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Bio-optical modeling and remote sensing of inland waters /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Bio-optical Modeling and Remote Sensing of Inland Waters; Copyright Page; Contents; List of Contributors; Abbreviations and Notations; Abbreviations; Notations; 1 Remote Sensing of Inland Waters: Background and Current State-of-the-Art; 1.1 Inland Waters; 1.2 Remote Sensing of Inland Waters; 1.3 Fundamental Bio-Optical Properties; 1.4 Bio-Optical Models; 1.4.1 Classification of Bio-optical Models; 1.4.2 Performance of Bio-optical Models; 1.5 Book Content; References; 2 Radiative Transfer Theory for Inland Waters; 2.1 Introduction; 2.2 Basic Principles.
- 2.2.1 Interaction of Light with Matter2.2.2 Radiometric Quantities; 2.2.3 Radiative Transfer Equation; 2.2.4 Inherent Optical Properties; 2.2.5 From Microscopic to Macroscopic Material Parameters; 2.3 Bio-Optical Models; 2.3.1 Water Composition; 2.3.1.1 Phytoplankton; 2.3.1.2 CDOM; 2.3.1.3 Total Suspended Matter; 2.3.2 Apparent Optical Properties; 2.3.3 AOP Models; 2.4 Light Field Models; 2.4.1 Incident Radiation; 2.4.2 Water Surface Effects; 2.4.3 Underwater Light Field; 2.4.4 Fluorescence; 2.4.5 Polarization; 2.5 Conclusions; Acknowledgments; References.
- 3 Atmospheric Correction for Inland Waters3.1 Introduction; 3.2 Challenges; 3.2.1 Challenges Due to Physical and Bio-optical Properties; 3.2.1.1 High Turbidity and Floating Objects; 3.2.1.2 Adjacency Effect; 3.2.2 Challenges Due to Difficulties in Atmospheric Modeling; 3.2.2.1 Optical Heterogeneity Due to Terrestrial Influence; 3.2.2.2 Breakdown of Basic Assumptions; 3.3 Existing Algorithms; 3.3.1 Atmospheric Correction Algorithms; 3.3.1.1 Algorithms Deriving Aerosol Information from Clear Water Pixels in the Image, Assuming Spatial Homogeneity.
- 3.3.1.2 Algorithms Based on Extending the "Black-Pixel" Approach to the SWIR Region3.3.1.3 Algorithms Based on Spatial Extension of Aerosol Information Retrieved from Nearby Land; 3.3.1.4 Simultaneous Retrieval of Atmospheric and Water Components; 3.3.1.5 Image-Based Algorithms; 3.3.2 Adjacency Correction Algorithms; 3.3.3 Case Study: Combined Atmospheric and Adjacency Correction; 3.4 Conclusion; Acknowledgments; References; 4 Bio-optical Modeling of Colored Dissolved Organic Matter; 4.1 Carbon in Inland Waters; 4.2 Optical Properties of CDOM; 4.3 Remote Sensing of CDOM.
- 4.4 CDOM Retrieval With Bio-Optical Models4.5 Final Considerations; References; 5 Bio-optical Modeling of Total Suspended Solids; 5.1 Introduction; 5.2 Optical Properties of Particles; 5.2.1 Relationship between IOPs and TSS; 5.2.2 Remote Sensing Algorithms for TSS; 5.3 Case Studies; 5.3.1 MERIS Time-Series-Lake Garda; 5.3.2 Airborne Imaging Spectrometry-Mantua Lakes; 5.3.3 Multitemporal OLI Data-Po River; 5.4 Conclusions; Acknowledgments; References; Further Reading; 6 Bio-optical Modeling of Phytoplankton Chlorophyll-a; 6.1 Introduction.