directional Effects.
The assumption that the ground is a Lambertian reflector is commonly adopted in operational atmospheric corrections of spaceborne sensors. Through a simple modeling of directional effects in radiative transfer following the second simulation of the satellite signal in the solar spectrum (6S) approach, we propose an operational method to account for the departure from Lambertian behavior of a reflector covered by a scattering medium. This method relies on the computation of coupling terms between the reflecting and the scattering media and is able to deal with a two-layer atmosphere. We focus on the difficult problem of aerosol remote sensing over land. One popular sensing method relies on observations over dense dark vegetation, for which the surface reflectance is low and quite well defined in the blue and in the red. Therefore a study was made for three cases: (1) dark vegetation covered by atmospheric aerosols, (2) atmospheric aerosols covered by molecules, and finally (3) dark vegetation covered by atmospheric aerosols covered by molecules. Comparisons of top-of-the-atmosphere reflectances computed with our modeling and reference computations made with the successive-order-of-scattering code show the robustness of the modeling in the blue and in the red for aerosol optical thicknesses as great as 0.6 and solar zenith angles as large as 60 degrees . The model begins to fail only in the blue for large solar zenith angles. The benefits expected for aerosol remote sensing over land are evaluated with an aerosol retrieval scheme developed for the Medium-Resolution Imaging Spectrometer. The main result is a better constraint on the aerosol model with inclusion of directional effects and a weaker effect on the optical thickness of the retrieved aerosol. The directional scheme is then applied to the aerosol remote-sensing problem in actual Indian Remote Sensing Satellite P3/Modular Optoelectronic Scanner images over land and shows significant improvement compared with a Lambertian algorithm. Moreover, it confirms our main theoretical conclusion.
Ramon
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Santer
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