D-Index & Metrics Best Publications

Agnès Bégué

University of Montpellier
France

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Environmental Sciences D-index 35 Citations 4,137 116 World Ranking 5906 National Ranking 263

Overview

What is she best known for?

The fields of study she is best known for:

Ecology
Agriculture
Statistics

Remote sensing, Normalized Difference Vegetation Index, Canopy, Leaf area index and Agriculture are her primary areas of study. Her multidisciplinary approach integrates Remote sensing and Temporal resolution in her work. Her Normalized Difference Vegetation Index research is within the category of Vegetation.

Agnès Bégué regularly links together related areas like Interception in her Canopy studies. Her Leaf area index study which covers Water balance that intersects with Ecosystem model, Thematic Mapper and Water content. While the research belongs to areas of Agriculture, Agnès Bégué spends her time largely on the problem of Crop, intersecting her research to questions surrounding Agroforestry, Soil quality, Ecoregion and Wet season.

Her most cited work include:

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island (152 citations)
Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test (125 citations)
Remote Sensing and Cropping Practices: A Review (108 citations)

What are the main themes of her work throughout her whole career to date?

Her primary scientific interests are in Remote sensing, Normalized Difference Vegetation Index, Vegetation, Agriculture and Canopy. The various areas that she examines in her Remote sensing study include Land cover, Land use, Leaf area index and Crop. Her study in Normalized Difference Vegetation Index is interdisciplinary in nature, drawing from both Climatology, Meteorology and Scale.

Her Vegetation study incorporates themes from Hydrology and Spatial variability. Her Agriculture research is multidisciplinary, incorporating perspectives in Agroforestry, Random forest and Environmental resource management. Her research integrates issues of Albedo and Interception in her study of Canopy.

She most often published in these fields:

Remote sensing (50.44%)
Normalized Difference Vegetation Index (25.66%)
Vegetation (23.89%)

What were the highlights of her more recent work (between 2016-2021)?

Remote sensing (50.44%)
Agriculture (19.47%)
Land cover (14.16%)

In recent papers she was focusing on the following fields of study:

Agnès Bégué spends much of her time researching Remote sensing, Agriculture, Land cover, Land use and Random forest. Her Remote sensing research includes elements of Data science, Multiple cropping and Vegetation, Normalized Difference Vegetation Index. The study incorporates disciplines such as Time series, Cropping, Tiger bush, Trend analysis and Scale in addition to Normalized Difference Vegetation Index.

The Land cover study combines topics in areas such as Cartography, Overgrazing, Field and Spearman's rank correlation coefficient. Her Random forest study combines topics from a wide range of disciplines, such as Crop, Digital elevation model and Spatial variability. Her Spatial variability research also works with subjects such as

Crop yield together with Water content, Canopy, Soil water and Linear regression,
Classifier together with Discriminant.

Between 2016 and 2021, her most popular works were:

Remote Sensing and Cropping Practices: A Review (108 citations)
A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) (89 citations)
Driving forces of recent vegetation changes in the Sahel: Lessons learned from regional and local level analyses (52 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island

Nicolas Baghdadi;Nathalie Boyer;Pierre Todoroff;Mahmoud El Hajj.
Remote Sensing of Environment (2009)

235 Citations

Remote Sensing and Cropping Practices: A Review

Agnès Bégué;Damien Arvor;Beatriz Bellón;Julie Betbeder.
Remote Sensing (2018)

210 Citations

Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test

Valentine Lebourgeois;Agnès Bégué;Sylvain Labbé;Benjamin Mallavan.
Sensors (2008)

200 Citations

Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices

Damien Arvor;Meirelles Margareth;Vincent Dubreuil;Agnes Begue.
Applied Geography (2012)

177 Citations

PAR extinction in shortgrass ecosystems: effects of clumping, sky conditions and soil albedo

Yann Nouvellon;Agnès Bégué;M. Susan Moran;Danny Lo Seen.
Agricultural and Forest Meteorology (2000)

159 Citations

Can a 25-year trend in Soudano-Sahelian vegetation dynamics be interpreted in terms of land use change? A remote sensing approach

Agnès Bégué;Elodie Vintrou;Denis Ruelland;Maxime Claden.
Global Environmental Change-human and Policy Dimensions (2011)

145 Citations

Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products

Elodie Vintrou;Annie Desbrosse;Agnès Bégué;Sibiry Traoré.
International Journal of Applied Earth Observation and Geoinformation (2012)

141 Citations

Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series

Mahmoud El Hajj;Agnès Bégué;Bruno Lafrance;Olivier Hagolle.
Sensors (2008)

141 Citations

Leaf area index, intercepted photosynthetically active radiation, and spectral vegetation indices: A sensitivity analysis for regular-clumped canopies

Agnès Bégué.
Remote Sensing of Environment (1993)

131 Citations

A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM)

Valentine Lebourgeois;Stéphane Dupuy;Élodie Vintrou;Maël Ameline.
Remote Sensing (2017)

122 Citations

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