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Atmospheric particles (aerosols) are the objective of intensive research. In addition to effects on our health, they also have a significant influence on climate. Aerosols can be measured in-situ or be retrieved using optical remote sensing instruments. Such instruments measure the upwelling solar radiance, which is scattered in the atmosphere as well as partially reflected from the Earth’s surface. The separation of measured radiance into these components is one of the great challenges for quantitative remote sensing. A radiative transfer algorithm was developed in the course of this thesis to generate an approximate solution to this problem. It combines analytical equations in a novel way with parameterizations and other approximations to achieve fast computations. An extended version of this algorithm was developed specifically to retrieve aerosol optical depth. Interesting findings were derived from this algorithm: e.g. the influence of surface albedo and its uncertainty on aerosol retrieval. The thesis is dived into the following three parts: In the first part, an analysis of the instrument performance requirements for aerosol retrieval is provided. This entirely theoretical study shows that a high sensor sensitivity is needed with a signal to noise ratio on the order of 102 to 103 . Even higher signal to noise ratio is required for reliable retrievals over bright surfaces with a surface albedo greater than 0.3. In the second part, the proposed fast algorithm is described in detail. The underlying equations are able to approximate most effects of the atmosphere and surface on the solar radiation. Comparisons with a widely used and recognized radiative transfer model show its fast computation and accuracy. In the last part, a promising application of the suggested aerosol retrieval algorithm is presented. Tests with synthetic and real remote sensing data show its efficiency and relatively high accuracy. The algorithm is then used to quantify the influence of the surface

Atmospheric particles (aerosols) are the objective of intensive research. In addition to effects on our health, they also have a significant influence on climate. Aerosols can be measured in-situ or be retrieved using optical remote sensing instruments. Such instruments measure the upwelling solar radiance, which is scattered in the atmosphere as well as partially reflected from the Earth’s surface. The separation of measured radiance into these components is one of the great challenges for quantitative remote sensing. A radiative transfer algorithm was developed in the course of this thesis to generate an approximate solution to this problem. It combines analytical equations in a novel way with parameterizations and other approximations to achieve fast computations. An extended version of this algorithm was developed specifically to retrieve aerosol optical depth. Interesting findings were derived from this algorithm: e.g. the influence of surface albedo and its uncertainty on aerosol retrieval. The thesis is dived into the following three parts: In the first part, an analysis of the instrument performance requirements for aerosol retrieval is provided. This entirely theoretical study shows that a high sensor sensitivity is needed with a signal to noise ratio on the order of 102 to 103. Even higher signal to noise ratio is required for reliable retrievals over bright surfaces with a surface albedo greater than 0.3. In the second part, the proposed fast algorithm is described in detail. The underlying equations are able to approximate most effects of the atmosphere and surface on the solar radiation. Comparisons with a widely used and recognized radiative transfer model show its fast computation and accuracy. In the last part, a promising application of the suggested aerosol retrieval algorithm is presented. Tests with synthetic and real remote sensing data show its efficiency and relatively high accuracy. The algorithm is then used to quantify the influence of the surface r