Anatoly A. Gitelson

Bio: Anatoly A. Gitelson is an academic researcher from University of Nebraska–Lincoln. The author has contributed to research in topics: Chlorophyll & Red edge. The author has an hindex of 88, co-authored 218 publications receiving 29425 citations. Previous affiliations of Anatoly A. Gitelson include Technion – Israel Institute of Technology & Ben-Gurion University of the Negev.

Remote estimation of canopy chlorophyll content in crops

Abstract: [1] Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes.

Support vector machines

Abstract: My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations

Abstract: Aerosol radiative forcing is a critical, though variable and uncertain, component of the global climate. Yet climate models rely on sparse information of the aerosol optical properties. In situ measurements, though important in many respects, seldom provide measurements of the undisturbed aerosol in the entire atmospheric column. Here, 8 yr of worldwide distributed data from the AERONET network of ground-based radiometers were used to remotely sense the aerosol absorption and other optical properties in several key locations. Established procedures for maintaining and calibrating the global network of radiometers, cloud screening, and inversion techniques allow for a consistent retrieval of the optical properties of aerosol in locations with varying emission sources and conditions. The multiyear, multi-instrument observations show robust differentiation in both the magnitude and spectral dependence of the absorption—a property driving aerosol climate forcing, for desert dust, biomass burning, urban‐industrial, and marine aerosols. Moreover, significant variability of the absorption for the same aerosol type appearing due to different meteorological and source characteristics as well as different emission characteristics are observed. It is expected that this aerosol characterization will help refine aerosol optical models and reduce uncertainties in satellite observations of the global aerosol and in modeling aerosol impacts on climate.