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Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing
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Sensitivity Analysis in Remote Sensing (Paperback, 2015 ed.)
Loot Price: R1,741
Discovery Miles 17 410
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Sensitivity Analysis in Remote Sensing (Paperback, 2015 ed.)
Series: SpringerBriefs in Earth Sciences
Expected to ship within 18 - 22 working days
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This book contains a detailed presentation of general principles of
sensitivity analysis as well as their applications to sample cases
of remote sensing experiments. An emphasis is made on applications
of adjoint problems, because they are more efficient in many
practical cases, although their formulation may seem
counterintuitive to a beginner. Special attention is paid to
forward problems based on higher-order partial differential
equations, where a novel matrix operator approach to formulation of
corresponding adjoint problems is presented. Sensitivity analysis
(SA) serves for quantitative models of physical objects the same
purpose, as differential calculus does for functions. SA provides
derivatives of model output parameters (observables) with respect
to input parameters. In remote sensing SA provides
computer-efficient means to compute the jacobians, matrices of
partial derivatives of observables with respect to the geophysical
parameters of interest. The jacobians are used to solve
corresponding inverse problems of remote sensing. They also play an
important role already while designing the remote sensing
experiment, where they are used to estimate the retrieval
uncertainties of the geophysical parameters with given measurement
errors of the instrument, thus providing means for formulations of
corresponding requirements to the specific remote sensing
instrument. If the quantitative models of geophysical objects can
be formulated in an analytic form, then sensitivity analysis is
reduced to differential calculus. But in most cases, the practical
geophysical models used in remote sensing are based on numerical
solutions of forward problems - differential equations with initial
and/or boundary conditions. As a result, these models cannot be
formulated in an analytic form and this is where the methods of SA
become indispensable. This book is intended for a wide audience.
The beginners in remote sensing could use it as a single source,
covering key issues of SA, from general principles, through
formulation of corresponding linearized and adjoint problems, to
practical applications to uncertainty analysis and inverse problems
in remote sensing. The experts, already active in the field, may
find useful the alternative formulations of some key issues of SA,
for example, use of individual observables, instead of a widespread
use of the cumulative cost function. The book also contains an
overview of author's matrix operator approach to formulation of
adjoint problems for forward problems based on the higher-order
partial differential equations. This approach still awaits its
publication in the periodic literature and thus may be of interest
to readership across all levels of expertise.
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