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 reﬂectance factor on aerosol retrieval. Results concerning the so-called "critical" surface albedo indicate that it may signiﬁcantly complicate the aerosol retrieval problem. The presented algorithm for the fast and simple radiative transfer computation provides a promising basis for many applications in the ﬁelds of remote sensing and climatology whenever quick calculations and ﬂexibility are preferred. Pointwise aerosol retrievals using remote sensing data, as demonstrated in this thesis, is one example. It is possible to extend the proposed approach in the future to account for the spatial distribution of aerosols. Further, the accuracy of the radiative transfer computation can be enhanced by incorporating higher orders of scattering and a more comprehensive representation of the surface, with its inﬂuence on radiation, into account.