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.

section Section I|1 pages

Introduction to Hyperspectral Remote Sensing of Agricultural Crops and Vegetation

chapter 1|35 pages

Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

ByPrasad S. Thenkabail, John G. Lyon, Alfredo Huete

section Section II|1 pages

Hyperspectral Sensor Systems

chapter 2|29 pages

Hyperspectral Sensor Characteristics

Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LiDAR
ByFred Ortenberg

chapter 3|24 pages

Hyperspectral Remote Sensing in Global Change Studies

ByJiaguo Qi, Yoshio Inoue, Narumon Wiangwang

section Section III|1 pages

Hyperspectral Libraries of Agricultural Crops and Vegetation

chapter 5|21 pages

The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials

ByAndries 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, David R. Jordan

chapter 7|21 pages

The Use of Spectral Databases for Remote Sensing of Agricultural Crops

ByAndreas Hueni, Lola Suarez, Laurie A. Chisholm, Alex Held

chapter 8|61 pages

Characterization of Soil Properties Using Reflectance Spectroscopy

ByE. Ben-Dor, S. Chabrillat, José A. M. Demattê

section Section IV|1 pages

Hyperspectral Data Mining, Data Fusion, and Algorithms

chapter 9|22 pages

Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing

Methods, Approaches, and Algorithms
ByItiya P. Aneece, Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, Terrance Slonecker

chapter 10|30 pages

Hyperspectral Image Data Mining

BySreekala G. Bajwa, Yu Zhang, Alimohammad Shirzadifar

chapter 11|17 pages

Hyperspectral Data Processing Algorithms

ByAntonio Plaza, Javier Plaza, Gabriel Martín, Sergio Sánchez

chapter 12|34 pages

Methods for Linking Drone and Field Hyperspectral Data to Satellite Data

ByMuhammad Al-Amin Hoque, Stuart Phinn

chapter 13|39 pages

Integrating Hyperspectral and LiDAR Data in the Study of Vegetation

ByJessica J. Mitchell, Nancy F. Glenn, Kyla M. Dahlin, Nayani Ilangakoon, Hamid Dashti, Megan C. Maloney

chapter 14|42 pages

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
ByPrasad S. Thenkabail, John G. Lyon, Alfredo Huete