Download Advances in Land Remote Sensing: System, Modeling, Inversion by Shunlin (Ed.) Liang, Shunlin Liang PDF

By Shunlin (Ed.) Liang, Shunlin Liang

This publication collects the overview papers from either technical periods and 3 dialogue panels of the ninth foreign Symposium on actual Measurements and Signatures in distant Sensing (ISPMSRS). It systematically summarizes the earlier achievements and identifies the frontier concerns because the learn time table for the close to destiny. It covers all facets of land distant sensing, from sensor structures, actual modeling, inversion algorithms, to numerous functions. The papers on distant sensing procedure overview the services of other sensor structures for estimating key land floor variables and the way they could top be superior and built-in successfully sooner or later. Papers on modeling and inversion overview the state of the art methodologies on actual modeling and the inversion algorithms for estimating a sequence of land floor variables. The papers on distant sensing software verify the present prestige of assorted purposes and speak about how larger to bridge the advance of remote-sensing technological know-how and expertise and useful purposes. Representing the neighborhood attempt and contributed via a crew of foreign major specialists, this necessary reference booklet for graduate scholars and practitioners of distant sensing additionally aids these engaged in educational study, govt and industry.

Included is a CD-ROM containing the complete color pictures that are revealed in Black and White within the book.

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Extra info for Advances in Land Remote Sensing: System, Modeling, Inversion and Application

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Under the assumption of spherical grains or randomly orientated particles, the volume scattering albedo is independent of the polarization. Therefore, the ratio for the first-order volume backscattering signals of VV and HH polarizations can be represented as a function of the local incidence angle and dielectric constant: DTH (θi , εs ) = Re [Tvvhh (θi , εs )]2 σvvvhh , = 2 (θ , ε ) hh σv Thh i s DTV (θi , εs ) = Re [Tvvhh (θi , εs )]2 σvvvhh = vvh Tvv2 (θi , εs ) σv The algorithm derived above requires no information about the volume scattering albedo or the surface roughness parameter.

There has also been significant improvement in the modeling of surface scattering characteristics. The Integral Equation Model (IEM) (Fung, 1994) and the more recent Advanced Integral Equation Model (AIEM) allow characterization of a wider range of the surface roughness conditions than past models. , 2003) have demonstrated significant improvement in modeling surface scattering and emission for microwave remote sensing of land surfaces. These efforts have established a fundamentally-improved understanding of the effects of snow physical parameters and underlying surface dielectric and roughness properties on the microwave measurements of snow-covered terrain, making it possible to characterize the microwave backscatter behaviors more accurately.

5 m at 37 GHz (a commonly used frequency). Moreover, the spatial resolution of the current passive microwave satellite sensors is too coarse to provide useful information at regional and drainage basin scales for hydrological investigations and applications. Active microwave sensors (radars), on the other hand, are sensitive to many snow parameters such as snow density, depth, grain size, free liquid water content, and snow-pack structures that are useful for hydrologic applications. Active microwave sensors, especially Synthetic Aperture Radar (SAR), have been used to estimate snow properties, such as snow wetness and snow water equivalency (SWE), and to discriminate snow with other surfaces.

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