Quantitative Remote Sensing of Land Surfaces

A comprehensive resource of basic principles and practical algorithms Remote sensing of land surfaces has entered a new era. A series of operating satellites from the NASA Earth Observing System (EOS) program, other international programs, and commercial programs are producing tremendous volumes of data at significantly higher levels of measurement precision. In order to effectively interpret the data and estimate Earth surface variables, scientists require ever more sophisticated and targeted quantitative algorithms. Quantitative Remote Sensing of Land Surfaces fills this reference need, connecting theoretical, physically based modeling to specific applications. Shunlin Liang divides his much-needed resource into two parts. The first presents the current understanding of optical remote sensing with an emphasis on radiative transfer modeling of the atmosphere, canopy, soil, and snow. The second, greater part of the text, discusses a variety of practical algorithms for estimating land surface variables quantitatively. It includes state-of-the-art quantitative algorithms for: Sensor calibration Atmospheric and topographic correction Estimation of a variety of biophysical and geophysical variables Four-dimensional data assimilation The book cites more than 1,300 references, and the companion CD-ROM includes useful computer program codes and valuable data sets. The author assumes no special mathematical background beyond a good working knowledge of statistics, calculus, and linear algebra on an undergraduate level. Graduate students as well as practitioners of interdisciplinary research on the Earth's land surface environment will find Quantitative Remote Sensing of Land Surfaces to be a peerless addition to the professional literature.