TUM Library

Includes bibliographical references and index.

Section I: Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation -- 1. Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops -- [Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete] -- Section II: Hyperspectral Sensor Systems -- 2. Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LiDAR -- [Fred Ortenberg] -- 3. Hyperspectral Remote Sensing in Global Change Studies -- [Jiaguo Qi, Yoshio Inoue, and Narumon Wiangwang] -- 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 -- [Angela Lausch and Pedro J. Leito] -- 5. The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials -- [Andries B. Potgieter, James Watson, Barbara George-Jaeggli, Gregory McLean, Mark Eldridge, Scott C. Chapman, Kenneth Laws, Jack Christopher, Karine Chenu, Andrew Borrell, Graeme L. Hammer, and David R. Jordan] -- 6. Linking Online Spectral Libraries with Hyperspectral Test Data through Library Building Tools and Code -- [Muhammad Al-Amin Hoque and Stuart Phinn] -- 7. The Use of Spectral Databases for Remote Sensing of Agricultural Crops -- [Andreas Hueni, Lola Suarez, Laurie A. Chisholm, and Alex Held] -- 8. Characterization of Soil Properties Using Reflectance Spectroscopy -- [E. Ben-Dor, S. Chabrillat, and Jos A. M. Dematt] -- Section IV: Hyperspectral Data Mining, Data Fusion, and Algorithms -- 9. Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing: Methods, Approaches, and Algorithms -- [Itiya P. Aneece, Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, and Terrance Slonecker] -- 10. Hyperspectral Image Data Mining -- [Sreekala G. Bajwa, Yu Zhang, and Alimohammad Shirzadifar] -- 11. Hyperspectral Data Processing Algorithms -- [Antonio Plaza, Javier Plaza, Gabriel Martn, and Sergio Snchez] -- 12. Methods for Linking Drone and Field Hyperspectral Data to Satellite Data -- [Muhammad Al-Amin Hoque and Stuart Phinn] -- 13. Integrating Hyperspectral and LiDAR Data in the Study of Vegetation -- [Jessica J. Mitchell, Nancy F. Glenn, Kyla M. Dahlin, Nayani Ilangakoon, Hamid Dashti, and Megan C. Maloney] -- 14. Fifty-Years of Advances in Hyperspectral Remote Sensing of Agriculture and VegetationSummary, Insights, and Highlights of Volume I: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation -- [Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete].

Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.

Also available in print format.