Huge product rangeOver 140,000 books & equipment products
Rapid shippingUK & Worldwide
Pay in £, € or U.S.$By card, cheque, transfer, draft
Exceptional customer serviceGet specialist help and advice
Processing the vast amounts of data on the Earth's land surface environment generated by NASA's and other international satellite programs is a significant challenge. Filling a gap between the theoretical, physically--based modelling and specific applications, this in--depth study presents practical quantitative algorithms for estimating various land surface variables from remotely sensed observations. A concise review of the basic principles of optical remote sensing as well as practical algorithms for estimating land surface variables quantitatively from remotely sensed observations. Emphasizes both the basic principles of optical remote sensing and practical algorithms for estimating land surface variables quantitatively from remotely sensed observations Presents the current physical understanding of remote sensing as a system with a focus on radiative transfer modelling of the atmosphere, canopy, soil and snow Gathers the state of the art quantitative algorithms for sensor calibration, atmospheric and topographic correction, estimation of a variety of biophysical and geoph ysical variables, and four--dimensional data assimilation
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 DirectorySoftware Directory.Index.
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.
...a well-researched book that the environmental modelling community will find an indispensable reference. (Progress in Physical Geography, Vol.29, No.1, 2005) "...extremely well produced, with good layout, clear type, a useful list of acronyms and a comprehensive index...timely and refreshing..." (The Photogrammetric Record, December 2004) "This volume a very useful and practical addition to the literature on remote sensing." (E-STREAMS, August 2004)