What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Machine learning
Jochem Verrelst mainly investigates Remote sensing, Hyperspectral imaging, Imaging spectrometer, Vegetation and Parametric statistics.
His work carried out in the field of Remote sensing brings together such families of science as Temporal resolution, Support vector machine, Terrestrial animal, Atmospheric radiative transfer codes and Chlorophyll fluorescence.
Jochem Verrelst does research in Hyperspectral imaging, focusing on Red edge specifically.
His research investigates the link between Imaging spectrometer and topics such as Imaging spectroscopy that cross with problems in Remote sensing application and Earth system science.
His research on Vegetation often connects related topics like Leaf area index.
His research in Parametric statistics intersects with topics in Data stream and Data stream mining.
His most cited work include:
- Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (270 citations)
- Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 (257 citations)
- Earth system science related imaging spectroscopy — an assessment (246 citations)
What are the main themes of his work throughout his whole career to date?
Jochem Verrelst mainly focuses on Remote sensing, Hyperspectral imaging, Vegetation, Radiative transfer and Gaussian process.
Jochem Verrelst combines subjects such as Imaging spectrometer, Canopy, Leaf area index, Atmospheric radiative transfer codes and Chlorophyll fluorescence with his study of Remote sensing.
His biological study spans a wide range of topics, including Regression analysis and Regression.
As part of the same scientific family, Jochem Verrelst usually focuses on Regression, concentrating on Imaging spectroscopy and intersecting with Parametric statistics.
His Vegetation study frequently draws connections between related disciplines such as Spectral bands.
His work in Radiative transfer addresses issues such as Radiance, which are connected to fields such as FLEX and Spectrometer.
He most often published in these fields:
- Remote sensing (55.92%)
- Hyperspectral imaging (26.32%)
- Vegetation (18.42%)
What were the highlights of his more recent work (between 2019-2021)?
- Remote sensing (55.92%)
- Kriging (7.89%)
- Earth observation (10.53%)
In recent papers he was focusing on the following fields of study:
His main research concerns Remote sensing, Kriging, Earth observation, Gaussian process and Mean squared error.
Jochem Verrelst studies Remote sensing, focusing on Hyperspectral imaging in particular.
The Imaging spectroscopy research Jochem Verrelst does as part of his general Hyperspectral imaging study is frequently linked to other disciplines of science, such as Image, therefore creating a link between diverse domains of science.
His work deals with themes such as Algorithm, Physical geography, Data mining and Remote sensing application, which intersect with Earth observation.
His work in Mean squared error covers topics such as Scale which are related to areas like Radiance and Artificial neural network.
Jochem Verrelst has researched Image resolution in several fields, including Vegetation cover, Imaging spectrometer, Photosynthetically active radiation, Spectral bands and Normalized Difference Vegetation Index.
Between 2019 and 2021, his most popular works were:
- Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods (63 citations)
- Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions (26 citations)
- Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Machine learning
His primary areas of study are Remote sensing, Mean squared error, Regression, Leaf area index and Vegetation.
His Remote sensing research is multidisciplinary, incorporating elements of Inversion and Vegetation cover.
In his articles, Jochem Verrelst combines various disciplines, including Mean squared error and Gaussian process.
Jochem Verrelst focuses mostly in the field of Regression, narrowing it down to matters related to Imaging spectroscopy and, in some cases, Parametric statistics.
Jochem Verrelst has included themes like Feature, Crop, Regression analysis, Atmospheric radiative transfer codes and Transpiration in his Leaf area index study.
His Vegetation study combines topics in areas such as Image resolution, Abundance and Algorithm.
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