Summary
Contents
Subject index
Remote sensing acquires and interprets small or large-scale data about the Earth from a distance. Using a wide range of spatial, spectral, temporal, and radiometric scales remote sensing is a large and diverse field for which this Handbook will be the key research reference. This Handbook is organized in four key sections: • Interactions of Electromagnetic Radiation with the Terrestrial Environment: chapters on Visible, Near-IR and Shortwave IR; Middle IR (3-5 micrometers); Thermal IR; Microwave • Digital sensors and Image Characteristics: chapters on Sensor Technology; Coarse Spatial Resolution Optical Sensors; Medium Spatial Resolution Optical Sensors; Fine Spatial Resolution Optical Sensors; Video Imaging and Multispectral Digital Photography; Hyperspectral Sensors; Radar and Passive Microwave Sensors; Lidar • Remote Sensing Analysis: Design and Implementation: chapters on Image Pre-Processing; Ground Data Collection; Integration with GIS; Quantitative Models in Remote Sensing; Validation and accuracy assessment; • Remote Sensing Analysis: Applications: LITHOSPHERIC SCIENCES: chapters on Topography; Geology; Soils; PLANT SCIENCES: Vegetation; Agriculture; HYDROSPHERIC and CRYSOPHERIC SCIENCES: Hydrosphere: Fresh and Ocean Water; Cryosphere; GLOBAL CHANGE AND HUMAN ENVIRONMENTS: Earth Systems; Human Environments & Links to the Social Sciences; Real Time Monitoring Systems and Disaster Management; Land Cover Change Illustrated throughout, an essential resource for the analysis of remotely sensed data, The SAGE Handbook of Remote Sensing provides researchers with a definitive statement of the core concepts and methodologies in the discipline.
Quantitative Models and Inversion in Optical Remote Sensing
Quantitative Models and Inversion in Optical Remote Sensing
Keywords
- radiative transfer
- land
- modeling
- inversion
- biogeophysical parameters.
Introduction
In the past several decades, vast amounts of remotely sensed Earth observations have been acquired and accumulated. With a series of new satellite programs planned, the data volume will continue to increase. Automating the procedures for processing and analyzing these data is critical. Furthermore, various numerical models characterizing Earth's environments, each associated with a different decision support system, have to be calibrated and run with spatially and temporally explicit data sets produced only from remotely sensed data at the appropriate scales.
Quantitative estimation of land surface variables from satellite observations is challenging. It requires not only the understanding of the remotely sensed signals through the physical modeling approaches, but also ...
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