What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Agriculture
Remote sensing, Vegetation, Hyperspectral imaging, Mineralogy and Imaging spectroscopy are his primary areas of study.
His study in Remote sensing focuses on Radiance in particular.
His study in Vegetation is interdisciplinary in nature, drawing from both Deposition, Soil retrogression and degradation, Hydrology, Erosion and Spatial variability.
His Hyperspectral imaging research is multidisciplinary, incorporating elements of Atmospheric radiative transfer codes, Radiative transfer, Absorption, Curvature and Dust storm.
His study looks at the intersection of Mineralogy and topics like Soil water with Spectrometer, Pulp and paper industry and Quantitative analysis.
Eyal Ben-Dor interconnects Acid mine drainage, Contaminated soils and Near-infrared spectroscopy in the investigation of issues within Imaging spectroscopy.
His most cited work include:
- A spectral based recognition of the urban environment using the visible and near-infrared spectral region (0.4-1.1 μm). A case study over Tel-Aviv, Israel (97 citations)
- Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy (91 citations)
- Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor (90 citations)
What are the main themes of his work throughout his whole career to date?
Eyal Ben-Dor mainly focuses on Remote sensing, Hyperspectral imaging, Soil water, Mineralogy and Soil test.
Eyal Ben-Dor has researched Remote sensing in several fields, including Pixel and Spectrometer.
His study looks at the relationship between Hyperspectral imaging and fields such as Longwave, as well as how they intersect with chemical problems.
Eyal Ben-Dor has included themes like Infiltration and Crust in his Soil water study.
Eyal Ben-Dor focuses mostly in the field of Mineralogy, narrowing it down to topics relating to Partial least squares regression and, in certain cases, Near-infrared spectroscopy and Reflectance spectroscopy.
His Soil test research incorporates elements of Albedo, Spectral signature and Reflectivity.
He most often published in these fields:
- Remote sensing (47.53%)
- Hyperspectral imaging (35.80%)
- Soil water (19.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Hyperspectral imaging (35.80%)
- Remote sensing (47.53%)
- Soil water (19.14%)
In recent papers he was focusing on the following fields of study:
Eyal Ben-Dor mainly investigates Hyperspectral imaging, Remote sensing, Soil water, Soil test and Longwave.
His Hyperspectral imaging study combines topics from a wide range of disciplines, such as Remote sensing, Mediterranean climate, Contamination and Petroleum.
Particularly relevant to EnMAP is his body of work in Remote sensing.
His Soil water study combines topics in areas such as Artifact and Multispectral image.
The study incorporates disciplines such as Soil classification, Spectral signature, Earth observation and Soil organic matter in addition to Soil test.
His work in Soil classification tackles topics such as Spectral shape analysis which are related to areas like Partial least squares regression.
Between 2018 and 2021, his most popular works were:
- Current Practices in UAS-based Environmental Monitoring (30 citations)
- Imaging Spectroscopy for Soil Mapping and Monitoring (30 citations)
- A memory-based learning approach utilizing combined spectral sources and geographical proximity for improved VIS-NIR-SWIR soil properties estimation (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Agriculture
His primary areas of investigation include Remote sensing, Hyperspectral imaging, Soil test, Topsoil and Water content.
His work carried out in the field of Remote sensing brings together such families of science as Silt and Computation.
His studies deal with areas such as Contamination and Petroleum as well as Hyperspectral imaging.
His Soil test research integrates issues from Soil texture, Range, Geographic coordinate system and Sample.
The concepts of his Topsoil study are interwoven with issues in Soil map, Soil carbon and Multispectral image.
His Water content research is multidisciplinary, relying on both Soil science, Soil classification, Partial least squares regression, Spectral shape analysis and Moisture.
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