Liang Quantitative Remote Sensing of Land Surfaces

1. Auflage 2005
ISBN: 978-0-471-72371-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band 1, 560 Seiten, E-Book

Reihe: Wiley Series in Remote Sensing and Image Processing

Processing the vast amounts of data on the Earth's land surfaceenvironment generated by NASA's and other international satelliteprograms is a significant challenge. Filling a gap between thetheoretical, physically-based modelling and specific applications,this in-depth study presents practical quantitative algorithms forestimating various land surface variables from remotely sensedobservations.
A concise review of the basic principles of optical remote sensingas well as practical algorithms for estimating land surfacevariables quantitatively from remotely sensed observations.
Emphasizes both the basic principles of optical remote sensing andpractical algorithms for estimating land surface variablesquantitatively from remotely sensed observations
Presents the current physical understanding of remote sensing as asystem with a focus on radiative transfer modelling of theatmosphere, canopy, soil and snow
Gathers the state of the art quantitative algorithms for sensorcalibration, atmospheric and topographic correction, estimation ofa variety of biophysical and geoph ysical variables, andfour-dimensional data assimilation

Autoren/Hrsg.

Liang, Shunlin S.

Inhaltsverzeichnis

Preface.
Acronyms.
Chapter 1. Introduction.
1.1 Quantitative Models in Optical remote Sensing.
1.2 Basic Concepts.
1.3 Remote Sensing Modeling System.
1.4 Summary.
1.5 References.
Chapter 2. Atmospheric Shortwave Radiative Transfer Modeling.
2.1 Radiative Transfer Equation.
2.2 Surface Statistical BRDF Models.
2.3 Atmospheric Optical Properties.
2.4 Solving Radiative Transfer Equations.
2.5 Approximate Representation for Incorporating Surface BRDF.
2.6 Summary.
2.7 References.
Chapter 3. Canopy Reflectance Modeling.
3.1 Canopy Radiative Transfer Formulation.
3.2 Leaf Optical Models.
3.3 Solving Radiative Transfer Equations.
3.4 Geometric Optical Models.
3.5 Computer Simulation Models.
3.6 Summary.
3.7 References.
Chapter 4. Soil and Snow Reflectance Modeling.
4.1 Single Scattering Properties of Snow and Soil.
4.2 Multiple Scattering Solutions for Angular Reflectance from Snow and Soil.
4.3 Geome tric Optical Modeling.
4.4 Inversion of Snow Parameters.
4.5 Practical Issues.
4.6 Summary.
4.7 References.
Chapter 5. Satellite Sensor Radiometric Calibration.
5.1 Background.
5.2 Post-launch Calibration Methods.
5.3 Calibration Coefficients for Landsat TM and AVHRR Reflective Bands.
5.4 Summary.
5.6 References.
Chapter 6. Atmospheric Correction.
6.1 Introduction.
6.2 Methods for Correcting Single Viewing-angle Imagery.
6.3 Methods for Correcting Multiangular Observations.
6.4 Methods for Estimating Total Column Water Vapor Content.
6.5 Summary.
6.6 References.
Chapter 7. Topographic Correction Methods.
7.1 Introduction.
7.2 Cosine Correction Algorithms.
7.3 IPW Method.
7.4 Shadowing Function Algorithms.
7.5 DEM Data and Generation.
7.6 Summary.
7.7 References.
Chapter 8. Estimation of Land Surface Biophysical variables.
8.1 Statistical Methods.
8.2 Optimization Inversion Method.
8.3 Generic Algorithm (GA).
8.4 Table Look-up Methods.
8.5 Hybrid Inversion Methods.
8.6 Comparisons of Different Inversion Methods.
8.7 Summary.
8.8 References.
Chapter 9. Estimation of Surface Radiation Budget: I. Broadband Albedo.
9.1 Introduction.
9.2 Broadband Albedo Characteristics.
9.3 Narrowband to Broadband Conversion.
9.4 Direct Estimation of Surface Broadband Albedos.
9.5 Diurnal Cycle Modeling.
9.6 Summary.
9.7 References.
Chapter 10. Estimation of Surface Radiation Budget (II): Longwave.
10.1 Introduction.
10.2 Monochromatic Radiative Transfer Formulation and Solutions.
10.3 Line-by-line Methods.
10.4 Band Models.
10.5 Correlated k-Distribution Methods.
10.6 Atmospheric Correction Methods.
10.7 Split-window Algorithm for Estimating LST.
10.8 Multispectral Algorithms for Separating Temperature and Emissivity.
10.9 Computing Broadband Emissivity.
10.10 Surface Energy Balance Modeling.
10.11 Summary.
10.12 References.
Chapter 11. Four-Dimensional (4D) Data Assimilation.
11.1 Introduction.
11.2 Assimilation Algorithms.
11.3 Minimization Algorithms.
11.4 Data Assimilation in Hydrology.
11.5 Data Assimilationdata with Crop Growth Models.
11.6 Summary.
11.7 References.
Chapter 12. Validation and Spatial Scaling.
12.1 Rationale of Validation.
12.2 Validation Methodology.
12.3 Spatial Scaling Techniques.
12.4 Summary.
12.5 References.
Chapter 13. Applications.
13.1 Methodologies for Integrating Remote Sensing with Ecological Process Models.
13.2 Agricultural Applications.
13.3 "Urban Heat Island" Effects.
13.4 Carbon Cycle Studies.
13.5 Land-atmospheric Interaction.
13.6 Summary.
References.
Appendix.
CD-ROM Content.
Data Directory
Software Directory.
Index.

Über Autor(innen)

SHUNLIN LIANG, PhD, is an associate professor in the Department of Geography at the University of Maryland, where he teaches courses in remote sensing, quantitative spatial analysis, and computer cartography. He is the Associate Editor for IEEE Transactions on Geoscience and Remote Sensing and the coeditor of Geographic Information Science.