Fifty-years of advances in hyperspectral remote sensing of agriculture and vegetation-Summary, insights, and highlights of Volume IV
No abstract available.
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Geology topics
John G. Lyon
Publications and source records attributed to John G. Lyon.
Fifty-years of advances in hyperspectral remote sensing of agriculture and vegetation: Summary, insights, and highlights of volume III
The goal of this summary chapter is twofold. The first is to provide the reader an overview of the content of the preceding chapters. This they can read at the very beginning, before moving on to individual chapters in detail. Alternatively, they may read it at the very end to refresh their memory and to summarize the contents of the Volume. Second, this summary provides the editors’ perspective, bringing in their rich collective experience and expertise to guide the reader. We have kept the summaries brief and illustrative, so that the reader can quickly gather essential initial knowledge and guidance. The in-depth details can be found in the individual chapters.
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Fifty years of advances in hyperspectral remote sensing of agriculture and vegetation—Summary, insights, and highlights of volume II
No abstract available.
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Spaceborne hyperspectral EO-1 hyperion data pre-processing: Methods, approaches, and algorithms
No abstract available.
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Hyperspectral remote sensing of vegetation
Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.
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Hyperspectral remote sensing of vegetation and agricultural crops: Knowledge gain and knowledge gap after 40 years of research
The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.
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Advances in hyperspectral remote sensing of vegetation and agricultural croplands
Recent advances in hyperspectral remote sensing (or imaging spectroscopy) demonstrate a great utility for a variety of land monitoring applications. It is now possible to be diagnostic in sensing species and plant communities using remotely sensed data and to do so in a direct and informed manner using modern tools and analyses. Hyperspectral data analyses are superior to traditional broadband analyses in spectral information. Many investigations explore and document remote sensing of vegetation and agricultural croplands. Some examples include (a) detecting plant stress [1], (b) measuring chlorophyll content of plants [2], (c) identifying small differences in percent of green vegetation cover [3], (d) extracting biochemical variables such as nitrogen and lignin [2,4–6], (e) discriminating land-cover types [7], (f) detecting crop moisture variations [8], (g) sensing subtle variations in leaf pigment concentrations [2,9,10], (h) modeling biophysical and yield characteristics of agricultural crops [6,11,12], (i) improving the detection of changes in sparse vegetation [13], and (j) assessing absolute water content in plant leaves [14]. This is a fairly detailed list but not exhaustive, meant to provide the reader with a measure of the current, proven experimental capabilities, and operational applications, and stimulate investigations of new, ambitious applications.
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Remote sensing of global croplands for food security: Way forward
This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.
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