Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation: Hyperspectral Remote Sensing of Vegetation

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.