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Radiative transfer and aerosol remote sensing


Seidel, Felix C (2011). Radiative transfer and aerosol remote sensing. Zurich, CH: Remote Sensing Laboratories, Department of Geography, University of Zurich.

Abstract

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 instru- ments 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 radia- tive transfer model show its fast computation and accuracy. In the last part, a promis- ing 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 pro- vides a promising basis for many applications in the fields of remote sensing and cli- matology 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 dis- tribution 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.

Abstract

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 instru- ments 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 radia- tive transfer model show its fast computation and accuracy. In the last part, a promis- ing 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 pro- vides a promising basis for many applications in the fields of remote sensing and cli- matology 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 dis- tribution 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.

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Additional indexing

Item Type:Monograph
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:941
Language:English
Date:2011
Deposited On:19 Jul 2011 08:45
Last Modified:24 May 2019 03:10
Publisher:Remote Sensing Laboratories, Department of Geography, University of Zurich
Series Name:Remote Sensing Series
Volume:61
Number of Pages:99
ISBN:978-3-03703-027-1
Additional Information:This work was approved as a PhD thesis by the Faculty of Science of the University of Zurich in the spring semester 2011 on the basis of expert reviews by Prof. Dr. Michael E. Schaepmann, Dr. Alexander A. Kokhanovsky and Dr. Alexei I. Lyapustin
OA Status:Green
Official URL:http://www.geo.uzh.ch/fileadmin/files/content/abteilungen/rsl1/Publications/PhD_Theses/2011_FelixSeidel.pdf

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