Remote sensing of vegetation. Hyperspectral indices and image classifications for agriculture and vegetation pdf

Dr. Prasad S. Thenkabail, Research Geographer-15, U.S. Geological Survey (USGS), is a world-recognized expert in remote sensing science with multiple major contributions in the field sustained over more than 30 years. He obtained his PhD from the Ohio State University in 1992 and has over 140 peer-reviewed scientific publications, mostly in major international journals. Dr. Thenkabail has conducted pioneering research in the area of hyperspectral remote sensing of vegetation and in that of global croplands and their water use for food security. In hyperspectral remote sensing he has done cutting-edge research with wide implications in advancing remote sensing science in application to agriculture and vegetation. This body of work led to more than ten peer-reviewed research publications with high impact. For example, a single paper entitled “Hyperspectral vegetation indices and their relationships with agricultural crop characteristics” has received 1250+ citations as at the time of writing (November 19, 2020). Two other papers entitled “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications” and “Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests” have 515+ and 420+ citations each ((November 19, 2020). Numerous other papers, book chapters, and books (some related to this work, as we will learn below) have made highly significant contributions to field of hyperspectral remote sensing of agriculture, vegetation, water, food security as well as in global croplands for food and water security. In addition, new pathfinding work on global hyperspectral imaging spectral libraries on agricultural crops (GHISA) has been initiated:

Remote sensing of vegetation. Hyperspectral indices and image classifications for agriculture and vegetation pdf by Prasad Thenkabail

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 II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. This volume presents and discusses topics such as the non-invasive quantification of foliar pigments, leaf nitrogen concentration of cereal crop, the estimation of nitrogen content in crops and pastures, and forest leaf chlorophyll content, among others. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume II through the editors’ perspective.