This book discusses both the strengths and the limitations of the topics covered: (a) hyperspectral processes, (b) sensors, and (c) data analysis. Each chapter reviews existing "state-of-art" knowledge, highlights the advances made, and provides guidance for appropriate use of hyperspectral data in study of vegetation and its numerous applications.

Section I: Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation 1. Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops Section II: Hyperspectral Sensor Systems 2. Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LiDAR 3. Hyperspectral Remote Sensing in Global Change Studies Section III: Hyperspectral Libraries of Agricultural Crops and Vegetation 4. Monitoring Vegetation Diversity and Health through Spectral Traits and Trait Variations Based on Hyperspectral Remote Sensing 5. The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials 6. Linking Online Spectral Libraries with Hyperspectral Test Data through Library Building Tools and Code 7. The Use of Spectral Databases for Remote Sensing of Agricultural Crops 8. Characterization of Soil Properties Using Reflectance Spectroscopy Section IV: Hyperspectral Data Mining, Data Fusion, and Algorithms 9. Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing: Methods, Approaches, and Algorithms 10. Hyperspectral Image Data Mining 11. Hyperspectral Data Processing Algorithms 12. Methods for Linking Drone and Field Hyperspectral Data to Satellite Data 13. Integrating Hyperspectral and LiDAR Data in the Study of Vegetation 14. Fifty-Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation-Summary, Insights, and Highlights of Volume I: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation